From fd6f090909b06123f73c64bc3d45979ba0f72b2a Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Mon, 23 Feb 2026 16:23:34 +0100 Subject: [PATCH 01/25] chore: add ciao algorithm dependencies --- pyproject.toml | 33 +- uv.lock | 2373 ++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 2402 insertions(+), 4 deletions(-) create mode 100644 uv.lock diff --git a/pyproject.toml b/pyproject.toml index d50e062..4d270ed 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,12 +1,37 @@ [project] -name = "rationai-" +name = "rationai-ciao" version = "0.1.0" -description = "" +description = "CIAO: Contextual Importance Assessment via Obfuscation - An XAI method for identifying influential image regions" authors = [] -requires-python = ">=3.11" +requires-python = ">=3.12,<3.14" readme = "README.md" license = { file = "LICENSE" } -dependencies = [] +dependencies = [ + # Core ML/DL frameworks + "torch>=2.0.0", + "torchvision>=0.15.0", + + # Configuration and experiment tracking + 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It comes pre-configured with essential tools and features to enhance the development experience. +An implementation of explainable AI techniques for image classification. CIAO identifies influential image regions by systematically segmenting images, obfuscating segments, and using search algorithms to find important regions (hyperpixels). -## Tools Included +## Overview -- [uv](https://docs.astral.sh/uv/) for efficient dependency management. -- [Ruff](https://docs.astral.sh/ruff) for comprehensive linting and code formatting. -- [Pytest](https://docs.pytest.org) for running tests and ensuring code reliability. -- [GitLab CI/CD](https://docs.gitlab.com/ee/ci) for continuous integration. -- [Pydocstyle](https://www.pydocstyle.org) for validating docstring styles, also following the [Google style](https://google.github.io/styleguide/pyguide.html#s3.8-comments-and-docstrings). -- [Mypy](https://mypy-lang.org) for static type checking. +CIAO explains what regions of an image contribute to a neural network's classification decisions. The method: +1. Segments the image into small regions +2. Obfuscates each segment and measures impact on model predictions +3. Uses search algorithms to group adjacent important segments into hyperpixels +4. Generates explanations showing which regions influenced the prediction -## Usage +## Quick Start -Key commands for effective project management: +### Installation -- `uv sync` - Installs all project dependencies. -- `uv add ` - Adds a new dependency to the project. -- `uv run ruff check` - Runs linting. -- `uv run ruff format` - Runs formatting -- `uv run mypy .` - Runs mypy. -- `uv run pytest tests` - Executes tests located in the tests directory. -- `uv run ` - Runs the specified command within the virtual environment. +```bash +# Clone the repository +git clone +cd ciao-simple -## CI/CD +# Install dependencies using uv +uv sync +``` -The project uses our [GitLab CI/CD templates](https://gitlab.ics.muni.cz/rationai/digital-pathology/templates/ci-templates) to automate the linting and testing processes. The pipeline is triggered on every merge request and push to the default branch. +### Basic Usage + +Explain a single image with default settings: + +```bash +python ciao +``` + +Customize the explanation using Hydra configuration overrides: + +```bash +python ciao data.image_path=./my_image.jpg explanation.method=mcts explanation.segment_size=8 +``` + +### Development Commands + +- `uv sync` - Install all dependencies +- `uv add ` - Add a new dependency +- `uv run ruff check` - Run linting +- `uv run ruff format` - Format code +- `uv run mypy .` - Run type checking +- `uv run python ciao` - Run CIAO with default configuration + +## Method Details + +### How CIAO Works + +1. **Segmentation**: The input image is divided into small regions (segments) using hexagonal or square grids +2. **Score Calculation**: Each segment is obfuscated (replaced) and the model is queried to measure how much that segment affects the prediction. This gives an importance score to each segment +3. **Hyperpixel Search**: A search algorithm finds groups of adjacent segments with high importance scores, creating "hyperpixels" that represent influential image regions +4. **Explanation**: The top hyperpixels are visualized to show which regions most influenced the model's prediction + +### Search Algorithms + +- **MCTS (Monte Carlo Tree Search)**: Tree-based search with UCB exploration +- **MC-RAVE**: MCTS with Rapid Action Value Estimation +- **MCGS (Monte Carlo Graph Search)**: Graph-based variant allowing revisiting of states +- **MCGS-RAVE**: MCGS with RAVE enhancements +- **Lookahead**: Greedy search with lookahead using efficient bitset operations +- **Potential**: Potential field-guided sequential search + +### Segmentation Methods + +- **Hexagonal Grid**: Divides image into hexagonal cells for better spatial coverage +- **Square Grid**: Simple square grid segmentation + +### Replacement Methods + +- **Mean Color**: Replace masked regions with the image's mean color (normalized) +- **Blur**: Gaussian blur applied to masked regions +- **Interlacing**: Interlaced pattern replacement +- **Solid Color**: Replace with a specified solid color (RGB) + +## Project Structure + +``` +ciao-simple/ +├── ciao/ # Main package +│ ├── algorithm/ # Search algorithms +│ │ ├── mcts.py # Monte Carlo Tree Search +│ │ ├── mcgs.py # Monte Carlo Graph Search +│ │ ├── lookahead_bitset.py # Greedy lookahead with bitsets +│ │ └── potential.py # Potential-based search +│ ├── data/ # Data loading and preprocessing +│ │ ├── loader.py # Image loaders +│ │ └── preprocessing.py # Image preprocessing utilities +│ ├── explainer/ # Core explainer implementation +│ │ └── ciao_explainer.py # Main CIAO explainer class +│ ├── structures/ # Data structures +│ │ ├── bitmask_graph.py # Bitset operations for hyperpixels +│ │ └── nodes.py # Node classes for tree/graph search +│ ├── utils/ # Utility functions +│ │ ├── calculations.py # Score calculations and predictions +│ │ ├── segmentation.py # Segmentation utilities +│ │ └── search_utils.py # Search algorithm utilities +│ ├── visualization/ # Visualization tools +│ │ └── visualisation.py # Interactive visualizations +│ └── __main__.py # CLI entry point +├── configs/ # Hydra configuration files +│ ├── ciao.yaml # Main entry point +│ ├── base.yaml # Base configuration +│ ├── data/ # Data configurations +│ │ └── default.yaml +│ ├── explanation/ # Explanation method configs +│ │ └── ciao_default.yaml # Default CIAO parameters +│ ├── hydra/ # Hydra settings +│ └── logger/ # Logger configurations +└── pyproject.toml # Project metadata and dependencies +``` From 9ac0438890aab52dc2cdb2d67cbef571dfff9491 Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Mon, 23 Feb 2026 19:17:42 +0100 Subject: [PATCH 03/25] refactor: apply agents' suggestions --- README.md | 5 +- pyproject.toml | 2 +- uv.lock | 773 ++++++++++++++++++++++++++++++++++++++++++++++++- 3 files changed, 774 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 042d292..85e23b7 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ CIAO explains what regions of an image contribute to a neural network's classifi ```bash # Clone the repository -git clone +git clone https://github.com/RationAI/ciao.git cd ciao-simple # Install dependencies using uv @@ -46,6 +46,7 @@ python ciao data.image_path=./my_image.jpg explanation.method=mcts explanation.s - `uv run ruff format` - Format code - `uv run mypy .` - Run type checking - `uv run python ciao` - Run CIAO with default configuration +- `uv run pytest tests` - Execute tests ## Method Details @@ -100,7 +101,7 @@ ciao-simple/ │ │ ├── segmentation.py # Segmentation utilities │ │ └── search_utils.py # Search algorithm utilities │ ├── visualization/ # Visualization tools -│ │ └── visualisation.py # Interactive visualizations +│ │ └── visualization.py # Interactive visualizations │ └── __main__.py # CLI entry point ├── configs/ # 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CIAO identifies influential image regions by systematically segmenting images, obfuscating segments, and using search algorithms to find important regions (hyperpixels). @@ -18,7 +18,7 @@ CIAO explains what regions of an image contribute to a neural network's classifi ```bash # Clone the repository git clone https://github.com/RationAI/ciao.git -cd ciao-simple +cd ciao # Install dependencies using uv uv sync @@ -29,13 +29,13 @@ uv sync Explain a single image with default settings: ```bash -python ciao +uv run python ciao ``` Customize the explanation using Hydra configuration overrides: ```bash -python ciao data.image_path=./my_image.jpg explanation.method=mcts explanation.segment_size=8 +uv run python ciao data.image_path=./my_image.jpg explanation.method=mcts explanation.segment_size=8 ``` ### Development Commands @@ -81,7 +81,7 @@ python ciao data.image_path=./my_image.jpg explanation.method=mcts explanation.s ## Project Structure ``` -ciao-simple/ +ciao/ ├── ciao/ # Main package │ 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Content-Transfer-Encoding: 8bit Co-authored-by: Adam Kukučka --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index f33f47b..4362c11 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -2,7 +2,7 @@ name = "rationai-ciao" version = "0.1.0" description = "CIAO: Contextual Importance Assessment via Obfuscation - An XAI method for identifying influential image regions" -authors = [] +authors = [{name = "David Halmazňa", email = "david.halmazna@mail.muni.cz"}] requires-python = ">=3.11" readme = "README.md" license = { file = "LICENSE" } From 7a3c79d4f253a321a0b4a253650a35640a4ee99b Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Mon, 23 Feb 2026 16:57:35 +0100 Subject: [PATCH 06/25] feat: add core data structures and utilities Add bitmask graph operations, MCTS/MCGS node structures, segmentation utilities, model predictor, and supporting ImageNet class names. --- ciao/__init__.py | 7 + ciao/imagenet_classes.txt | 1000 ++++++++++++++++++++++++++++++ ciao/structures/__init__.py | 5 + ciao/structures/bitmask_graph.py | 207 +++++++ ciao/structures/nodes.py | 64 ++ ciao/utils/__init__.py | 10 + ciao/utils/calculations.py | 388 ++++++++++++ ciao/utils/search_utils.py | 39 ++ ciao/utils/segmentation.py | 289 +++++++++ 9 files changed, 2009 insertions(+) create mode 100644 ciao/__init__.py create mode 100644 ciao/imagenet_classes.txt create mode 100644 ciao/structures/__init__.py create mode 100644 ciao/structures/bitmask_graph.py create mode 100644 ciao/structures/nodes.py create mode 100644 ciao/utils/__init__.py create mode 100644 ciao/utils/calculations.py create mode 100644 ciao/utils/search_utils.py create mode 100644 ciao/utils/segmentation.py diff --git a/ciao/__init__.py b/ciao/__init__.py new file mode 100644 index 0000000..b1a78ee --- /dev/null +++ b/ciao/__init__.py @@ -0,0 +1,7 @@ +"""CIAO: Contextual Importance Assessment via Obfuscation + +An explainable AI (XAI) method for identifying influential image regions using +Mutual Information and greedy feature selection. +""" + +from ciao.explainer.ciao_explainer import CIAOExplainer \ No newline at end of file diff --git a/ciao/imagenet_classes.txt b/ciao/imagenet_classes.txt new file mode 100644 index 0000000..f40829e --- /dev/null +++ b/ciao/imagenet_classes.txt @@ -0,0 +1,1000 @@ +tench +goldfish +great white shark +tiger shark +hammerhead +electric ray +stingray +cock +hen +ostrich +brambling +goldfinch +house finch +junco +indigo bunting +robin +bulbul +jay +magpie +chickadee +water ouzel +kite +bald eagle +vulture +great grey owl +European fire salamander +common newt +eft +spotted salamander +axolotl +bullfrog +tree frog +tailed frog +loggerhead +leatherback turtle +mud turtle +terrapin +box turtle +banded gecko +common iguana +American chameleon +whiptail +agama +frilled lizard +alligator lizard +Gila monster +green lizard +African chameleon +Komodo dragon +African crocodile +American alligator +triceratops 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poodle +Mexican hairless +timber wolf +white wolf +red wolf +coyote +dingo +dhole +African hunting dog +hyena +red fox +kit fox +Arctic fox +grey fox +tabby +tiger cat +Persian cat +Siamese cat +Egyptian cat +cougar +lynx +leopard +snow leopard +jaguar +lion +tiger +cheetah +brown bear +American black bear +ice bear +sloth bear +mongoose +meerkat +tiger beetle +ladybug +ground beetle +long-horned beetle +leaf beetle +dung beetle +rhinoceros beetle +weevil +fly +bee +ant +grasshopper +cricket +walking stick +cockroach +mantis +cicada +leafhopper +lacewing +dragonfly +damselfly +admiral +ringlet +monarch +cabbage butterfly +sulphur butterfly +lycaenid +starfish +sea urchin +sea cucumber +wood rabbit +hare +Angora +hamster +porcupine +fox squirrel +marmot +beaver +guinea pig +sorrel +zebra +hog +wild boar +warthog +hippopotamus +ox +water buffalo +bison +ram +bighorn +ibex +hartebeest +impala +gazelle +Arabian camel +llama +weasel +mink +polecat +black-footed ferret +otter +skunk +badger +armadillo +three-toed sloth +orangutan +gorilla +chimpanzee +gibbon +siamang +guenon +patas +baboon +macaque +langur +colobus +proboscis monkey +marmoset +capuchin +howler monkey +titi +spider monkey +squirrel monkey +Madagascar cat +indri +Indian elephant +African elephant +lesser panda +giant panda +barracouta +eel +coho +rock beauty +anemone fish +sturgeon +gar +lionfish +puffer +abacus +abaya +academic gown +accordion +acoustic guitar +aircraft carrier +airliner +airship +altar +ambulance +amphibian +analog clock +apiary +apron +ashcan +assault rifle +backpack +bakery +balance beam +balloon +ballpoint +Band Aid +banjo +bannister +barbell +barber chair +barbershop +barn +barometer +barrel +barrow +baseball +basketball +bassinet +bassoon +bathing cap +bath towel +bathtub +beach wagon +beacon +beaker +bearskin +beer bottle +beer glass +bell cote +bib +bicycle-built-for-two +bikini +binder +binoculars +birdhouse +boathouse +bobsled +bolo tie +bonnet +bookcase +bookshop +bottlecap +bow +bow tie +brass +brassiere +breakwater +breastplate +broom +bucket +buckle +bulletproof vest +bullet train +butcher shop +cab +caldron +candle +cannon +canoe +can opener +cardigan +car mirror +carousel +carpenter's kit +carton +car wheel +cash machine +cassette +cassette player +castle +catamaran +CD player +cello +cellular telephone +chain +chainlink fence +chain mail +chain saw +chest +chiffonier +chime +china cabinet +Christmas stocking +church +cinema +cleaver +cliff dwelling +cloak +clog +cocktail shaker +coffee mug +coffeepot +coil +combination lock +computer keyboard +confectionery +container ship +convertible +corkscrew +cornet +cowboy boot +cowboy hat +cradle +crane +crash helmet +crate +crib +Crock Pot +croquet ball +crutch +cuirass +dam +desk +desktop computer +dial telephone +diaper +digital clock +digital watch +dining table +dishrag +dishwasher +disk brake +dock +dogsled +dome +doormat +drilling platform +drum +drumstick +dumbbell +Dutch oven +electric fan +electric guitar +electric locomotive +entertainment center +envelope +espresso maker +face powder +feather boa +file +fireboat +fire engine +fire screen +flagpole +flute +folding chair +football helmet +forklift +fountain +fountain pen +four-poster +freight car +French horn +frying pan +fur coat +garbage truck +gasmask +gas pump +goblet +go-kart +golf ball +golfcart +gondola +gong +gown +grand piano +greenhouse +grille +grocery store +guillotine +hair slide +hair spray +half track +hammer +hamper +hand blower +hand-held computer +handkerchief +hard disc +harmonica +harp +harvester +hatchet +holster +home theater +honeycomb +hook +hoopskirt +horizontal bar +horse cart +hourglass +iPod +iron +jack-o'-lantern +jean +jeep +jersey +jigsaw puzzle +jinrikisha +joystick +kimono +knee pad +knot +lab coat +ladle +lampshade +laptop +lawn mower +lens cap +letter opener +library +lifeboat +lighter +limousine +liner +lipstick +Loafer +lotion +loudspeaker +loupe +lumbermill +magnetic compass +mailbag +mailbox +maillot +maillot +manhole cover +maraca +marimba +mask +matchstick +maypole +maze +measuring cup +medicine chest +megalith +microphone +microwave +military uniform +milk can +minibus +miniskirt +minivan +missile +mitten +mixing bowl +mobile home +Model T +modem +monastery +monitor +moped +mortar +mortarboard +mosque +mosquito net +motor scooter +mountain bike +mountain tent +mouse +mousetrap +moving van +muzzle +nail +neck brace +necklace +nipple +notebook +obelisk +oboe +ocarina +odometer +oil filter +organ +oscilloscope +overskirt +oxcart +oxygen mask +packet +paddle +paddlewheel +padlock +paintbrush +pajama +palace +panpipe +paper towel +parachute +parallel bars +park bench +parking meter +passenger car +patio +pay-phone +pedestal +pencil box +pencil sharpener +perfume +Petri dish +photocopier +pick +pickelhaube +picket fence +pickup +pier +piggy bank +pill bottle +pillow +ping-pong ball +pinwheel +pirate +pitcher +plane +planetarium +plastic bag +plate rack +plow +plunger +Polaroid camera +pole +police van +poncho +pool table +pop bottle +pot +potter's wheel +power drill +prayer rug +printer +prison +projectile +projector +puck +punching bag +purse +quill +quilt +racer +racket +radiator +radio +radio telescope +rain barrel +recreational vehicle +reel +reflex camera +refrigerator +remote control +restaurant +revolver +rifle +rocking chair +rotisserie +rubber eraser +rugby ball +rule +running shoe +safe +safety pin +saltshaker +sandal +sarong +sax +scabbard +scale +school bus +schooner +scoreboard +screen +screw +screwdriver +seat belt +sewing machine +shield +shoe shop +shoji +shopping basket +shopping cart +shovel +shower cap +shower curtain +ski +ski mask +sleeping bag +slide rule +sliding door +slot +snorkel +snowmobile +snowplow +soap dispenser +soccer ball +sock +solar dish +sombrero +soup bowl +space bar +space heater +space shuttle +spatula +speedboat +spider web +spindle +sports car +spotlight +stage +steam locomotive +steel arch bridge +steel drum +stethoscope +stole +stone wall +stopwatch +stove +strainer +streetcar +stretcher +studio couch +stupa +submarine +suit +sundial +sunglass +sunglasses +sunscreen +suspension bridge +swab +sweatshirt +swimming trunks +swing +switch +syringe +table lamp +tank +tape player +teapot +teddy +television +tennis ball +thatch +theater curtain +thimble +thresher +throne +tile roof +toaster +tobacco shop +toilet seat +torch +totem pole +tow truck +toyshop +tractor +trailer truck +tray +trench coat +tricycle +trimaran +tripod +triumphal arch +trolleybus +trombone +tub +turnstile +typewriter keyboard +umbrella +unicycle +upright +vacuum +vase +vault +velvet +vending machine +vestment +viaduct +violin +volleyball +waffle iron +wall clock +wallet +wardrobe +warplane +washbasin +washer +water bottle +water jug +water tower +whiskey jug +whistle +wig +window screen +window shade +Windsor tie +wine bottle +wing +wok +wooden spoon +wool +worm fence +wreck +yawl +yurt +web site +comic book +crossword puzzle +street sign +traffic light +book jacket +menu +plate +guacamole +consomme +hot pot +trifle +ice cream +ice lolly +French loaf +bagel +pretzel +cheeseburger +hotdog +mashed potato +head cabbage +broccoli +cauliflower +zucchini +spaghetti squash +acorn squash +butternut squash +cucumber +artichoke +bell pepper +cardoon +mushroom +Granny Smith +strawberry +orange +lemon +fig +pineapple +banana +jackfruit +custard apple +pomegranate +hay +carbonara +chocolate sauce +dough +meat loaf +pizza +potpie +burrito +red wine +espresso +cup +eggnog +alp +bubble +cliff +coral reef +geyser +lakeside +promontory +sandbar +seashore +valley +volcano +ballplayer +groom +scuba diver +rapeseed +daisy +yellow lady's slipper +corn +acorn +hip +buckeye +coral fungus +agaric +gyromitra +stinkhorn +earthstar +hen-of-the-woods +bolete +ear +toilet tissue diff --git a/ciao/structures/__init__.py b/ciao/structures/__init__.py new file mode 100644 index 0000000..99a1662 --- /dev/null +++ b/ciao/structures/__init__.py @@ -0,0 +1,5 @@ +"""Data structures for CIAO.""" + +from ciao.structures.nodes import MCGSNode, MCTSNode + +__all__ = ["MCTSNode", "MCGSNode"] \ No newline at end of file diff --git a/ciao/structures/bitmask_graph.py b/ciao/structures/bitmask_graph.py new file mode 100644 index 0000000..e241e89 --- /dev/null +++ b/ciao/structures/bitmask_graph.py @@ -0,0 +1,207 @@ +"""Bitmask-based graph utilities for efficient segment manipulation. + +This module provides low-level primitives for working with graph structures +represented as integer bitmasks, where each bit represents a node/segment. +""" + +import random + +import numpy as np + + +def mask_to_ids(mask: int) -> list[int]: + """Convert integer bitmask to list of segment indices.""" + return [i for i in range(mask.bit_length()) if (mask >> i) & 1] + + +def iter_bits(mask: int): + """Iterate over set bits in a mask using low-bit isolation. + + Yields node IDs in arbitrary order (depends on bit positions). + Performance: O(k) where k is the number of set bits. + + Example: + mask = 0b10110 # bits 1, 2, 4 are set + list(iter_bits(mask)) # [1, 2, 4] + """ + temp = mask + while temp: + low_bit = temp & -temp + node_id = low_bit.bit_length() - 1 + yield node_id + temp ^= low_bit + + +def has_node(mask: int, node: int) -> bool: + """Test if a node is present in the mask.""" + return (mask >> node) & 1 == 1 + + +def add_node(mask: int, node: int) -> int: + """Add a node to the mask.""" + return mask | (1 << node) + + +def remove_node(mask: int, node: int) -> int: + """Remove a node from the mask.""" + return mask & ~(1 << node) + + +def pick_random_set_bit(mask: int) -> int: + """Select a random set bit from the mask in O(N) where N is the index of the bit, + without allocating a list. Efficient for sparse masks. + """ + count = mask.bit_count() + if count == 0: + return -1 + + which = random.randrange(count) + + temp = mask + for _ in range(which): + temp &= temp - 1 # Clear lowest set bit + + return (temp & -temp).bit_length() - 1 + + +def get_frontier(mask: int, adj_masks: tuple[int, ...], used_mask: int) -> int: + """Compute the expansion frontier (valid neighbors) for graph traversal. + + The frontier is the set of segments adjacent to the current structure + that can be added in the next step. + + A segment is in the frontier if: + - It is adjacent to at least one segment in the current mask + - It is NOT already in the current mask + - It is NOT in the used_mask (respects global exclusion constraints) + + Args: + mask: Bitmask of current structure/connected component + adj_masks: Tuple of adjacency bitmasks (adj_masks[i] = neighbors of segment i) + used_mask: Bitmask of globally excluded segments + + Returns: + Bitmask of valid frontier segments + """ + frontier = 0 + + for node_id in iter_bits(mask): + frontier |= adj_masks[node_id] + + frontier &= ~mask + frontier &= ~used_mask + + return frontier + + +def sample_connected_superset( + base_mask: int, + target_length: int, + adj_masks: tuple[int, ...], + base_frontier: int, + used_mask: int, + segment_weights: np.ndarray | None = None, + optimization_sign: int = 1, + temperature: float = 3.0, +) -> int: + """Sample a connected superset via random walk expansion. + + IMPORTANT: This is NOT a uniform sampler over all connected supersets. + The distribution is biased towards segments discovered early and + depends on graph topology. This bias is acceptable for Monte Carlo + estimation in the parent algorithm. + + With segment_weights provided, uses guided sampling based on max rewards + observed during search, using softmax with temperature and epsilon-mixing + for exploration. + + Args: + base_mask: Starting set (must be non-empty and connected) + target_length: Desired size of the superset + adj_masks: Adjacency bitmasks for neighbor lookups + base_frontier: Initial expansion frontier (unused, kept for compatibility) + used_mask: Global exclusion mask (segments that must not be added) + segment_weights: Optional array of max rewards per segment for guided sampling + optimization_sign: +1 to maximize, -1 to minimize (affects weighting) + temperature: Softmax temperature (higher = more uniform, default 3.0) + + Returns: + Bitmask of connected superset containing base_mask + """ + mask = base_mask + + while mask.bit_count() < target_length: + frontier = get_frontier(mask, adj_masks, used_mask) + if frontier == 0: + break + + # Select next segment (weighted or uniform) + if segment_weights is not None: + seg_id = _pick_weighted_frontier_segment( + frontier, segment_weights, optimization_sign, temperature + ) + else: + seg_id = pick_random_set_bit(frontier) + + mask = add_node(mask, seg_id) + + return mask + + +def _pick_weighted_frontier_segment( + frontier: int, + segment_weights: np.ndarray, + optimization_sign: int, + temperature: float, +) -> int: + """Pick a segment from frontier using softmax weighting over max rewards. + + Logic: + 1. Extract weights for frontier segments + 2. Replace -inf (unvisited) with min observed reward + 3. Apply optimization sign and softmax with temperature + 4. Mix with uniform distribution (epsilon=0.05) for exploration + 5. Sample using the final probabilities + + Args: + frontier: Bitmask of candidate segments + segment_weights: Array of max rewards per segment (may contain -inf) + optimization_sign: +1 to maximize, -1 to minimize + temperature: Softmax temperature for probability distribution + + Returns: + Selected segment ID + """ + # Extract frontier segment IDs and their weights + frontier_ids = list(iter_bits(frontier)) + frontier_weights = segment_weights[frontier_ids] + + # Handle unvisited segments: replace -inf with min observed reward + visited_mask = np.isfinite(frontier_weights) + if np.any(visited_mask): + min_observed = np.min(frontier_weights[visited_mask]) + frontier_weights = np.where(visited_mask, frontier_weights, min_observed) + else: + # No segments visited yet - treat all as equal (zero) + frontier_weights = np.zeros_like(frontier_weights) + + # Apply optimization sign to align with "bigger is better" + effective_scores = frontier_weights * optimization_sign + + # Compute softmax probabilities with temperature + # Subtract max for numerical stability + max_score = np.max(effective_scores) + exp_scores = np.exp((effective_scores - max_score) / temperature) + softmax_probs = exp_scores / np.sum(exp_scores) + + # Epsilon-greedy mixing: 95% softmax, 5% uniform + epsilon = 0.05 + uniform_probs = np.ones(len(frontier_ids)) / len(frontier_ids) + final_probs = (1 - epsilon) * softmax_probs + epsilon * uniform_probs + + # Renormalize to ensure probabilities sum to exactly 1.0 (fix floating point errors) + final_probs = final_probs / np.sum(final_probs) + + # Sample segment using final probabilities + idx = np.random.choice(len(frontier_ids), p=final_probs) + return frontier_ids[idx] diff --git a/ciao/structures/nodes.py b/ciao/structures/nodes.py new file mode 100644 index 0000000..bc9e998 --- /dev/null +++ b/ciao/structures/nodes.py @@ -0,0 +1,64 @@ +from typing import Optional + + +class MCTSNode: + def __init__( + self, mask: int, parent: Optional["MCTSNode"] = None, prior_score: float = 0.0 + ): + self.mask = mask + self.parent = parent + self.children: dict[int, MCTSNode] = {} + self.visits = 0 + self.value_sum = 0.0 + self.max_value = -float("inf") + + self.rave_visits = 0 + self.rave_value_sum = 0.0 + self.rave_max_value = -float("inf") + + self.pending = 0 # virtual loss counter + + # RAVE-specific: Global RAVE prior for smart FPU initialization + self.prior_score = prior_score + self.frontier_cache: int | None = None + + def mean_value(self): + return self.value_sum / self.visits if self.visits > 0 else 0.0 + + def rave_mean(self): + return self.rave_value_sum / self.rave_visits if self.rave_visits > 0 else 0.0 + + +class MCGSNode: + def __init__(self, mask: int): + self.mask = mask + self.children: dict[int, MCGSNode] = {} # segment_id -> child node + + self.edge_stats: dict[ + int, dict[str, float] + ] = {} # segment_id -> {'N': 0, 'W': 0.0, 'Q': 0.0, 'max_reward': -inf} + self.rave_stats: dict[int, dict[str, float]] = {} + self.pending_edges: dict[int, int] = {} # segment_id -> pending count + + self.visits = 0 + self.value_sum = 0.0 + self.max_value = -float("inf") + self.pending = 0 # virtual loss counter (for non-RAVE modes) + + def init_edge(self, action: int): + if action not in self.edge_stats: + self.edge_stats[action] = { + "N": 0, + "W": 0.0, + "Q": 0.0, + "max_reward": -float("inf"), + } + if action not in self.rave_stats: + self.rave_stats[action] = { + "N": 0, + "W": 0.0, + "Q": 0.0, + "max_reward": -float("inf"), + } + if action not in self.pending_edges: + self.pending_edges[action] = 0 diff --git a/ciao/utils/__init__.py b/ciao/utils/__init__.py new file mode 100644 index 0000000..5065a75 --- /dev/null +++ b/ciao/utils/__init__.py @@ -0,0 +1,10 @@ +"""Utility functions for CIAO.""" + +# Export commonly used utilities +from ciao.utils.calculations import ModelPredictor +from ciao.utils.segmentation import create_segmentation + +__all__ = [ + "ModelPredictor", + "create_segmentation", +] \ No newline at end of file diff --git a/ciao/utils/calculations.py b/ciao/utils/calculations.py new file mode 100644 index 0000000..40bae0c --- /dev/null +++ b/ciao/utils/calculations.py @@ -0,0 +1,388 @@ +import matplotlib.pyplot as plt +import numpy as np +import torch +import torch.nn.functional as F + + +class ModelPredictor: + """Handles model predictions and class information""" + + def __init__(self, model, class_names: list[str]): + self.model = model + self.class_names = class_names + self.device = next(model.parameters()).device + self.replacement_image = None + + # Pre-compute normalization constants + self.imagenet_mean = ( + torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(self.device) + ) + self.imagenet_std = ( + torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(self.device) + ) + + def get_predictions(self, input_batch: torch.Tensor) -> torch.Tensor: + """Get model predictions (returns probabilities)""" + with torch.no_grad(): + outputs = self.model(input_batch) + probabilities = torch.nn.functional.softmax(outputs, dim=1) + return probabilities + + def predict_image( + self, input_batch: torch.Tensor, top_k: int = 5 + ) -> list[tuple[int, str, float]]: + """Get top-k predictions for an image""" + probabilities = self.get_predictions(input_batch) + top_probs, top_indices = torch.topk(probabilities[0], top_k) + + results = [] + for i in range(top_k): + class_idx = top_indices[i].item() + prob = top_probs[i].item() + class_name = ( + self.class_names[class_idx] + if class_idx < len(self.class_names) + else f"class_{class_idx}" + ) + results.append((class_idx, class_name, prob)) + return results + + def calculate_image_mean_color(self, input_tensor: torch.Tensor) -> torch.Tensor: + """Calculate image mean color using pre-computed constants""" + # Add batch dimension if needed + if input_tensor.dim() == 3: + input_tensor = input_tensor.unsqueeze(0) + + unnormalized = (input_tensor * self.imagenet_std) + self.imagenet_mean + mean_color = unnormalized.mean(dim=(2, 3), keepdim=True) + normalized_mean = (mean_color - self.imagenet_mean) / self.imagenet_std + return normalized_mean.squeeze(0) # Remove batch dimension + + def get_replacement_image( + self, input_tensor: torch.Tensor, replacement: str = "mean_color", **kwargs + ) -> torch.Tensor: + """Generate replacement image for masking operations. + + Args: + input_tensor: Input tensor [3, 224, 224] (ImageNet normalized) + replacement: Strategy - "mean_color", "interlacing", "blur", or "solid_color" + **kwargs: Additional options: + - color: For solid_color mode, RGB tuple (0-255). Defaults to black (0, 0, 0) + + Returns: + replacement_image: torch tensor [3, 224, 224] on same device + """ + # Ensure tensor is on correct device + input_tensor = input_tensor.to(self.device) + + if replacement == "mean_color": + # Fill entire image with mean color + mean_color = self.calculate_image_mean_color(input_tensor) # [3, 1, 1] + replacement_image = mean_color.expand(-1, 224, 224) # [3, 224, 224] + + elif replacement == "interlacing": + # Create interlaced pattern: even columns flipped vertically, then even indices flipped horizontally + replacement_image = input_tensor.clone() + even_indices = torch.arange(0, 224, 2) # [0, 2, 4, ..., 222] + + # Step 1: Flip even columns vertically (upside down) + replacement_image[:, :, even_indices] = torch.flip( + replacement_image[:, :, even_indices], dims=[1] + ) + + # Step 2: Flip even indices horizontally (left-right) + replacement_image[:, even_indices, :] = torch.flip( + replacement_image[:, even_indices, :], dims=[2] + ) + + elif replacement == "blur": + # Apply Gaussian blur using conv2d + # Create 7x7 Gaussian kernel (sigma≈1.5 for noticeable but not extreme blur) + kernel_size = 7 + sigma = 1.5 + + # Generate 1D Gaussian kernel + x = torch.arange(kernel_size, dtype=torch.float32, device=self.device) + x = x - kernel_size // 2 + gaussian_1d = torch.exp(-(x**2) / (2 * sigma**2)) + gaussian_1d = gaussian_1d / gaussian_1d.sum() + + # Create 2D kernel by outer product + gaussian_2d = gaussian_1d[:, None] * gaussian_1d[None, :] + gaussian_2d = gaussian_2d / gaussian_2d.sum() + + # Create convolution kernel for each channel + kernel = gaussian_2d.expand(3, 1, kernel_size, kernel_size) + + # Apply blur with padding to maintain image size + input_batch = input_tensor.unsqueeze(0) # [1, 3, 224, 224] + padding = kernel_size // 2 + + replacement_image = F.conv2d( + input_batch, + kernel, + padding=padding, + groups=3, # Apply same kernel to each channel independently + ).squeeze(0) # [3, 224, 224] + + elif replacement == "solid_color": + # Fill with specified solid color (expects RGB values in 0-255 range) + color = kwargs.get("color", (0, 0, 0)) # Default to black + + # Convert color to torch tensor (always assume 0-255 range) + if isinstance(color, (list, tuple)): + color = torch.tensor(color, dtype=torch.float32, device=self.device) + + # Convert from 0-255 range to 0-1 range + color = color / 255.0 + + # Apply ImageNet normalization - squeeze to remove batch dimension from constants + color = color.view(3, 1, 1) # [3, 1, 1] + mean = self.imagenet_mean.squeeze(0) # [3, 1, 1] + std = self.imagenet_std.squeeze(0) # [3, 1, 1] + normalized_color = (color - mean) / std + replacement_image = normalized_color.expand(-1, 224, 224) # [3, 224, 224] + + else: + raise ValueError(f"Unknown replacement strategy: {replacement}") + + return replacement_image + + def plot_image_mean_color(self, input_tensor): + normalized_mean = self.calculate_image_mean_color(input_tensor).unsqueeze(0) + plt.imshow(normalized_mean[0].permute(1, 2, 0)) + plt.show() + + def get_class_logit_batch( + self, input_batch: torch.Tensor, target_class_idx: int + ) -> torch.Tensor: + """Get logits for a batch of images - optimized for batched inference (directly from model outputs)""" + with torch.no_grad(): + outputs = self.model(input_batch) # Get raw logits + + # experiment with logarithms + # probabilities = self.get_predictions(input_batch) + # result = torch.log(probabilities) - torch.log(1 - probabilities) + + return outputs[:, target_class_idx] + + +def create_surrogate_dataset( + predictor: ModelPredictor, + input_batch: torch.Tensor, + segments: np.ndarray, + graph, # NetworkX graph + target_class_idx: int, + neighborhood_distance: int = 1, + batch_size: int = 16, +) -> tuple[np.ndarray, np.ndarray]: + """Create surrogate dataset for interpretability. + + Each row represents one masking operation: + - Features (X): Binary indicator vector [num_segments] - 1 if segment was masked, 0 otherwise + - Target (y): Delta score (original_logit - masked_logit) + + This dataset can be used for: + - Computing segment importance scores + - Training interpretable models (like LIME does) + - Analyzing masking effects + + Args: + predictor: ModelPredictor instance + input_batch: Input tensor batch + segments: Pixel-to-segment mapping array [H, W] + graph: NetworkX graph of spatial relationships + target_class_idx: Target class index + neighborhood_distance: Distance for neighborhood masking + batch_size: Batch size for processing segments + + Returns: + X: Binary indicator matrix [num_samples, num_segments] + y: Delta scores array [num_samples] + """ + # Get original logit + original_logit = predictor.get_class_logit_batch(input_batch, target_class_idx)[ + 0 + ].item() + print(f"Original logit: {original_logit}") + print( + f"Probability of class {target_class_idx}: " + f"{predictor.get_predictions(input_batch)[0, target_class_idx].item()}" + ) + + segment_ids = list(graph.nodes()) + num_segments = len(segment_ids) + + # Pre-compute local groups (segment + neighbors within distance) + local_groups = [] + for segment_id in segment_ids: + # Get neighbors within specified distance using BFS + neighbors = set([segment_id]) + current_layer = {segment_id} + + for _ in range(neighborhood_distance): + next_layer = set() + for node in current_layer: + next_layer.update(graph.neighbors(node)) + next_layer -= neighbors + neighbors.update(next_layer) + current_layer = next_layer + + local_groups.append(list(neighbors)) + + # Calculate deltas for all local groups + deltas = calculate_hyperpixel_deltas( + predictor, + input_batch, + segments, + local_groups, + target_class_idx, + batch_size=batch_size, + ) + + # Create surrogate dataset + num_samples = len(local_groups) + X = np.zeros((num_samples, num_segments), dtype=np.float32) + y = np.array(deltas, dtype=np.float32) + + # Fill indicator matrix + for i, masked_segments in enumerate(local_groups): + for segment_id in masked_segments: + X[i, segment_id] = 1.0 + + print(f"Created surrogate dataset: X shape {X.shape}, y shape {y.shape}") + print(f"Average delta: {y.mean():.4f}, std: {y.std():.4f}") + + return X, y + + +def calculate_scores_from_surrogate(X: np.ndarray, y: np.ndarray) -> dict[int, float]: + """Calculate averaged segment importance scores from surrogate dataset. + + For each segment, averages all delta scores where that segment was masked. + This provides an importance score representing how much the segment + contributes to the prediction. + + Args: + X: Binary indicator matrix [num_samples, num_segments] + y: Delta scores array [num_samples] + + Returns: + Dict mapping segment_id -> averaged score + """ + num_segments = X.shape[1] + scores = {} + + for segment_id in range(num_segments): + # Find all samples where this segment was masked + mask = X[:, segment_id] == 1.0 + + segment_scores = y[mask] + scores[segment_id] = float(segment_scores.mean()) + + score_values = list(scores.values()) + print(f"Score range: [{min(score_values):.4f}, {max(score_values):.4f}]") + + return scores + + +def get_predicted_class(predictor: ModelPredictor, input_batch: torch.Tensor) -> int: + """Get the predicted class index from model output""" + predictions = predictor.predict_image(input_batch, top_k=1) + return predictions[0][0] + + +def calculate_hyperpixel_deltas( + predictor: ModelPredictor, + input_batch: torch.Tensor, + segments: np.ndarray, + hyperpixel_segment_ids_list: list[list[int]], + target_class_idx: int, + batch_size: int = 64, +) -> list[float]: + """Calculate masking deltas for hyperpixel candidates using batched inference. + Handles internal batching to prevent memory overflow with large path counts. + + Args: + predictor: ModelPredictor instance + input_batch: Input tensor batch [1, 3, H, W] + segments: Pixel-to-segment mapping array [H, W] + hyperpixel_segment_ids_list: List of segment ID lists, e.g. [[1,2,3], [4,5,6]] + target_class_idx: Target class index + batch_size: Batch size + + Returns: + List[float]: Delta scores for each candidate + """ + if not hyperpixel_segment_ids_list: + return [] + + # Validate all segment lists are non-empty + for i, segment_ids in enumerate(hyperpixel_segment_ids_list): + if not segment_ids: + raise ValueError(f"Empty segment list at index {i}") + + with torch.no_grad(): # Prevent gradient accumulation + original_logit = predictor.get_class_logit_batch(input_batch, target_class_idx)[ + 0 + ].item() + + # Get replacement image using the specified strategy + assert predictor.replacement_image is not None + replacement_image = predictor.replacement_image + + # Process in batches to avoid memory overflow + all_deltas = [] + num_masks = len(hyperpixel_segment_ids_list) + + for batch_start in range(0, num_masks, batch_size): + batch_end = min(batch_start + batch_size, num_masks) + current_batch_size = batch_end - batch_start + + batch_inputs = input_batch.repeat(current_batch_size, 1, 1, 1) + + # Convert segments numpy array to GPU tensor once + gpu_segments = torch.from_numpy(segments).to(predictor.device) + + for i, segment_ids in enumerate( + hyperpixel_segment_ids_list[batch_start:batch_end] + ): + # Optimized: Use torch.isin for fast GPU-based mask creation + target_ids = torch.tensor( + segment_ids, dtype=gpu_segments.dtype, device=predictor.device + ) + combined_mask = torch.isin(gpu_segments, target_ids) + + # Apply mask with proper broadcasting + batch_inputs[i] = torch.where( + combined_mask.unsqueeze(0), # [1, H, W] broadcasts to [3, H, W] + replacement_image, # [3, H, W] + batch_inputs[i], # [3, H, W] + ) + + masked_logits = predictor.get_class_logit_batch( + batch_inputs, target_class_idx + ) + batch_deltas = [ + original_logit - masked_logit.item() for masked_logit in masked_logits + ] + all_deltas.extend(batch_deltas) + + # Memory cleanup + del batch_inputs, masked_logits + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + return all_deltas + + +def select_top_hyperpixels( + hyperpixels: list[dict], max_hyperpixels: int = 10 +) -> list[dict]: + """Select top hyperpixels by their primary algorithm-specific score""" + # Use hyperpixel_score + return sorted( + hyperpixels, + key=lambda hp: abs(hp.get("hyperpixel_score", 0)), + reverse=True, + )[:max_hyperpixels] diff --git a/ciao/utils/search_utils.py b/ciao/utils/search_utils.py new file mode 100644 index 0000000..81e26fc --- /dev/null +++ b/ciao/utils/search_utils.py @@ -0,0 +1,39 @@ +"""Shared utilities for MCTS and MCGS search algorithms. + +This module contains common functions used by both Monte Carlo Tree Search (MCTS) +and Monte Carlo Graph Search (MCGS) implementations. +""" + +import numpy as np +import torch + +from ciao.structures.bitmask_graph import get_frontier, iter_bits +from ciao.utils.calculations import ModelPredictor, calculate_hyperpixel_deltas + + +def is_terminal(mask: int, adj_masks: tuple, used_mask: int, max_depth: int) -> bool: + """Check if state is terminal (max depth or no frontier).""" + return ( + mask.bit_count() >= max_depth or get_frontier(mask, adj_masks, used_mask) == 0 + ) + + +def evaluate_masks( + predictor: ModelPredictor, + input_batch: torch.Tensor, + segments: np.ndarray, + target_class_idx: int, + masks: list[int], +) -> list[float]: + """Evaluate multiple segment masks by computing class score deltas (batched).""" + all_segment_ids = [list(iter_bits(mask)) for mask in masks] + + rewards = calculate_hyperpixel_deltas( + predictor=predictor, + input_batch=input_batch, + segments=segments, + target_class_idx=target_class_idx, + hyperpixel_segment_ids_list=all_segment_ids, + ) + + return rewards diff --git a/ciao/utils/segmentation.py b/ciao/utils/segmentation.py new file mode 100644 index 0000000..39fbe03 --- /dev/null +++ b/ciao/utils/segmentation.py @@ -0,0 +1,289 @@ +import math + +import networkx as nx +import numpy as np + + +def hex_round(q, r): + """Round axial coordinates to nearest hex. + + Args: + q, r: Fractional axial coordinates + + Returns: + (q, r): Integer axial coordinates of nearest hex + """ + x = q + z = r + y = -x - z + + rx = round(x) + ry = round(y) + rz = round(z) + + dx = abs(rx - x) + dy = abs(ry - y) + dz = abs(rz - z) + + if dx > dy and dx > dz: + rx = -ry - rz + elif dy > dz: + ry = -rx - rz + else: + rz = -rx - ry + + return int(rx), int(rz) # back to axial (q = x, r = z) + + +def pixel_to_hex(px, py, size): + """Convert pixel coordinate to axial hex coordinates. + + Args: + px, py: Pixel coordinates + size: Hex size (distance from center to flat edge for flat-top hexagons) + + Returns: + (q, r): Axial coordinates of the hex containing this pixel + """ + q = (math.sqrt(3) / 3 * px - 1 / 3 * py) / size + r = (2 / 3 * py) / size + return hex_round(q, r) + + +def build_hex_adjacency_graph(hex_to_id): + """Build adjacency graph for hexagonal grid using axial coordinate neighbors. + + Hexagons have exactly 6 neighbors with well-defined axial coordinate offsets: + [(+1,0), (+1,-1), (0,-1), (-1,0), (-1,+1), (0,+1)] + + Args: + hex_to_id: Dictionary mapping (q, r) axial coords to segment IDs + + Returns: + NetworkX graph with edges between adjacent hexagons + """ + adj_graph = nx.Graph() + adj_graph.add_nodes_from(hex_to_id.values()) + + # Six neighbor offsets for flat-top hexagons in axial coordinates + hex_neighbors = [ + (+1, 0), # East + (+1, -1), # Northeast + (0, -1), # Northwest + (-1, 0), # West + (-1, +1), # Southwest + (0, +1), # Southeast + ] + + for (q, r), seg_id in hex_to_id.items(): + for dq, dr in hex_neighbors: + neighbor_key = (q + dq, r + dr) + if neighbor_key in hex_to_id: + neighbor_id = hex_to_id[neighbor_key] + adj_graph.add_edge(seg_id, neighbor_id) + + return adj_graph + + +def build_adjacency_graph(segments, neighborhood=8): + adj_graph = nx.Graph() + segment_ids = np.unique(segments) + adj_graph.add_nodes_from(segment_ids) + + height, width = segments.shape + + # Check horizontal adjacency + for y in range(height): + for x in range(width - 1): + seg1, seg2 = segments[y, x], segments[y, x + 1] + if seg1 != seg2: + adj_graph.add_edge(seg1, seg2) + + # Check vertical adjacency + for y in range(height - 1): + for x in range(width): + seg1, seg2 = segments[y, x], segments[y + 1, x] + if seg1 != seg2: + adj_graph.add_edge(seg1, seg2) + + if neighborhood == 8: + # Add diagonal adjacency for 8-neighborhood + for y in range(height - 1): + for x in range(width - 1): + center_seg = segments[y, x] + # Check diagonal neighbors + if segments[y + 1, x + 1] != center_seg: + adj_graph.add_edge(center_seg, segments[y + 1, x + 1]) + if x > 0 and segments[y + 1, x - 1] != center_seg: + adj_graph.add_edge(center_seg, segments[y + 1, x - 1]) + + return adj_graph + + +def build_fast_adjacency_list(hex_to_id, max_id): + """Vytvoří 'static adjacency list' optimalizovaný pro rychlé čtení. + + Args: + hex_to_id: Dict mapující (q, r) -> int_id (0 až N-1) + max_id: Celkový počet segmentů (N) + + Returns: + adj_list: Tuple of Tuples. + adj_list[5] vrátí např. (4, 6, 12) - sousedy segmentu 5. + """ + # Inicializujeme prázdné listy pro každé ID + # Používáme list listů pro konstrukci + temp_adj = [[] for _ in range(max_id)] + + # Offsets pro sousedy (axial coords) + hex_neighbors = [(+1, 0), (+1, -1), (0, -1), (-1, 0), (-1, +1), (0, +1)] + + for (q, r), seg_id in hex_to_id.items(): + for dq, dr in hex_neighbors: + neighbor_key = (q + dq, r + dr) + + # Pokud soused existuje (je uvnitř obrázku) + if neighbor_key in hex_to_id: + neighbor_id = hex_to_id[neighbor_key] + temp_adj[seg_id].append(neighbor_id) + + # Konverze na tuple of tuples pro maximální rychlost čtení a paměťovou efektivitu + # Seřadíme sousedy (volitelné, ale dobré pro determinismus) + final_adj = tuple(tuple(sorted(neighbors)) for neighbors in temp_adj) + + return final_adj + + +# --- Upravená funkce create_hexagonal_grid --- + + +def create_hexagonal_grid_with_list(input_tensor, hex_radius=14): + channels, height, width = input_tensor.shape + segments = np.zeros((height, width), dtype=np.int32) + + hex_to_id = {} + next_id = 0 + + # 1. Mapování pixelů na Hex ID + # (Tohle je nejpomalejší část, ale běží jen jednou při initu) + for y in range(height): + for x in range(width): + q, r = pixel_to_hex(x, y, hex_radius) + key = (q, r) + + if key not in hex_to_id: + hex_to_id[key] = next_id + next_id += 1 + + segments[y, x] = hex_to_id[key] + + # 2. Vytvoření Rychlého Grafu (žádný NetworkX) + adjacency_list = build_fast_adjacency_list(hex_to_id, next_id) + + return segments, adjacency_list, next_id + + +def build_adjacency_bitmasks(adj_list): + """Převede adjacency list na seznam integerů. + adj_masks[5] bude integer, který má jedničky na pozicích sousedů hexu 5. + """ + adj_masks = [] + for neighbors in adj_list: + mask = 0 + for n in neighbors: + mask |= 1 << n + adj_masks.append(mask) + return tuple(adj_masks) + + +def create_square_grid(input_tensor, square_size=14, neighborhood=8): + """Create a grid of squares with graph structure representing spatial relationships""" + channels, height, width = input_tensor.shape + segments = np.zeros((height, width), dtype=np.int32) + + segment_id = 0 + + # Create square grid + for row in range(0, height, square_size): + for col in range(0, width, square_size): + # Define square boundaries + row_end = min(row + square_size, height) + col_end = min(col + square_size, width) + + # Assign segment ID to all pixels in this square + segments[row:row_end, col:col_end] = segment_id + segment_id += 1 + + # Build adjacency graph + adjacency_graph = build_adjacency_graph(segments, neighborhood=neighborhood) + + return segments, adjacency_graph + + +def create_hexagonal_grid(input_tensor, hex_radius=14, neighborhood=6): + """Create a grid of hexagons with graph structure representing spatial relationships. + + Uses axial coordinate system for precise hexagonal tiling (flat-top orientation). + Each hexagon has exactly 6 neighbors (neighborhood parameter ignored). + + Args: + input_tensor: Input image tensor [C, H, W] + hex_radius: Hex size parameter (distance from center to flat edge, default: 14) + neighborhood: Ignored for hexagons (always 6-connected) + + Returns: + segments: 2D array mapping pixels to segment IDs + adjacency_graph: NetworkX graph of segment relationships + """ + channels, height, width = input_tensor.shape + segments = np.zeros((height, width), dtype=np.int32) + + # Map axial coordinates (q, r) to unique segment IDs + hex_to_id = {} + next_id = 0 + + # Assign each pixel to its corresponding hex using axial coordinates + for y in range(height): + for x in range(width): + q, r = pixel_to_hex(x, y, hex_radius) + key = (q, r) + + if key not in hex_to_id: + hex_to_id[key] = next_id + next_id += 1 + + segments[y, x] = hex_to_id[key] + + # Build adjacency graph using axial coordinate neighbors (always 6-connected) + adjacency_graph = build_hex_adjacency_graph(hex_to_id) + + return segments, adjacency_graph + + +def create_segmentation( + input_tensor, segmentation_type="hexagonal", segment_size=14, neighborhood=8 +): + """Create image segmentation with specified type. + + Args: + input_tensor: Input image tensor [C, H, W] + segmentation_type: "square" or "hexagonal" + segment_size: Size parameter (square_size or hex_radius) + neighborhood: Neighborhood connectivity (4, 6, or 8) + + Returns: + segments: 2D array mapping pixels to segment IDs + adjacency_graph: NetworkX graph of segment relationships + """ + if segmentation_type == "square": + return create_square_grid( + input_tensor, square_size=segment_size, neighborhood=neighborhood + ) + elif segmentation_type == "hexagonal": + return create_hexagonal_grid( + input_tensor, hex_radius=segment_size, neighborhood=neighborhood + ) + else: + raise ValueError( + f"Unknown segmentation_type: {segmentation_type}. Use 'square' or 'hexagonal'." + ) From 9e3d00d3d2e41c9c65a925c2a956f9f2241bcb34 Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Mon, 23 Feb 2026 16:57:55 +0100 Subject: [PATCH 07/25] feat: implement CIAO search algorithms Add MCTS, MCGS, lookahead, and potential search algorithms for hyperpixel construction. --- ciao/algorithm/__init__.py | 3 + ciao/algorithm/lookahead_bitset.py | 324 ++++++++++++++ ciao/algorithm/mcgs.py | 684 +++++++++++++++++++++++++++++ ciao/algorithm/mcts.py | 607 +++++++++++++++++++++++++ ciao/algorithm/potential.py | 544 +++++++++++++++++++++++ 5 files changed, 2162 insertions(+) create mode 100644 ciao/algorithm/__init__.py create mode 100644 ciao/algorithm/lookahead_bitset.py create mode 100644 ciao/algorithm/mcgs.py create mode 100644 ciao/algorithm/mcts.py create mode 100644 ciao/algorithm/potential.py diff --git a/ciao/algorithm/__init__.py b/ciao/algorithm/__init__.py new file mode 100644 index 0000000..ab946c9 --- /dev/null +++ b/ciao/algorithm/__init__.py @@ -0,0 +1,3 @@ +"""CIAO algorithm implementations.""" + +__all__ = [] \ No newline at end of file diff --git a/ciao/algorithm/lookahead_bitset.py b/ciao/algorithm/lookahead_bitset.py new file mode 100644 index 0000000..e3ecdf4 --- /dev/null +++ b/ciao/algorithm/lookahead_bitset.py @@ -0,0 +1,324 @@ +"""Greedy lookahead hyperpixel building with bitmask operations. + +Rolling horizon strategy: Look ahead multiple steps but only commit one step at a time. +""" + +from ciao.structures.bitmask_graph import ( + add_node, + get_frontier, + iter_bits, + mask_to_ids, +) +from ciao.utils.calculations import calculate_hyperpixel_deltas + + +def build_hyperpixel_greedy_lookahead( + predictor, + input_batch, + segments, + adj_masks: tuple, + target_class_idx: int, + scores: dict, + seed_idx: int, + desired_length: int, + lookahead_distance: int, + optimization_sign: int, + used_mask: int, + batch_size: int = 64, +) -> dict: + """Build a single hyperpixel using greedy lookahead with rolling horizon. + + Strategy: Look ahead up to lookahead_distance steps, evaluate all candidates, + but only commit the first step of the best path found. + + Args: + predictor: Model predictor + input_batch: Preprocessed image + segments: Segmentation map + adj_masks: Adjacency bitmasks + target_class_idx: Target class + scores: Segment scores (for determining sign) + seed_idx: Starting segment + desired_length: Target hyperpixel size + lookahead_distance: How many steps to look ahead (1=greedy, 2+=lookahead) + optimization_sign: +1 to maximize, -1 to minimize + used_mask: Globally excluded segments + batch_size: Batch size for evaluation + + Returns: + Dict with segments, sign, scores, final mask, and stats + """ + current_mask = add_node(0, seed_idx) + path = [seed_idx] # Track the path for prefix evaluation + total_evaluations = 0 # Track total number of evaluations + num_steps = 0 + + print(f" Starting greedy lookahead from seed {seed_idx}") + + # Grow hyperpixel one step at a time + while current_mask.bit_count() < desired_length: + num_steps += 1 + current_size = current_mask.bit_count() + + # Generate all candidate masks via BFS up to lookahead_distance + candidates = _generate_lookahead_candidates( + current_mask=current_mask, + adj_masks=adj_masks, + used_mask=used_mask, + lookahead_distance=lookahead_distance, + max_total_size=desired_length, + ) + + if not candidates: + print( + f" Step {num_steps}: No candidates available, stopping at size {current_size}" + ) + break + + print( + f" Step {num_steps}: Size={current_size}/{desired_length}, evaluating {len(candidates)} candidates..." + ) + + # Batch evaluate all candidates + candidate_masks = list(candidates.keys()) + segment_id_lists = [mask_to_ids(mask) for mask in candidate_masks] + total_evaluations += len(candidate_masks) + + scores_list = calculate_hyperpixel_deltas( + predictor=predictor, + input_batch=input_batch, + segments=segments, + hyperpixel_segment_ids_list=segment_id_lists, + target_class_idx=target_class_idx, + batch_size=batch_size, + ) + + # Find best candidate (maximize optimization_sign * score) + best_idx = max( + range(len(scores_list)), key=lambda i: scores_list[i] * optimization_sign + ) + best_mask = candidate_masks[best_idx] + best_score = scores_list[best_idx] + first_step = candidates[best_mask] + + print( + f" Step {num_steps}: Best score={best_score:.4f}, adding segment {first_step}" + ) + + # Commit only the first step + current_mask = add_node(current_mask, first_step) + path.append(first_step) + + # Evaluate all prefixes and find the best one + print(f" Evaluating {len(path)} prefixes to find best subset...") + num_prefix_evaluations = len(path) + total_evaluations += num_prefix_evaluations + + best_prefix_mask, best_score = _evaluate_prefixes( + path=path, + predictor=predictor, + input_batch=input_batch, + segments=segments, + target_class_idx=target_class_idx, + optimization_sign=optimization_sign, + batch_size=batch_size, + ) + + best_segments = mask_to_ids(best_prefix_mask) + print( + f" Best prefix has {len(best_segments)} segments with score={best_score:.4f}" + ) + + return { + "mask": best_prefix_mask, + "segments": best_segments, + "sign": optimization_sign, + "score": best_score, + "size": len(best_segments), + "stats": { + "method": "lookahead", + "lookahead_distance": lookahead_distance, + "num_steps": num_steps, + "total_evaluations": total_evaluations, + "prefix_evaluations": num_prefix_evaluations, + }, + } + + +def _generate_lookahead_candidates( + current_mask: int, + adj_masks: tuple, + used_mask: int, + lookahead_distance: int, + max_total_size: int, +) -> dict[int, int]: + """Generate all connected supersets up to lookahead_distance steps via BFS. + + Returns: + Dict mapping candidate_mask -> first_step_segment + """ + candidates = {} # mask -> first_step + + # BFS: Track masks at each depth + current_depth_masks = {current_mask} + + for depth in range(1, lookahead_distance + 1): + next_depth_masks = set() + + for mask in current_depth_masks: + if mask.bit_count() >= max_total_size: + continue + + frontier = get_frontier(mask, adj_masks, used_mask) + if frontier == 0: + continue + + # Expand to all frontier neighbors + for seg_id in iter_bits(frontier): + new_mask = add_node(mask, seg_id) + + # Determine first_step for this candidate + if depth == 1: + first_step = seg_id + else: + # Inherit first_step from parent mask + # Find which first_step led to this mask + # We need to track this through the BFS + # Since we're at depth > 1, the parent mask should already be in candidates + if mask in candidates: + first_step = candidates[mask] + else: + # This shouldn't happen in proper BFS, but handle edge case + # This mask came from current_mask, find the connection + first_step = _find_first_step(current_mask, new_mask) + + # Only add if not already seen (first path wins) + if new_mask not in candidates: + candidates[new_mask] = first_step + next_depth_masks.add(new_mask) + + current_depth_masks = next_depth_masks + if not current_depth_masks: + break + + return candidates + + +def _find_first_step(base_mask: int, target_mask: int) -> int: + """Find the first segment added from base_mask to reach target_mask.""" + diff = target_mask & ~base_mask + # Return the first bit in the difference + for seg_id in iter_bits(diff): + return seg_id + return -1 # Shouldn't happen + + +def _evaluate_prefixes( + path: list[int], + predictor, + input_batch, + segments, + target_class_idx: int, + optimization_sign: int, + batch_size: int, +) -> tuple[int, float]: + """Evaluate all prefixes of the path and return the best one. + + Returns: + (best_mask, best_score) + """ + # Generate all prefix masks + prefix_masks = [] + mask = 0 + for seg_id in path: + mask = add_node(mask, seg_id) + prefix_masks.append(mask) + + # Batch evaluate + segment_id_lists = [mask_to_ids(mask) for mask in prefix_masks] + scores = calculate_hyperpixel_deltas( + predictor=predictor, + input_batch=input_batch, + segments=segments, + hyperpixel_segment_ids_list=segment_id_lists, + target_class_idx=target_class_idx, + batch_size=batch_size, + ) + + # Find best prefix + best_idx = max(range(len(scores)), key=lambda i: scores[i] * optimization_sign) + + return prefix_masks[best_idx], scores[best_idx] + + +def build_all_hyperpixels_greedy_lookahead( + predictor, + input_batch, + segments, + adj_masks: tuple, + target_class_idx: int, + scores: dict, + max_hyperpixels: int, + desired_length: int, + lookahead_distance: int, + batch_size: int = 64, +) -> list[dict]: + """Build multiple hyperpixels using greedy lookahead. + + Returns: + List of hyperpixel dicts sorted by absolute score + """ + hyperpixels = [] + used_mask = 0 + + for i in range(max_hyperpixels): + # Find best unprocessed seed + available_segments = [ + seg_id for seg_id in scores if not (used_mask & (1 << seg_id)) + ] + + if not available_segments: + break + + seed_idx = max(available_segments, key=lambda x: abs(scores[x])) + seed_score = scores[seed_idx] + optimization_sign = 1 if seed_score >= 0 else -1 + + print(f"\n--- Hyperpixel {i + 1}/{max_hyperpixels} ---") + print(f"Seed: {seed_idx}, score: {seed_score:.4f}, sign: {optimization_sign}") + + result = build_hyperpixel_greedy_lookahead( + predictor=predictor, + input_batch=input_batch, + segments=segments, + adj_masks=adj_masks, + target_class_idx=target_class_idx, + scores=scores, + seed_idx=seed_idx, + desired_length=desired_length, + lookahead_distance=lookahead_distance, + optimization_sign=optimization_sign, + used_mask=used_mask, + batch_size=batch_size, + ) + + # Update used_mask + used_mask = result["mask"] | used_mask + + # Format for compatibility + hyperpixel = { + "segments": result["segments"], + "sign": result["sign"], + "size": result["size"], + "hyperpixel_score": result["score"], + "stats": result.get("stats", {}), # Include lookahead statistics + } + hyperpixels.append(hyperpixel) + + print( + f"Built hyperpixel with {len(result['segments'])} segments, score={result['score']:.4f}" + ) + + # Sort by absolute score + hyperpixels.sort(key=lambda x: abs(x["hyperpixel_score"]), reverse=True) + return hyperpixels diff --git a/ciao/algorithm/mcgs.py b/ciao/algorithm/mcgs.py new file mode 100644 index 0000000..fa32f97 --- /dev/null +++ b/ciao/algorithm/mcgs.py @@ -0,0 +1,684 @@ +"""Unified Monte Carlo Graph Search (MCGS) Implementation + +This module provides a unified MCGS implementation with multiple modes: +- mode='standard': Standard MCGS with eager expansion and optimized node creation +- mode='rave': RAVE (Rapid Action Value Estimation) with edge-level statistics + +All modes share: +- Tree structure for state exploration +- Eager expansion strategy (check entire frontier before creating nodes) +- Virtual loss for parallel batch safety +- MAX-based UCT for deterministic optimization + +Key features: +- MCGSNode: Tree nodes with edge-level statistics (for RAVE mode) +- use_guided_rollout: Choose between guided (weighted) vs pure random rollouts +""" + +import math +import random +from typing import Any + +import numpy as np +import torch +from tqdm import tqdm + +from ciao.structures.bitmask_graph import ( + add_node, + get_frontier, + iter_bits, + sample_connected_superset, +) +from ciao.utils.calculations import ModelPredictor +from ciao.structures.nodes import MCGSNode +from ciao.utils.search_utils import evaluate_masks, is_terminal + + +def select_uct_child( + node: MCGSNode, exploration_c: float, virtual_loss: float +) -> tuple[int, MCGSNode] | None: + """Select child with highest UCT score using edge statistics (MCGS mode). + + In MCGS, we use edge statistics rather than node statistics to handle DAGs correctly. + A child node may have high visits from other parents, which should not influence + selection from this parent. + + Returns: + (action, child) tuple with best UCT score, or None if no children + """ + if not node.children: + return None + + best_score = -float("inf") + best_action = None + best_child = None + + parent_visits = node.visits + 1 # +1 for numerical stability + + for action, child in node.children.items(): + # Get edge statistics (with virtual loss) + edge_stats = node.edge_stats.get( + action, {"N": 0, "W": 0.0, "Q": 0.0, "max_reward": -float("inf")} + ) + pending = node.pending_edges.get(action, 0) + + # Use edge visit count (not child.visits) with virtual loss + edge_n = edge_stats["N"] + pending * virtual_loss + + # Exploitation: Use edge max_reward (not child.max_value) + exploit = edge_stats["max_reward"] if edge_stats["N"] > 0 else 0.0 + + # Exploration: Use edge visit count in denominator + explore = exploration_c * math.sqrt( + math.log(parent_visits) / max(1, edge_n) + ) + + score = exploit + explore + + if score > best_score: + best_score = score + best_action = action + best_child = child + + if best_child is not None and best_action is not None: + return (best_action, best_child) + return None + + +def select_uct_child_rave( + node: MCGSNode, exploration_c: float, virtual_loss: float, rave_k: float +) -> tuple[int, MCGSNode] | None: + """Select child with highest UCT score using RAVE mixing. + + RAVE formula: + beta = sqrt(k / (3 * N_edge + k)) + Q_combined = (1 - beta) * Q_edge + beta * Q_rave + UCT = Q_combined + c * sqrt(log(N_parent) / N_edge) + + Returns: + (action, child) tuple with best UCT score, or None if no children + """ + if not node.children: + return None + + best_score = -float("inf") + best_action = None + best_child = None + + parent_visits = node.visits + 1 # +1 for numerical stability + + for action, child in node.children.items(): + # Get edge statistics (with virtual loss) + edge_stats = node.edge_stats.get( + action, {"N": 0, "W": 0.0, "Q": 0.0, "max_reward": -float("inf")} + ) + rave_stats = node.rave_stats.get(action, {"N": 0, "W": 0.0, "Q": 0.0}) + pending = node.pending_edges.get(action, 0) + edge_n = edge_stats["N"] + pending * virtual_loss + + # RAVE mixing + # Use actual edge statistics only if we have real visits (not just virtual loss) + if edge_stats["N"] > 0: + # Calculate beta (mixing parameter) + beta = math.sqrt(rave_k / (3 * edge_n + rave_k)) + + # Get Q values: + # - For MC (edge): Use MAX reward (deterministic optimization) + # - For RAVE: Use MEAN reward (AMAF is a heuristic average) + q_edge = edge_stats["max_reward"] + q_rave = rave_stats["Q"] if rave_stats["N"] > 0 else 0.0 + + # Combined Q value + q_combined = (1 - beta) * q_edge + beta * q_rave + else: + # No edge visits yet, use pure RAVE or 0 + q_combined = rave_stats["Q"] if rave_stats["N"] > 0 else 0.0 + + # UCT exploration term (uses edge visits, not child.visits) + explore = exploration_c * math.sqrt(math.log(parent_visits) / max(1, edge_n)) + + # Final UCT score + score = q_combined + explore + + if score > best_score: + best_score = score + best_action = action + best_child = child + + if best_child is not None and best_action is not None: + return (best_action, best_child) + return None + + +def expand_node_eager( + node: MCGSNode, + adj_masks: tuple, + used_mask: int, + transposition_table: dict[int, MCGSNode], + mode: str, +) -> tuple[int, MCGSNode] | None: + """Eager expansion with grafting: Check ALL frontier segments before creating a new node. + + This function processes the entire frontier to: + 1. Skip segments that are already children + 2. Link existing nodes from transposition table (grafting) + 3. Identify truly new candidates for node creation + 4. Randomly select one new candidate to expand + + Returns: + (segment_id, child_node) if a NEW node was created + None if all frontier segments are already children or grafted + """ + frontier = get_frontier(node.mask, adj_masks, used_mask) + + if frontier == 0: + return None + + existing_children_ids = set(node.children.keys()) + + new_candidates = [] + + for seg_id in iter_bits(frontier): + # Check: Already a child? + if seg_id in existing_children_ids: + continue # Already linked, skip + + new_mask = add_node(node.mask, seg_id) + + # Check: Does this state already exist in the graph? + if new_mask in transposition_table: + # GRAFT: Link existing node (DAG structure) + existing_node = transposition_table[new_mask] + node.children[seg_id] = existing_node + if mode == "rave": + node.init_edge(seg_id) + else: + # Truly new: add to candidates for potential creation + new_candidates.append((seg_id, new_mask)) + + if not new_candidates: + # All frontier segments are already children or grafted + return None + + # Pick one random new candidate and create it + seg_id, new_mask = random.choice(new_candidates) + child = MCGSNode(mask=new_mask) + transposition_table[new_mask] = child + node.children[seg_id] = child + if mode == "rave": + node.init_edge(seg_id) + + return seg_id, child + + +def update_edge_stats(node: MCGSNode, action: int, reward: float): + """Update edge statistics for a specific action (RAVE mode).""" + if action not in node.edge_stats: + node.init_edge(action) + + stats = node.edge_stats[action] + stats["N"] += 1 + stats["W"] += reward + stats["Q"] = stats["W"] / stats["N"] + stats["max_reward"] = max(stats["max_reward"], reward) + + +def update_rave_stats(node: MCGSNode, action: int, reward: float): + """Update RAVE statistics for a specific action (RAVE mode).""" + if action not in node.rave_stats: + node.init_edge(action) + + stats = node.rave_stats[action] + stats["N"] += 1 + stats["W"] += reward + stats["Q"] = stats["W"] / stats["N"] + stats["max_reward"] = max(stats.get("max_reward", -float("inf")), reward) + + +def backup_paths( + batch_paths: list[list[MCGSNode]], + batch_actions: list[list[int]], + rewards: list[float] +) -> None: + """Backup rewards through all nodes in the paths (standard mode). + + Updates: + - visits, value_sum (mean tracking) + - max_value (MAX backup for selection) + - edge statistics (track edge-level stats for proper DAG handling) + - pending_edges (release virtual loss) + + Args: + batch_paths: List of node paths (one per simulation) + batch_actions: List of action sequences (one per simulation) + rewards: List of rewards for each path + """ + for path, actions, reward in zip(batch_paths, batch_actions, rewards): + for i, node in enumerate(path): + # Update node statistics + node.visits += 1 + node.value_sum += reward # Mean tracking + node.max_value = max(node.max_value, reward) # MAX backup + + # Update edge statistics and release virtual loss + if i > 0: # Skip root (no incoming edge) + action = actions[i - 1] # Action that led to this node + parent = path[i - 1] + + # Release virtual loss on edge + if action in parent.pending_edges: + parent.pending_edges[action] = max(0, parent.pending_edges[action] - 1) + + # Update edge statistics + update_edge_stats(parent, action, reward) + + +def backup_paths_rave( + batch_paths: list[list[MCGSNode]], + batch_actions: list[list[int]], + batch_masks: list[int], + rewards: list[float], + adj_masks: tuple, + used_mask: int, +) -> None: + """Backup rewards using edge-level statistics and RAVE updates (RAVE mode). + + Standard Backup: + - Update visits and max_value for nodes on the path + + Edge-level Backup: + - Update edge statistics for the action taken from each node + + RAVE Backup (AMAF - All-Moves-As-First): + - For each node in path, check ALL its frontier segments + - If a segment appears in the rollout, update its RAVE stats + - This generalizes learning: "if we picked X later, it was good/bad" + + Args: + batch_paths: List of node paths (one per simulation) + batch_actions: List of action sequences (one per path) + batch_masks: List of final rollout masks + rewards: List of rewards for each path + adj_masks: Adjacency bitmasks for frontier calculation + used_mask: Globally excluded segments + """ + for path, actions, rollout_mask, reward in zip( + batch_paths, batch_actions, batch_masks, rewards + ): + rollout_segments = set(iter_bits(rollout_mask)) + + for i, node in enumerate(path): + # --- STANDARD BACKUP --- + node.visits += 1 + node.value_sum += reward + node.max_value = max(node.max_value, reward) + + # --- EDGE-LEVEL BACKUP --- + # Update edge statistics for the action taken from this node + if i < len(actions): + action = actions[i] + update_edge_stats(node, action, reward) + + # --- RAVE BACKUP (AMAF) --- + # Update RAVE stats for frontier segments that appeared in rollout + # Optimized: iterate through rollout_segments (small) instead of frontier (large) + frontier = get_frontier(node.mask, adj_masks, used_mask) + frontier_bits = frontier # Keep as bitmask for fast membership check + for seg_id in rollout_segments: + # Check if this rollout segment was legal from this node + if (frontier_bits >> seg_id) & 1: # Fast bit check + update_rave_stats(node, seg_id, reward) + + # --- RELEASE VIRTUAL LOSS --- + if i < len(actions): + action = actions[i] + if action in node.pending_edges: + node.pending_edges[action] = max(0, node.pending_edges[action] - 1) + + +def build_hyperpixel_mcgs( + predictor: ModelPredictor, + input_batch: torch.Tensor, + segments: np.ndarray, + adj_masks: tuple[int, ...], + target_class_idx: int, + seed_idx: int, + desired_length: int, + num_iterations: int, + mode: str = "standard", + optimization_sign: int = 1, + batch_size: int = 64, + exploration_c: float = 1.4, + virtual_loss: float = 1.0, + used_mask: int = 0, + # RAVE-specific parameters + rave_k: float = 1000.0, +) -> dict[str, Any]: + """Unified Monte Carlo Graph Search for hyperpixel selection. + + This function implements two MCGS modes: + - 'standard': Standard MCGS with eager expansion and optimized grafting + - 'rave': RAVE with edge-level statistics + + Args: + predictor: Model for evaluating segment masks + input_batch: Preprocessed image tensor + segments: Segmentation map array + adj_masks: Adjacency bitmasks for each segment + target_class_idx: Target class to explain + seed_idx: Starting segment index + desired_length: Maximum hyperpixel size + num_iterations: Number of MCGS iterations + mode: 'standard' or 'rave' + optimization_sign: +1 to maximize deltas, -1 to minimize + batch_size: Number of paths to collect per iteration + exploration_c: UCT exploration constant + virtual_loss: Multiplier for pending counter in UCT + used_mask: Bitmask of globally excluded segments + rave_k: RAVE mixing parameter (for mode="rave") + + Returns: + Dict with best mask, score, and search statistics + """ + if mode not in ["standard", "rave"]: + raise ValueError(f"Invalid mode: {mode}. Must be 'standard' or 'rave'") + + # --- INITIALIZATION --- + # Create transposition table for state deduplication (DAG structure) + transposition_table = {} + + # Create root node + root_mask = add_node(0, seed_idx) + root = MCGSNode(mask=root_mask) + transposition_table[root_mask] = root + + best_mask = root.mask + best_score = -float("inf") + best_score_history = [] + + # Statistics tracking + total_cache_hits = 0 + total_gpu_evaluations = 0 + + # --- MAIN MCGS LOOP --- + mode_label = f"MCGS-{mode.upper()}" + for iteration in tqdm(range(num_iterations), desc=f" {mode_label}", ncols=80): + # --- PHASE 1: BATCH COLLECTION --- + batch_paths = [] + batch_masks = [] + cached_rewards = [] # Store cached values for visited terminals + needs_gpu_eval = [] # Track which entries need GPU evaluation + batch_actions = [] # For RAVE mode: track actions taken + + for _ in range(batch_size): + # --- SELECTION with EAGER EXPANSION --- + node = root + path = [node] + actions_taken = [] # Track actions for RAVE + + # Continue descending until we create a new node or reach terminal + while ( + expansion_result := expand_node_eager( + node, adj_masks, used_mask, transposition_table, mode + ) + ) is None and not is_terminal( + node.mask, adj_masks, used_mask, desired_length + ): + # All frontier segments are already children or grafted - select best child + if mode == "rave": + uct_result = select_uct_child_rave( + node, exploration_c, virtual_loss, rave_k + ) + assert uct_result is not None + action, child = uct_result + + # Apply virtual loss to edge + if action not in node.pending_edges: + node.pending_edges[action] = 0 + node.pending_edges[action] += 1 + + actions_taken.append(action) + else: + uct_result = select_uct_child(node, exploration_c, virtual_loss) + assert uct_result is not None + action, child = uct_result + + # Apply virtual loss to edge + if action not in node.pending_edges: + node.pending_edges[action] = 0 + node.pending_edges[action] += 1 + + actions_taken.append(action) + + node = child + path.append(node) + + # --- EXPANSION --- + if expansion_result is not None: + seg_id, child = expansion_result + + # Apply virtual loss to edge (both modes) + if seg_id not in node.pending_edges: + node.pending_edges[seg_id] = 0 + node.pending_edges[seg_id] += 1 + actions_taken.append(seg_id) + + node = child + path.append(node) + + # --- SIMULATION: Generate rollout mask --- + if ( + is_terminal(node.mask, adj_masks, used_mask, desired_length) + and node.visits > 0 + ): + rollout_mask = node.mask + cached_rewards.append(node.max_value) + needs_gpu_eval.append(False) + else: + if is_terminal(node.mask, adj_masks, used_mask, desired_length): + rollout_mask = node.mask + else: + # Random rollout using sample_connected_superset + frontier = get_frontier(node.mask, adj_masks, used_mask) + rollout_mask = sample_connected_superset( + base_mask=node.mask, + target_length=desired_length, + adj_masks=adj_masks, + base_frontier=frontier, + used_mask=used_mask, + ) + + cached_rewards.append(None) + needs_gpu_eval.append(True) + + batch_paths.append(path) + batch_masks.append(rollout_mask) + batch_actions.append(actions_taken) + + # --- PHASE 2: BATCH EVALUATION --- + # Separate masks that need GPU evaluation from cached ones + masks_to_evaluate = [ + (i, batch_masks[i]) + for i, need_eval in enumerate(needs_gpu_eval) + if need_eval + ] + + # Update statistics + cache_hits = sum(1 for need_eval in needs_gpu_eval if not need_eval) + total_cache_hits += cache_hits + total_gpu_evaluations += len(masks_to_evaluate) + + # Evaluate only masks that need GPU + gpu_rewards = [] + if masks_to_evaluate: + indices, masks = zip(*masks_to_evaluate) + raw_rewards = evaluate_masks( + predictor, input_batch, segments, target_class_idx, list(masks) + ) + gpu_rewards = [r * optimization_sign for r in raw_rewards] + + # Merge GPU results with cached values (cached values are already signed) + batch_rewards = [] + gpu_idx = 0 + for i in range(batch_size): + if not needs_gpu_eval[i]: + batch_rewards.append(cached_rewards[i]) + else: + # Use GPU result + batch_rewards.append(gpu_rewards[gpu_idx]) + gpu_idx += 1 + + # Update best score + for path_idx, (reward, rollout_mask) in enumerate( + zip(batch_rewards, batch_masks) + ): + if reward > best_score: + best_score = reward + best_mask = rollout_mask + + # --- PHASE 3: BACKPROPAGATION --- + if mode == "rave": + backup_paths_rave( + batch_paths=batch_paths, + batch_actions=batch_actions, + batch_masks=batch_masks, + rewards=batch_rewards, + adj_masks=adj_masks, + used_mask=used_mask, + ) + else: + backup_paths(batch_paths, batch_actions, batch_rewards) + + # Track best score history + best_score_history.append(best_score * optimization_sign) + + # --- RETURN RESULTS --- + # Convert best score back to raw (un-signed) value + best_score_raw = best_score * optimization_sign + + # Update used_mask with the segments from the best hyperpixel + updated_used_mask = used_mask + for seg_id in iter_bits(best_mask): + updated_used_mask |= 1 << seg_id + + result = { + "mask": best_mask, + "score": best_score_raw, + "used_mask": updated_used_mask, + "root": root, + "stats": { + "method": "mcgs", + "mode": mode, + "iterations": num_iterations, + "batch_size": batch_size, + "total_evaluations": num_iterations * batch_size, + "gpu_evaluations": total_gpu_evaluations, + "cache_hits": total_cache_hits, + "cache_hit_rate": total_cache_hits / (num_iterations * batch_size) + if num_iterations * batch_size > 0 + else 0, + "best_score_history": best_score_history, + "nodes": len(transposition_table), + "root_visits": root.visits, + }, + } + + # Add RAVE-specific data + if mode == "rave": + result["stats"]["rave_k"] = rave_k + + return result + + +def build_all_hyperpixels_mcgs( + predictor, + input_batch, + segments, + adj_masks, + target_class_idx, + scores, + next_id, + max_hyperpixels=10, + desired_length=30, + num_iterations=100, + mode="standard", + batch_size=64, + exploration_c=1.4, + virtual_loss=1.0, + rave_k=1000.0, +): + """Build multiple hyperpixels using MCGS. + + Args: + predictor: Model predictor + input_batch: Preprocessed input tensor + segments: Segmentation map + adj_masks: Adjacency bitmasks + target_class_idx: Target class index + scores: Individual segment scores + next_id: Total number of segments + max_hyperpixels: Maximum number of hyperpixels to build + desired_length: Target segments per hyperpixel + num_iterations: Number of MCGS iterations + mode: 'standard' or 'rave' + batch_size: Batch size for evaluation + exploration_c: UCT exploration constant + virtual_loss: Virtual loss multiplier for parallel safety + rave_k: RAVE parameter + + Returns: + List of hyperpixel dictionaries + """ + hyperpixels = [] + processed_segments = set() + used_mask = 0 + + for i in range(max_hyperpixels): + available_segments = [ + seg_id for seg_id in scores.keys() if seg_id not in processed_segments + ] + + if not available_segments: + break + + seed_idx = max(available_segments, key=lambda x: abs(scores[x])) + seed_score = scores[seed_idx] + optimization_sign = 1 if seed_score >= 0 else -1 + + result = build_hyperpixel_mcgs( + predictor=predictor, + input_batch=input_batch, + segments=segments, + adj_masks=adj_masks, + target_class_idx=target_class_idx, + seed_idx=seed_idx, + desired_length=desired_length, + num_iterations=num_iterations, + mode=mode, + optimization_sign=optimization_sign, + batch_size=batch_size, + exploration_c=exploration_c, + virtual_loss=virtual_loss, + used_mask=used_mask, + rave_k=rave_k, + ) + + hyperpixel_mask = result["mask"] + used_mask = result["used_mask"] + hyperpixel_segments = [ + seg_id for seg_id in range(next_id) if hyperpixel_mask & (1 << seg_id) + ] + + if hyperpixel_segments: + hyperpixels.append( + { + "segments": hyperpixel_segments, + "sign": optimization_sign, + "size": len(hyperpixel_segments), + "hyperpixel_score": result["score"], + "stats": result["stats"], # Include MCGS search statistics + } + ) + processed_segments.update(hyperpixel_segments) + + return hyperpixels diff --git a/ciao/algorithm/mcts.py b/ciao/algorithm/mcts.py new file mode 100644 index 0000000..f6a7ec8 --- /dev/null +++ b/ciao/algorithm/mcts.py @@ -0,0 +1,607 @@ +"""Unified Monte Carlo Tree Search for Connected Image Segments + +This module provides two MCTS variants controlled by the 'mode' parameter: +1. 'standard': Standard MCTS with UCT selection and random rollouts +2. 'rave': MCTS with Rapid Action Value Estimation (local + global RAVE) + +Both modes support: +- Batch collection and evaluation for GPU efficiency +- Virtual loss for parallel safety +- Terminal caching to avoid re-evaluating visited states +- MAX backup for finding peak explanations + +State = integer bitmask of selected segments +""" + +import math +import random +from typing import Any + +import numpy as np +import torch +from tqdm import tqdm + +from ciao.structures.bitmask_graph import ( + add_node, + get_frontier, + has_node, + iter_bits, + sample_connected_superset, +) +from ciao.utils.calculations import ModelPredictor +from ciao.structures.nodes import MCTSNode +from ciao.utils.search_utils import evaluate_masks, is_terminal + + +# ============================================================================ +# RAVE-specific Classes +# ============================================================================ + + +class GlobalStats: + """Global RAVE statistics shared across the entire search tree (RAVE mode only).""" + + def __init__(self, num_segments: int): + self.visits = np.zeros(num_segments, dtype=np.int32) + self.value_sum = np.zeros(num_segments, dtype=np.float32) + + def get_prior_score(self, seg_id: int) -> float: + """Get the global RAVE score for a segment (smart FPU).""" + if self.visits[seg_id] == 0: + return 0.0 + return self.value_sum[seg_id] / self.visits[seg_id] + + def update(self, rollout_mask: int, reward: float) -> None: + """Update global stats for all segments in the rollout.""" + for seg_id in iter_bits(rollout_mask): + self.visits[seg_id] += 1 + self.value_sum[seg_id] += reward + + +# ============================================================================ +# Shared Helper Functions +# ============================================================================ + + +def is_fully_expanded(node: MCTSNode, adj_masks: tuple, used_mask: int) -> bool: + """Check if all frontier segments have been expanded as children.""" + frontier = get_frontier(node.mask, adj_masks, used_mask) + + for seg_id in iter_bits(frontier): + if seg_id not in node.children: + return False + return True + + +# ============================================================================ +# Selection Functions +# ============================================================================ + + +def select_uct_child( + node: MCTSNode, exploration_c: float, virtual_loss: float +) -> MCTSNode | None: + """Select child with highest UCT score using MAX-value (simple and nested modes).""" + best_score = -float("inf") + best_child = None + + parent_visits = node.visits + 1 # +1 for numerical stability + + for child in node.children.values(): + # Virtual loss: increase effective visit count + effective_visits = child.visits + child.pending * virtual_loss + + # UCT formula with MAX value (not mean) + exploit = child.max_value if child.visits > 0 else 0.0 + explore = exploration_c * math.sqrt( + math.log(parent_visits) / max(1, effective_visits) + ) + score = exploit + explore + + if score > best_score: + best_score = score + best_child = child + + return best_child + + +def calculate_beta(visits: int, k: int = 1000) -> float: + """Calculate beta parameter for MC-RAVE combination (RAVE mode only). + + Beta controls the weight given to RAVE vs. real statistics: + - β = 1: Trust RAVE completely (at start) + - β → 0: Trust real statistics more (as visits increase) + + Formula: β = sqrt(k / (3 * visits + k)) + """ + return math.sqrt(k / (3 * visits + k)) + + +def select_uct_child_rave( + node: MCTSNode, exploration_c: float, virtual_loss: float, rave_k: int +) -> MCTSNode | None: + """Select child with highest MC-RAVE score (RAVE mode only). + + Combines real statistics (MAX value) with RAVE statistics (mean value) + using the optimistic beta parameter. + """ + best_score = -float("inf") + best_child = None + + parent_visits = node.visits + 1 # +1 for numerical stability + + for child in node.children.values(): + effective_visits = child.visits + child.pending * virtual_loss + beta = calculate_beta(child.visits, rave_k) if child.visits > 0 else 1.0 + q_real = child.max_value if child.visits > 0 else -float("inf") + q_rave = ( + (child.rave_value_sum / child.rave_visits) if child.rave_visits > 0 else 0.0 + ) + + # Combined Q with Global RAVE for unvisited nodes + if child.visits == 0: + # Use Global RAVE prior (smart FPU initialization) + q_combined = child.prior_score + else: + # Use local MC-RAVE: Optimistic combination of real and local RAVE + q_combined = (1 - beta) * q_real + beta * q_rave + + explore = exploration_c * math.sqrt( + math.log(parent_visits) / max(1, effective_visits) + ) + score = q_combined + explore + + if score > best_score: + best_score = score + best_child = child + + return best_child + + +# ============================================================================ +# Expansion Functions +# ============================================================================ + + +def expand_node( + node: MCTSNode, + adj_masks: tuple, + used_mask: int, + global_stats: GlobalStats | None = None, +) -> MCTSNode | None: + """Standard expansion: Pick one random unexpanded segment (standard and RAVE modes). + + Args: + node: Node to expand + adj_masks: Adjacency bitmasks + used_mask: Globally excluded segments + global_stats: Optional GlobalStats for RAVE prior initialization + + Returns: + New child node if created, None if no expansion possible + """ + frontier = get_frontier(node.mask, adj_masks, used_mask) + + unexpanded = [] + for seg_id in iter_bits(frontier): + if seg_id not in node.children: + unexpanded.append(seg_id) + + if not unexpanded: + return None + + # Create one new child + seg_id = random.choice(unexpanded) + child_mask = add_node(node.mask, seg_id) + + # Get prior score for RAVE mode + prior_score = ( + global_stats.get_prior_score(seg_id) if global_stats is not None else 0.0 + ) + + child = MCTSNode(mask=child_mask, parent=node, prior_score=prior_score) + node.children[seg_id] = child + + return child + + +# ============================================================================ +# Backup Functions +# ============================================================================ + + +def backup_paths(batch_paths: list[list[MCTSNode]], rewards: list[float]) -> None: + """Backup rewards using standard statistics (standard mode). + + Updates: + - visits, value_sum (mean tracking) + - max_value (MAX backup for selection) + - pending (release virtual loss) + """ + for path, reward in zip(batch_paths, rewards): + for node in path: + node.pending -= 1 # Release virtual loss + node.visits += 1 + node.value_sum += reward # Mean tracking + node.max_value = max(node.max_value, reward) # MAX backup + + +def backup_paths_rave( + batch_paths: list[list[MCTSNode]], + batch_rollout_masks: list[int], + rewards: list[float], + global_stats: GlobalStats | None = None, +) -> None: + """Backup rewards using standard, local RAVE, and global RAVE updates (RAVE mode). + + Standard Backup: + - Update visits and max_value for nodes on the path + + Local RAVE Backup (AMAF): + - For each node in path, check ALL its children + - If a child's segment appears in the rollout, update its RAVE stats + - This generalizes learning: "if we picked X later, it was good/bad" + + Global RAVE Backup: + - Update global statistics for all segments in the rollout + - Used for smart FPU initialization of new nodes + + Args: + batch_paths: List of node paths (one per rollout) + batch_rollout_masks: List of final rollout masks + rewards: List of rewards for each rollout + global_stats: Optional global RAVE statistics + """ + for path, rollout_mask, reward in zip(batch_paths, batch_rollout_masks, rewards): + # --- GLOBAL RAVE BACKUP --- + if global_stats is not None: + global_stats.update(rollout_mask, reward) + + for node in path: + # --- STANDARD BACKUP --- + node.pending -= 1 # Release virtual loss + node.visits += 1 + node.value_sum += reward # Mean tracking + node.max_value = max(node.max_value, reward) # MAX backup + + # --- LOCAL RAVE BACKUP (AMAF) --- + # Update RAVE stats for children that appeared in rollout + for child_seg_id, child in node.children.items(): + # Check if this child's segment appears in the rollout mask + if has_node(rollout_mask, child_seg_id): + # This segment was used in the rollout + child.rave_visits += 1 + child.rave_value_sum += reward + + +# ============================================================================ +# Main Unified MCTS Function +# ============================================================================ + + +def build_hyperpixel_mcts( + predictor: ModelPredictor, + input_batch: torch.Tensor, + segments: np.ndarray, + adj_masks: tuple[int, ...], + target_class_idx: int, + seed_idx: int, + desired_length: int, + num_iterations: int, + mode: str = "standard", + optimization_sign: int = 1, + batch_size: int = 64, + exploration_c: float = 1.4, + virtual_loss: float = 1.0, + used_mask: int = 0, + # RAVE-specific parameters + rave_k: int = 1000, +) -> dict[str, Any]: + """Build a hyperpixel using Monte Carlo Tree Search. + + Supports two modes: + 1. 'standard': Standard MCTS with random rollouts + 2. 'rave': MCTS with Rapid Action Value Estimation (local + global RAVE) + + Both modes include: + - Batch collection and evaluation + - Terminal caching to avoid re-evaluation + - Virtual loss for parallel safety + - MAX backup for finding peak explanations + + Args: + predictor: Model for evaluating segment masks + input_batch: Preprocessed image tensor + segments: Segmentation map array + adj_masks: Adjacency bitmasks for each segment + target_class_idx: Target class to explain + seed_idx: Starting segment index + desired_length: Target number of segments per hyperpixel + num_iterations: Number of MCTS iterations (batch collections) + mode: MCTS variant - 'standard' or 'rave' + optimization_sign: +1 to maximize deltas, -1 to minimize + batch_size: Number of leaf nodes to collect per iteration + exploration_c: UCT exploration constant + virtual_loss: Multiplier for pending counter in UCT + used_mask: Bitmask of globally excluded segments + rave_k: RAVE equivalence parameter (controls beta decay) - RAVE mode only + + Returns: + Dict with best mask, score, search statistics, and mode-specific data + """ + if mode not in ["standard", "rave"]: + raise ValueError(f"Invalid mode '{mode}'. Must be 'standard' or 'rave'.") + + # Initialize mode-specific components + global_stats = None + if mode == "rave": + num_segments = len(adj_masks) + global_stats = GlobalStats(num_segments) + + # Create root node + root_mask = add_node(0, seed_idx) + root = MCTSNode(mask=root_mask, parent=None) + + best_mask = root.mask + best_score = -float("inf") + best_score_history = [] + + # Statistics tracking + total_cache_hits = 0 + total_gpu_evaluations = 0 + + # --- MAIN MCTS LOOP --- + for _ in tqdm(range(num_iterations), desc=f"MCTS-{mode.upper()}", unit="iter"): + # --- PHASE 1: BATCH COLLECTION --- + batch_paths = [] + batch_rollout_masks = [] + cached_rewards = [] # Store cached values for visited terminals + needs_gpu_eval = [] # Track which entries need GPU evaluation + + for _ in range(batch_size): + # --- SELECTION --- + node = root + path = [node] + + # Standard selection for standard and RAVE modes + while is_fully_expanded(node, adj_masks, used_mask) and not is_terminal( + node.mask, adj_masks, used_mask, desired_length + ): + if mode == "rave": + child = select_uct_child_rave( + node, exploration_c, virtual_loss, rave_k + ) + else: + child = select_uct_child(node, exploration_c, virtual_loss) + + assert child is not None + child.pending += 1 + node = child + path.append(node) + + # --- EXPANSION --- + if not is_terminal(node.mask, adj_masks, used_mask, desired_length): + child = expand_node(node, adj_masks, used_mask, global_stats) + + if child is not None: + child.pending += 1 + node = child + path.append(node) + + # --- SIMULATION (or cache lookup) --- + # Check terminal cache + if ( + is_terminal(node.mask, adj_masks, used_mask, desired_length) + and node.visits > 0 + ): + # Reuse cached value - no GPU evaluation needed + rollout_mask = node.mask + cached_rewards.append(node.max_value) + needs_gpu_eval.append(False) + else: + # Need GPU evaluation + if is_terminal(node.mask, adj_masks, used_mask, desired_length): + rollout_mask = node.mask + else: + # Random rollout + frontier = get_frontier(node.mask, adj_masks, used_mask) + rollout_mask = sample_connected_superset( + base_mask=node.mask, + target_length=desired_length, + adj_masks=adj_masks, + base_frontier=frontier, + used_mask=used_mask, + ) + + cached_rewards.append(None) + needs_gpu_eval.append(True) + + batch_paths.append(path) + batch_rollout_masks.append(rollout_mask) + + # --- PHASE 2: BATCH EVALUATION --- + # Separate masks that need GPU evaluation from cached ones + masks_to_evaluate = [ + (i, batch_rollout_masks[i]) + for i, need_eval in enumerate(needs_gpu_eval) + if need_eval + ] + + # Update statistics + cache_hits = sum(1 for need_eval in needs_gpu_eval if not need_eval) + total_cache_hits += cache_hits + total_gpu_evaluations += len(masks_to_evaluate) + + # Evaluate only masks that need GPU + gpu_rewards = [] + if masks_to_evaluate: + indices, masks = zip(*masks_to_evaluate) + raw_rewards = evaluate_masks( + predictor, input_batch, segments, target_class_idx, list(masks) + ) + gpu_rewards = [r * optimization_sign for r in raw_rewards] + + # Merge GPU results with cached values (cached values are already signed) + batch_rewards = [] + gpu_idx = 0 + for i in range(batch_size): + if not needs_gpu_eval[i]: + batch_rewards.append(cached_rewards[i]) + else: + # Use GPU result + batch_rewards.append(gpu_rewards[gpu_idx]) + gpu_idx += 1 + + # Update best mask if we found a better one + for i, reward in enumerate(batch_rewards): + if reward > best_score: + best_score = reward + best_mask = batch_rollout_masks[i] + + # --- PHASE 3: BATCH BACKUP --- + if mode == "rave": + backup_paths_rave( + batch_paths=batch_paths, + batch_rollout_masks=batch_rollout_masks, + rewards=batch_rewards, + global_stats=global_stats, + ) + else: + backup_paths(batch_paths, batch_rewards) + + # Track best score after each iteration + best_score_history.append(best_score * optimization_sign) + + best_score = best_score * optimization_sign + + # Count total nodes created + def count_nodes(node: MCTSNode) -> int: + return 1 + sum(count_nodes(c) for c in node.children.values()) + + # Update used_mask with segments from best mask + updated_used_mask = used_mask + for seg_id in iter_bits(best_mask): + updated_used_mask = add_node(updated_used_mask, seg_id) + + # Build return dictionary with mode-specific stats + result = { + "mask": best_mask, + "score": best_score, + "used_mask": updated_used_mask, + "stats": { + "method": "mcts", + "mode": mode, + "iterations": num_iterations, + "batch_size": batch_size, + "total_evaluations": num_iterations * batch_size, + "gpu_evaluations": total_gpu_evaluations, + "cache_hits": total_cache_hits, + "cache_hit_rate": total_cache_hits / (num_iterations * batch_size) + if num_iterations * batch_size > 0 + else 0, + "best_score_history": best_score_history, + "nodes": count_nodes(root), + "root_visits": root.visits, + }, + "root": root, + } + + # Add RAVE-specific data + if mode == "rave": + result["stats"]["rave_k"] = rave_k + + return result + + +def build_all_hyperpixels_mcts( + predictor, + input_batch, + segments, + adj_masks, + target_class_idx, + scores, + next_id, + max_hyperpixels=10, + desired_length=30, + num_iterations=100, + mode="standard", + batch_size=64, + exploration_c=1.4, + virtual_loss=1.0, + rave_k=1000, +): + """Build multiple hyperpixels using MCTS. + + Args: + predictor: Model predictor + input_batch: Preprocessed input tensor + segments: Segmentation map + adj_masks: Adjacency bitmasks + target_class_idx: Target class index + scores: Individual segment scores + next_id: Total number of segments + max_hyperpixels: Maximum number of hyperpixels to build + desired_length: Target segments per hyperpixel + num_iterations: Number of MCTS iterations + mode: 'standard' or 'rave' + batch_size: Batch size for evaluation + exploration_c: UCT exploration constant + virtual_loss: Virtual loss multiplier for parallel safety + rave_k: RAVE parameter + + Returns: + List of hyperpixel dictionaries + """ + hyperpixels = [] + processed_segments = set() + used_mask = 0 + + for i in range(max_hyperpixels): + available_segments = [ + seg_id for seg_id in scores.keys() if seg_id not in processed_segments + ] + + if not available_segments: + break + + seed_idx = max(available_segments, key=lambda x: abs(scores[x])) + seed_score = scores[seed_idx] + optimization_sign = 1 if seed_score >= 0 else -1 + + result = build_hyperpixel_mcts( + predictor=predictor, + input_batch=input_batch, + segments=segments, + adj_masks=adj_masks, + target_class_idx=target_class_idx, + seed_idx=seed_idx, + desired_length=desired_length, + num_iterations=num_iterations, + mode=mode, + optimization_sign=optimization_sign, + batch_size=batch_size, + exploration_c=exploration_c, + virtual_loss=virtual_loss, + used_mask=used_mask, + rave_k=rave_k, + ) + + hyperpixel_mask = result["mask"] + used_mask = result["used_mask"] + hyperpixel_segments = [ + seg_id for seg_id in range(next_id) if hyperpixel_mask & (1 << seg_id) + ] + + if hyperpixel_segments: + hyperpixels.append( + { + "segments": hyperpixel_segments, + "sign": optimization_sign, + "size": len(hyperpixel_segments), + "hyperpixel_score": result["score"], + "stats": result["stats"], # Include MCTS search statistics + } + ) + processed_segments.update(hyperpixel_segments) + + return hyperpixels diff --git a/ciao/algorithm/potential.py b/ciao/algorithm/potential.py new file mode 100644 index 0000000..2f7a454 --- /dev/null +++ b/ciao/algorithm/potential.py @@ -0,0 +1,544 @@ +import time + +from ciao.structures.bitmask_graph import ( + add_node, + get_frontier, + iter_bits, + mask_to_ids, + remove_node, + sample_connected_superset, +) +from ciao.utils.calculations import calculate_hyperpixel_deltas + + +def compute_potentials( + cache: dict[int, list[tuple[int, float]]], +) -> dict[int, list[float]]: + """Compute potential vectors for each neighbor. + + NOTE: This uses a lexicographical dominance rule based on sorted scores. + This is a heuristic choice specific to this project's requirements. + Longer histories may have a statistical advantage. + """ + potentials = {} + for node_id, history in cache.items(): + scores = [score for _, score in history] + # Lexicographical sort (primary requirement) + scores.sort(reverse=True) + potentials[node_id] = scores + return potentials + + +def select_best_neighbor(potentials: dict[int, list[float]]) -> int: + """Select neighbor with highest potential using lexicographical comparison.""" + best_neighbor = -1 + best_vector = [] + + for node_id, scores in potentials.items(): + if not scores: + continue + + if scores > best_vector: + best_vector = scores + best_neighbor = node_id + + return best_neighbor + + +def build_hyperpixel_using_potential( + predictor, + input_batch, + segments, + adj_list: tuple[tuple[int, ...], ...], + adj_masks: tuple[int, ...], + target_class_idx: int, + desired_length: int, + seed_idx: int, + num_simulations: int, + used_segments: set | None = None, + batch_size: int = 64, + optimization_sign: int = 1, +): + """Build a hyperpixel using Sequential Monte Carlo with Potential-based Selection. + + This algorithm grows a connected region on the segmentation graph by: + 1. Computing the expansion frontier (valid neighbors of current structure) + 2. For each frontier node, running Monte Carlo simulations of random expansions + 3. Selecting the frontier node with the best potential (lexicographically highest scores) + 4. Pruning: Discarding all histories except the winner's ("wavefunction collapse") + 5. Repeating until target length is reached + + The result is a connected component optimized for the target class prediction. + + Args: + predictor: Model predictor for scoring hyperpixels + input_batch: Preprocessed input image tensor [1, C, H, W] + segments: Segmentation map [H, W] (pixel -> segment ID) + adj_list: Adjacency list (tuple of tuples, legacy parameter for compatibility) + adj_masks: Adjacency bitmasks (adj_masks[i] = neighbors of segment i) + target_class_idx: Class to optimize for + desired_length: Maximum hyperpixel size before prefix optimization + seed_idx: Starting segment (typically highest scoring unprocessed segment) + num_simulations: Monte Carlo samples per frontier node per iteration + used_segments: Globally excluded segments (e.g., from other hyperpixels) + batch_size: Batch size for model inference during evaluation + optimization_sign: +1 to maximize score, -1 to minimize + + Returns: + Dict with segments, mask, size, and statistics + """ + # Initialize optional set + if used_segments is None: + used_segments = set() + + # Strict Type Separation: Convert set to mask ONCE at entry + used_mask = 0 + for seg_id in used_segments: + used_mask = add_node(used_mask, seg_id) + + # Initialize current hyperpixel structure + hyperpixel_structure = [seed_idx] + structure_mask = add_node(0, seed_idx) + + # Potential cache: Maps frontier_node_id -> [(sample_mask, score), ...] + # This stores the Monte Carlo history for each frontier node + potential_cache: dict[int, list[tuple[int, float]]] = {} + + # Statistics tracking + total_steps = 0 + total_evaluations = 0 + total_samples = 0 + + print("\n=== Sequential Monte Carlo Set Extension ===") + print(f"Seed: {seed_idx}, Target: {desired_length}, Sims: {num_simulations}") + + step = 0 + + # Main loop: Grow hyperpixel until target length + while len(hyperpixel_structure) < desired_length: + step += 1 + total_steps += 1 + print( + f"\n--- Step {step}: |S| = {len(hyperpixel_structure)}/{desired_length} ---" + ) + + # Compute expansion frontier + current_frontier_mask = get_frontier(structure_mask, adj_masks, used_mask) + + if not current_frontier_mask: + print("Frontier empty. Stopping.") + break + + frontier_list = mask_to_ids(current_frontier_mask) + print(f"Frontier size: {len(frontier_list)}") + + # Phase 1: Sampling - Monte Carlo exploration from each frontier node + step_start = time.time() + num_evals, num_samps = sampling_phase( + S_mask=structure_mask, + neighbors=frontier_list, + current_frontier_mask=current_frontier_mask, + num_simulations=num_simulations, + desired_length=desired_length, + adj_masks=adj_masks, + predictor=predictor, + input_batch=input_batch, + segments=segments, + target_class_idx=target_class_idx, + batch_size=batch_size, + optimization_sign=optimization_sign, + cache=potential_cache, + used_mask=used_mask, + ) + total_evaluations += num_evals + total_samples += num_samps + sampling_time = time.time() - step_start + + # Phase 2: Selection - Choose best frontier node by potential + potentials = compute_potentials(potential_cache) + winner = select_best_neighbor(potentials) + + if winner == -1: + print("No valid winner found. Stopping.") + break + + winner_stats = potentials[winner] + max_potential = max(winner_stats) if winner_stats else 0 + print( + f"Winner: {winner} (samples: {len(winner_stats)}, max: {max_potential:.4f})" + ) + print(f"Timing: {sampling_time:.2f}s") + + # Commit: Add winner to hyperpixel structure + hyperpixel_structure.append(winner) + structure_mask = add_node(structure_mask, winner) + + # Phase 3: Pruning - Wavefunction collapse + # Discard all histories except the winner's, then redistribute + winner_history = potential_cache[winner] + potential_cache = {} # Aggressive pruning: discard all non-winner histories + + # Recompute frontier for the updated structure + new_frontier_mask = get_frontier(structure_mask, adj_masks, used_mask) + redistribute_history(winner_history, new_frontier_mask, potential_cache) + + recipient_count = len(potential_cache) + print( + f"Pruning: Kept {len(winner_history)} samples, redistributed to {recipient_count} neighbors" + ) + + print("\n=== Extension Complete ===") + + # Post-processing: Find optimal prefix (sometimes full length adds noise) + final_hyperpixel, hyperpixel_score = select_best_prefix( + full_structure=hyperpixel_structure, + predictor=predictor, + input_batch=input_batch, + segments=segments, + target_class_idx=target_class_idx, + batch_size=batch_size, + optimization_sign=optimization_sign, + cache={}, + ) + + # Count prefix evaluations + prefix_evaluations = len(hyperpixel_structure) + total_evaluations += prefix_evaluations + + # Create result dict with stats + final_mask = 0 + for seg_id in final_hyperpixel: + final_mask = add_node(final_mask, seg_id) + + return { + "segments": final_hyperpixel, + "mask": final_mask, + "size": len(final_hyperpixel), + "score": hyperpixel_score, # Actual model delta score + "stats": { + "method": "potential", + "num_simulations": num_simulations, + "num_steps": total_steps, + "total_evaluations": total_evaluations, + "total_samples": total_samples, + "prefix_evaluations": prefix_evaluations, + }, + } + + +def sampling_phase( + S_mask: int, + neighbors: list[int], + current_frontier_mask: int, + num_simulations: int, + desired_length: int, + adj_masks: tuple[int, ...], + predictor, + input_batch, + segments, + target_class_idx: int, + batch_size: int, + optimization_sign: int, + cache: dict[int, list[tuple[int, float]]], + used_mask: int, +) -> tuple[int, int]: + """Monte Carlo Sampling Phase: Explore expansions and populate potential cache. + + For each frontier node n: + 1. Create extended structure S ∪ {n} + 2. Run num_simulations random walk expansions from this extended structure + 3. Evaluate each unique expansion with the model + 4. Distribute results to cache: For each frontier node that appears in an + expansion, record (expansion_mask, score) in that node's history + + This builds a statistical basis for comparing frontier nodes: nodes that + consistently appear in high-scoring expansions will have better potentials. + + Args: + S_mask: Current hyperpixel structure (bitmask) + neighbors: Frontier nodes (list for iteration) + current_frontier_mask: Same frontier as bitmask (for efficient computation) + num_simulations: Monte Carlo samples per frontier node + desired_length: Target expansion size for random walks + adj_masks: Adjacency bitmasks for graph traversal + predictor, input_batch, segments, target_class_idx: For model evaluation + batch_size: Batch size for model inference + optimization_sign: +1 or -1 for score interpretation + cache: Potential cache to populate (modified in-place) + used_mask: Global exclusion mask + + Returns: + Tuple of (num_evaluations, num_samples) + """ + evaluation_queue = [] + mask_to_neighbors_mask = {} # Maps expansion_mask -> which frontier nodes it contains + + all_neighbors_mask = current_frontier_mask + total_samples = 0 + + # --- Sampling Loop: Generate candidate expansions --- + for n in neighbors: + # Start with S ∪ {n} + extended_mask = add_node(S_mask, n) + + # Compute frontier for random walk: + # - Start with current frontier + # - Remove n (now part of structure) + # - Add valid neighbors of n (expand exploration boundary) + n_neighbors_mask = adj_masks[n] + valid_n_neighbors = n_neighbors_mask & ~(used_mask | S_mask | add_node(0, n)) + + base_frontier = remove_node(current_frontier_mask, n) | valid_n_neighbors + + for _ in range(num_simulations): + total_samples += 1 + M = sample_connected_superset( + base_mask=extended_mask, + target_length=desired_length, + adj_masks=adj_masks, + base_frontier=base_frontier, + used_mask=used_mask, + ) + + if M in mask_to_neighbors_mask: + continue + + # Bucketization: Which frontier nodes appear in this expansion? + hits = M & all_neighbors_mask + evaluation_queue.append(M) + mask_to_neighbors_mask[M] = hits + + if not evaluation_queue: + return 0, total_samples + + # --- Batch Evaluation: Score all unique expansions --- + print(f" Evaluating {len(evaluation_queue)} unique samples...") + segment_id_lists = [mask_to_ids(mask) for mask in evaluation_queue] + scores = calculate_hyperpixel_deltas( + predictor=predictor, + input_batch=input_batch, + segments=segments, + hyperpixel_segment_ids_list=segment_id_lists, + target_class_idx=target_class_idx, + batch_size=batch_size, + ) + + # --- Distribution to Cache --- + for mask, score in zip(evaluation_queue, scores): + signed_score = score * optimization_sign + hits = mask_to_neighbors_mask[mask] + + # Distribute to all neighbors in the hit set + for neighbor_id in iter_bits(hits): + if neighbor_id not in cache: + cache[neighbor_id] = [] + cache[neighbor_id].append((mask, signed_score)) + + return len(evaluation_queue), total_samples + + +def select_best_prefix( + full_structure: list[int], + predictor, + input_batch, + segments, + target_class_idx: int, + batch_size: int, + optimization_sign: int, + cache: dict[int, float], +) -> tuple[list[int], float]: + """Find the optimal prefix of a hyperpixel structure. + + Sometimes the fixed desired_length forces the algorithm to add segments + that dilute the signal after the main object region is covered. This + post-processing step evaluates all prefixes [1:k] of the full structure + and returns the one with the best score. + + Args: + full_structure: Complete hyperpixel (list of segment IDs in build order) + predictor, input_batch, segments, target_class_idx: For evaluation + batch_size: Batch size for model inference + optimization_sign: +1 to maximize, -1 to minimize + cache: Simple cache (mask -> score) for reuse + + Returns: + Tuple of (optimal_prefix, raw_score) + """ + if not full_structure: + return [], 0.0 + + prefixes = [] + current_mask = 0 + prefix_masks = [] + + for seg_id in full_structure: + current_mask = add_node(current_mask, seg_id) + prefix_masks.append(current_mask) + prefixes.append(mask_to_ids(current_mask)) + + missing_indices = [] + scores = [None] * len(prefix_masks) + + for i, mask in enumerate(prefix_masks): + if mask in cache: + scores[i] = cache[mask] + else: + missing_indices.append(i) + + if missing_indices: + missing_id_lists = [prefixes[i] for i in missing_indices] + + computed_scores = calculate_hyperpixel_deltas( + predictor=predictor, + input_batch=input_batch, + segments=segments, + hyperpixel_segment_ids_list=missing_id_lists, + target_class_idx=target_class_idx, + batch_size=batch_size, + ) + + for i, score in zip(missing_indices, computed_scores): + signed_score = score * optimization_sign + scores[i] = signed_score + cache[prefix_masks[i]] = signed_score + + best_idx = 0 + max_score = -float("inf") + + for i, score in enumerate(scores): + if score > max_score: + max_score = score + best_idx = i + + optimized_length = best_idx + 1 + if optimized_length < len(full_structure): + print( + f"Optimization: Trimmed from {len(full_structure)} to {optimized_length} segments (Score: {max_score:.4f})" + ) + else: + print( + f"Optimization: Kept full length {len(full_structure)} segments (Score: {max_score:.4f})" + ) + + # Return both the prefix and its score (convert signed score back to raw score) + raw_score = max_score * optimization_sign + return full_structure[: best_idx + 1], raw_score + + +def redistribute_history( + H_winner: list[tuple[int, float]], + new_frontier_mask: int, + cache: dict[int, list[tuple[int, float]]], +): + """Redistribute winner's Monte Carlo history to the new frontier. + + After adding the winning node to the structure, the frontier changes. + The winner's history contains expansions that may include the NEW frontier + nodes. This function distributes those historical samples to the appropriate + frontier nodes' potential caches. + + This inheritance mechanism allows the algorithm to "learn" from past + explorations: if an expansion that included the winner also included + a new frontier node, that information is valuable for evaluating that + frontier node. + + Args: + H_winner: Winner's history [(expansion_mask, score), ...] + new_frontier_mask: Frontier nodes after adding winner to structure + cache: Potential cache to populate (modified in-place) + """ + # Iterate through winner's historical expansions + for M, v in H_winner: + # Which new frontier nodes are in this historical expansion? + hits = M & new_frontier_mask + + if not hits: + continue + + # Distribute this historical (expansion, score) pair to all hit nodes + for neighbor_id in iter_bits(hits): + if neighbor_id not in cache: + cache[neighbor_id] = [] + cache[neighbor_id].append((M, v)) + + +def build_all_hyperpixels_potential( + predictor, + input_batch, + segments, + adj_list, + adj_masks, + target_class_idx, + scores, + max_hyperpixels=10, + desired_length=30, + num_simulations=50, + batch_size=64, +): + """Build multiple hyperpixels using the potential field method. + + Args: + predictor: Model predictor + input_batch: Preprocessed input tensor + segments: Segmentation map + adj_list: Adjacency list + adj_masks: Adjacency bitmasks + target_class_idx: Target class index + scores: Individual segment scores + max_hyperpixels: Maximum number of hyperpixels to build + desired_length: Target segments per hyperpixel + num_simulations: Number of MC simulations per frontier node + batch_size: Batch size for evaluation + + Returns: + List of hyperpixel dictionaries + """ + hyperpixels = [] + processed_segments = set() + + for i in range(max_hyperpixels): + # Find unprocessed segment with highest absolute score + available_segments = [ + seg_id for seg_id in scores.keys() if seg_id not in processed_segments + ] + + if not available_segments: + break + + seed_idx = max(available_segments, key=lambda x: abs(scores[x])) + seed_score = scores[seed_idx] + optimization_sign = 1 if seed_score >= 0 else -1 + + # Build hyperpixel using potential field + result = build_hyperpixel_using_potential( + predictor, + input_batch, + segments, + adj_list, + adj_masks, + target_class_idx, + desired_length, + seed_idx, + num_simulations, + used_segments=processed_segments, + batch_size=batch_size, + optimization_sign=optimization_sign, + ) + + hyperpixel_segments = result["segments"] + + if hyperpixel_segments: + hyperpixels.append( + { + "segments": hyperpixel_segments, + "sign": optimization_sign, + "size": len(hyperpixel_segments), + "hyperpixel_score": result["score"], + "stats": result.get( + "stats", {} + ), # Include potential method statistics + } + ) + processed_segments.update(hyperpixel_segments) + + return hyperpixels From 7b3a5cfcc2a5db6e81224bbc73b93ba14235c669 Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Mon, 23 Feb 2026 17:20:58 +0100 Subject: [PATCH 08/25] refactor: clean up imports and formatting across multiple files --- ciao/__init__.py | 2 +- ciao/algorithm/__init__.py | 2 +- ciao/algorithm/mcgs.py | 40 ++++++++++++++++++------------------- ciao/algorithm/mcts.py | 8 ++++---- ciao/algorithm/potential.py | 4 ++-- ciao/structures/__init__.py | 3 ++- ciao/utils/__init__.py | 3 ++- 7 files changed, 32 insertions(+), 30 deletions(-) diff --git a/ciao/__init__.py b/ciao/__init__.py index b1a78ee..332c2ee 100644 --- a/ciao/__init__.py +++ b/ciao/__init__.py @@ -4,4 +4,4 @@ Mutual Information and greedy feature selection. """ -from ciao.explainer.ciao_explainer import CIAOExplainer \ No newline at end of file +from ciao.explainer.ciao_explainer import CIAOExplainer diff --git a/ciao/algorithm/__init__.py b/ciao/algorithm/__init__.py index ab946c9..4e33cd4 100644 --- a/ciao/algorithm/__init__.py +++ b/ciao/algorithm/__init__.py @@ -1,3 +1,3 @@ """CIAO algorithm implementations.""" -__all__ = [] \ No newline at end of file +__all__ = [] diff --git a/ciao/algorithm/mcgs.py b/ciao/algorithm/mcgs.py index fa32f97..b0bd650 100644 --- a/ciao/algorithm/mcgs.py +++ b/ciao/algorithm/mcgs.py @@ -29,8 +29,8 @@ iter_bits, sample_connected_superset, ) -from ciao.utils.calculations import ModelPredictor from ciao.structures.nodes import MCGSNode +from ciao.utils.calculations import ModelPredictor from ciao.utils.search_utils import evaluate_masks, is_terminal @@ -38,11 +38,11 @@ def select_uct_child( node: MCGSNode, exploration_c: float, virtual_loss: float ) -> tuple[int, MCGSNode] | None: """Select child with highest UCT score using edge statistics (MCGS mode). - + In MCGS, we use edge statistics rather than node statistics to handle DAGs correctly. A child node may have high visits from other parents, which should not influence selection from this parent. - + Returns: (action, child) tuple with best UCT score, or None if no children """ @@ -61,18 +61,16 @@ def select_uct_child( action, {"N": 0, "W": 0.0, "Q": 0.0, "max_reward": -float("inf")} ) pending = node.pending_edges.get(action, 0) - + # Use edge visit count (not child.visits) with virtual loss edge_n = edge_stats["N"] + pending * virtual_loss # Exploitation: Use edge max_reward (not child.max_value) exploit = edge_stats["max_reward"] if edge_stats["N"] > 0 else 0.0 - + # Exploration: Use edge visit count in denominator - explore = exploration_c * math.sqrt( - math.log(parent_visits) / max(1, edge_n) - ) - + explore = exploration_c * math.sqrt(math.log(parent_visits) / max(1, edge_n)) + score = exploit + explore if score > best_score: @@ -236,9 +234,9 @@ def update_rave_stats(node: MCGSNode, action: int, reward: float): def backup_paths( - batch_paths: list[list[MCGSNode]], + batch_paths: list[list[MCGSNode]], batch_actions: list[list[int]], - rewards: list[float] + rewards: list[float], ) -> None: """Backup rewards through all nodes in the paths (standard mode). @@ -253,22 +251,24 @@ def backup_paths( batch_actions: List of action sequences (one per simulation) rewards: List of rewards for each path """ - for path, actions, reward in zip(batch_paths, batch_actions, rewards): + for path, actions, reward in zip(batch_paths, batch_actions, rewards, strict=True): for i, node in enumerate(path): # Update node statistics node.visits += 1 node.value_sum += reward # Mean tracking node.max_value = max(node.max_value, reward) # MAX backup - + # Update edge statistics and release virtual loss if i > 0: # Skip root (no incoming edge) action = actions[i - 1] # Action that led to this node parent = path[i - 1] - + # Release virtual loss on edge if action in parent.pending_edges: - parent.pending_edges[action] = max(0, parent.pending_edges[action] - 1) - + parent.pending_edges[action] = max( + 0, parent.pending_edges[action] - 1 + ) + # Update edge statistics update_edge_stats(parent, action, reward) @@ -303,7 +303,7 @@ def backup_paths_rave( used_mask: Globally excluded segments """ for path, actions, rollout_mask, reward in zip( - batch_paths, batch_actions, batch_masks, rewards + batch_paths, batch_actions, batch_masks, rewards, strict=True ): rollout_segments = set(iter_bits(rollout_mask)) @@ -447,7 +447,7 @@ def build_hyperpixel_mcgs( if action not in node.pending_edges: node.pending_edges[action] = 0 node.pending_edges[action] += 1 - + actions_taken.append(action) node = child @@ -511,7 +511,7 @@ def build_hyperpixel_mcgs( # Evaluate only masks that need GPU gpu_rewards = [] if masks_to_evaluate: - indices, masks = zip(*masks_to_evaluate) + indices, masks = zip(*masks_to_evaluate, strict=True) raw_rewards = evaluate_masks( predictor, input_batch, segments, target_class_idx, list(masks) ) @@ -530,7 +530,7 @@ def build_hyperpixel_mcgs( # Update best score for path_idx, (reward, rollout_mask) in enumerate( - zip(batch_rewards, batch_masks) + zip(batch_rewards, batch_masks, strict=True) ): if reward > best_score: best_score = reward diff --git a/ciao/algorithm/mcts.py b/ciao/algorithm/mcts.py index f6a7ec8..36ffeb1 100644 --- a/ciao/algorithm/mcts.py +++ b/ciao/algorithm/mcts.py @@ -28,8 +28,8 @@ iter_bits, sample_connected_superset, ) -from ciao.utils.calculations import ModelPredictor from ciao.structures.nodes import MCTSNode +from ciao.utils.calculations import ModelPredictor from ciao.utils.search_utils import evaluate_masks, is_terminal @@ -218,7 +218,7 @@ def backup_paths(batch_paths: list[list[MCTSNode]], rewards: list[float]) -> Non - max_value (MAX backup for selection) - pending (release virtual loss) """ - for path, reward in zip(batch_paths, rewards): + for path, reward in zip(batch_paths, rewards, strict=True): for node in path: node.pending -= 1 # Release virtual loss node.visits += 1 @@ -252,7 +252,7 @@ def backup_paths_rave( rewards: List of rewards for each rollout global_stats: Optional global RAVE statistics """ - for path, rollout_mask, reward in zip(batch_paths, batch_rollout_masks, rewards): + for path, rollout_mask, reward in zip(batch_paths, batch_rollout_masks, rewards, strict=True): # --- GLOBAL RAVE BACKUP --- if global_stats is not None: global_stats.update(rollout_mask, reward) @@ -435,7 +435,7 @@ def build_hyperpixel_mcts( # Evaluate only masks that need GPU gpu_rewards = [] if masks_to_evaluate: - indices, masks = zip(*masks_to_evaluate) + indices, masks = zip(*masks_to_evaluate, strict=True) raw_rewards = evaluate_masks( predictor, input_batch, segments, target_class_idx, list(masks) ) diff --git a/ciao/algorithm/potential.py b/ciao/algorithm/potential.py index 2f7a454..0e62ac1 100644 --- a/ciao/algorithm/potential.py +++ b/ciao/algorithm/potential.py @@ -324,7 +324,7 @@ def sampling_phase( ) # --- Distribution to Cache --- - for mask, score in zip(evaluation_queue, scores): + for mask, score in zip(evaluation_queue, scores, strict=True): signed_score = score * optimization_sign hits = mask_to_neighbors_mask[mask] @@ -397,7 +397,7 @@ def select_best_prefix( batch_size=batch_size, ) - for i, score in zip(missing_indices, computed_scores): + for i, score in zip(missing_indices, computed_scores, strict=True): signed_score = score * optimization_sign scores[i] = signed_score cache[prefix_masks[i]] = signed_score diff --git a/ciao/structures/__init__.py b/ciao/structures/__init__.py index 99a1662..bb73e5c 100644 --- a/ciao/structures/__init__.py +++ b/ciao/structures/__init__.py @@ -2,4 +2,5 @@ from ciao.structures.nodes import MCGSNode, MCTSNode -__all__ = ["MCTSNode", "MCGSNode"] \ No newline at end of file + +__all__ = ["MCGSNode", "MCTSNode"] diff --git a/ciao/utils/__init__.py b/ciao/utils/__init__.py index 5065a75..99e3787 100644 --- a/ciao/utils/__init__.py +++ b/ciao/utils/__init__.py @@ -4,7 +4,8 @@ from ciao.utils.calculations import ModelPredictor from ciao.utils.segmentation import create_segmentation + __all__ = [ "ModelPredictor", "create_segmentation", -] \ No newline at end of file +] From 7dc9f783d46930e2bb948ef5f22de9675e277341 Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Tue, 24 Feb 2026 10:36:32 +0100 Subject: [PATCH 09/25] refactor: improve docstrings and variable naming for clarity (ruff check) --- ciao/__init__.py | 4 ++-- ciao/algorithm/mcgs.py | 12 ++++++------ ciao/algorithm/mcts.py | 17 ++++++++--------- ciao/algorithm/potential.py | 26 ++++++++++++++++---------- ciao/structures/bitmask_graph.py | 5 +++-- ciao/utils/calculations.py | 17 +++++++++-------- ciao/utils/segmentation.py | 15 +++++++++------ 7 files changed, 53 insertions(+), 43 deletions(-) diff --git a/ciao/__init__.py b/ciao/__init__.py index 332c2ee..6ea1c58 100644 --- a/ciao/__init__.py +++ b/ciao/__init__.py @@ -1,7 +1,7 @@ -"""CIAO: Contextual Importance Assessment via Obfuscation +"""CIAO: Contextual Importance Assessment via Obfuscation. An explainable AI (XAI) method for identifying influential image regions using Mutual Information and greedy feature selection. """ -from ciao.explainer.ciao_explainer import CIAOExplainer +# from ciao.explainer.ciao_explainer import CIAOExplainer diff --git a/ciao/algorithm/mcgs.py b/ciao/algorithm/mcgs.py index b0bd650..cf0cdb8 100644 --- a/ciao/algorithm/mcgs.py +++ b/ciao/algorithm/mcgs.py @@ -1,4 +1,4 @@ -"""Unified Monte Carlo Graph Search (MCGS) Implementation +"""Unified Monte Carlo Graph Search (MCGS) Implementation. This module provides a unified MCGS implementation with multiple modes: - mode='standard': Standard MCGS with eager expansion and optimized node creation @@ -402,7 +402,7 @@ def build_hyperpixel_mcgs( # --- MAIN MCGS LOOP --- mode_label = f"MCGS-{mode.upper()}" - for iteration in tqdm(range(num_iterations), desc=f" {mode_label}", ncols=80): + for _iteration in tqdm(range(num_iterations), desc=f" {mode_label}", ncols=80): # --- PHASE 1: BATCH COLLECTION --- batch_paths = [] batch_masks = [] @@ -511,7 +511,7 @@ def build_hyperpixel_mcgs( # Evaluate only masks that need GPU gpu_rewards = [] if masks_to_evaluate: - indices, masks = zip(*masks_to_evaluate, strict=True) + _indices, masks = zip(*masks_to_evaluate, strict=True) raw_rewards = evaluate_masks( predictor, input_batch, segments, target_class_idx, list(masks) ) @@ -529,7 +529,7 @@ def build_hyperpixel_mcgs( gpu_idx += 1 # Update best score - for path_idx, (reward, rollout_mask) in enumerate( + for _path_idx, (reward, rollout_mask) in enumerate( zip(batch_rewards, batch_masks, strict=True) ): if reward > best_score: @@ -633,9 +633,9 @@ def build_all_hyperpixels_mcgs( processed_segments = set() used_mask = 0 - for i in range(max_hyperpixels): + for _ in range(max_hyperpixels): available_segments = [ - seg_id for seg_id in scores.keys() if seg_id not in processed_segments + seg_id for seg_id in scores if seg_id not in processed_segments ] if not available_segments: diff --git a/ciao/algorithm/mcts.py b/ciao/algorithm/mcts.py index 36ffeb1..d2b9897 100644 --- a/ciao/algorithm/mcts.py +++ b/ciao/algorithm/mcts.py @@ -1,4 +1,4 @@ -"""Unified Monte Carlo Tree Search for Connected Image Segments +"""Unified Monte Carlo Tree Search for Connected Image Segments. This module provides two MCTS variants controlled by the 'mode' parameter: 1. 'standard': Standard MCTS with UCT selection and random rollouts @@ -67,10 +67,7 @@ def is_fully_expanded(node: MCTSNode, adj_masks: tuple, used_mask: int) -> bool: """Check if all frontier segments have been expanded as children.""" frontier = get_frontier(node.mask, adj_masks, used_mask) - for seg_id in iter_bits(frontier): - if seg_id not in node.children: - return False - return True + return all(seg_id in node.children for seg_id in iter_bits(frontier)) # ============================================================================ @@ -252,7 +249,9 @@ def backup_paths_rave( rewards: List of rewards for each rollout global_stats: Optional global RAVE statistics """ - for path, rollout_mask, reward in zip(batch_paths, batch_rollout_masks, rewards, strict=True): + for path, rollout_mask, reward in zip( + batch_paths, batch_rollout_masks, rewards, strict=True + ): # --- GLOBAL RAVE BACKUP --- if global_stats is not None: global_stats.update(rollout_mask, reward) @@ -435,7 +434,7 @@ def build_hyperpixel_mcts( # Evaluate only masks that need GPU gpu_rewards = [] if masks_to_evaluate: - indices, masks = zip(*masks_to_evaluate, strict=True) + _indices, masks = zip(*masks_to_evaluate, strict=True) raw_rewards = evaluate_masks( predictor, input_batch, segments, target_class_idx, list(masks) ) @@ -556,9 +555,9 @@ def build_all_hyperpixels_mcts( processed_segments = set() used_mask = 0 - for i in range(max_hyperpixels): + for _ in range(max_hyperpixels): available_segments = [ - seg_id for seg_id in scores.keys() if seg_id not in processed_segments + seg_id for seg_id in scores if seg_id not in processed_segments ] if not available_segments: diff --git a/ciao/algorithm/potential.py b/ciao/algorithm/potential.py index 0e62ac1..39b22db 100644 --- a/ciao/algorithm/potential.py +++ b/ciao/algorithm/potential.py @@ -258,14 +258,17 @@ def sampling_phase( S_mask: Current hyperpixel structure (bitmask) neighbors: Frontier nodes (list for iteration) current_frontier_mask: Same frontier as bitmask (for efficient computation) - num_simulations: Monte Carlo samples per frontier node - desired_length: Target expansion size for random walks - adj_masks: Adjacency bitmasks for graph traversal - predictor, input_batch, segments, target_class_idx: For model evaluation + num_simulations: Number of random walk simulations per frontier node + desired_length: Target hyperpixel size + adj_masks: Adjacency masks for graph structure + predictor: Model predictor for evaluation + input_batch: Input tensor batch + segments: Pixel-to-segment mapping array + target_class_idx: Target class index for prediction batch_size: Batch size for model inference - optimization_sign: +1 or -1 for score interpretation - cache: Potential cache to populate (modified in-place) - used_mask: Global exclusion mask + optimization_sign: +1 to maximize, -1 to minimize + cache: Potential cache mapping node -> list of (mask, score) pairs + used_mask: Bitmask of already-used nodes Returns: Tuple of (num_evaluations, num_samples) @@ -356,7 +359,10 @@ def select_best_prefix( Args: full_structure: Complete hyperpixel (list of segment IDs in build order) - predictor, input_batch, segments, target_class_idx: For evaluation + predictor: Model predictor for evaluation + input_batch: Input tensor batch + segments: Pixel-to-segment mapping array + target_class_idx: Target class index for prediction batch_size: Batch size for model inference optimization_sign: +1 to maximize, -1 to minimize cache: Simple cache (mask -> score) for reuse @@ -496,10 +502,10 @@ def build_all_hyperpixels_potential( hyperpixels = [] processed_segments = set() - for i in range(max_hyperpixels): + for _ in range(max_hyperpixels): # Find unprocessed segment with highest absolute score available_segments = [ - seg_id for seg_id in scores.keys() if seg_id not in processed_segments + seg_id for seg_id in scores if seg_id not in processed_segments ] if not available_segments: diff --git a/ciao/structures/bitmask_graph.py b/ciao/structures/bitmask_graph.py index e241e89..32a6f64 100644 --- a/ciao/structures/bitmask_graph.py +++ b/ciao/structures/bitmask_graph.py @@ -48,8 +48,9 @@ def remove_node(mask: int, node: int) -> int: def pick_random_set_bit(mask: int) -> int: - """Select a random set bit from the mask in O(N) where N is the index of the bit, - without allocating a list. Efficient for sparse masks. + """Select a random set bit from the mask in O(N) where N is the index of the bit. + + Without allocating a list. Efficient for sparse masks. """ count = mask.bit_count() if count == 0: diff --git a/ciao/utils/calculations.py b/ciao/utils/calculations.py index 40bae0c..cad7f98 100644 --- a/ciao/utils/calculations.py +++ b/ciao/utils/calculations.py @@ -5,7 +5,7 @@ class ModelPredictor: - """Handles model predictions and class information""" + """Handles model predictions and class information.""" def __init__(self, model, class_names: list[str]): self.model = model @@ -22,7 +22,7 @@ def __init__(self, model, class_names: list[str]): ) def get_predictions(self, input_batch: torch.Tensor) -> torch.Tensor: - """Get model predictions (returns probabilities)""" + """Get model predictions (returns probabilities).""" with torch.no_grad(): outputs = self.model(input_batch) probabilities = torch.nn.functional.softmax(outputs, dim=1) @@ -31,7 +31,7 @@ def get_predictions(self, input_batch: torch.Tensor) -> torch.Tensor: def predict_image( self, input_batch: torch.Tensor, top_k: int = 5 ) -> list[tuple[int, str, float]]: - """Get top-k predictions for an image""" + """Get top-k predictions for an image.""" probabilities = self.get_predictions(input_batch) top_probs, top_indices = torch.topk(probabilities[0], top_k) @@ -48,7 +48,7 @@ def predict_image( return results def calculate_image_mean_color(self, input_tensor: torch.Tensor) -> torch.Tensor: - """Calculate image mean color using pre-computed constants""" + """Calculate image mean color using pre-computed constants.""" # Add batch dimension if needed if input_tensor.dim() == 3: input_tensor = input_tensor.unsqueeze(0) @@ -156,7 +156,7 @@ def plot_image_mean_color(self, input_tensor): def get_class_logit_batch( self, input_batch: torch.Tensor, target_class_idx: int ) -> torch.Tensor: - """Get logits for a batch of images - optimized for batched inference (directly from model outputs)""" + """Get logits for a batch of images - optimized for batched inference (directly from model outputs).""" with torch.no_grad(): outputs = self.model(input_batch) # Get raw logits @@ -217,7 +217,7 @@ def create_surrogate_dataset( local_groups = [] for segment_id in segment_ids: # Get neighbors within specified distance using BFS - neighbors = set([segment_id]) + neighbors = {segment_id} current_layer = {segment_id} for _ in range(neighborhood_distance): @@ -287,7 +287,7 @@ def calculate_scores_from_surrogate(X: np.ndarray, y: np.ndarray) -> dict[int, f def get_predicted_class(predictor: ModelPredictor, input_batch: torch.Tensor) -> int: - """Get the predicted class index from model output""" + """Get the predicted class index from model output.""" predictions = predictor.predict_image(input_batch, top_k=1) return predictions[0][0] @@ -301,6 +301,7 @@ def calculate_hyperpixel_deltas( batch_size: int = 64, ) -> list[float]: """Calculate masking deltas for hyperpixel candidates using batched inference. + Handles internal batching to prevent memory overflow with large path counts. Args: @@ -379,7 +380,7 @@ def calculate_hyperpixel_deltas( def select_top_hyperpixels( hyperpixels: list[dict], max_hyperpixels: int = 10 ) -> list[dict]: - """Select top hyperpixels by their primary algorithm-specific score""" + """Select top hyperpixels by their primary algorithm-specific score.""" # Use hyperpixel_score return sorted( hyperpixels, diff --git a/ciao/utils/segmentation.py b/ciao/utils/segmentation.py index 39fbe03..26bc871 100644 --- a/ciao/utils/segmentation.py +++ b/ciao/utils/segmentation.py @@ -8,7 +8,8 @@ def hex_round(q, r): """Round axial coordinates to nearest hex. Args: - q, r: Fractional axial coordinates + q: Fractional axial q coordinate + r: Fractional axial r coordinate Returns: (q, r): Integer axial coordinates of nearest hex @@ -39,7 +40,8 @@ def pixel_to_hex(px, py, size): """Convert pixel coordinate to axial hex coordinates. Args: - px, py: Pixel coordinates + px: Pixel x coordinate + py: Pixel y coordinate size: Hex size (distance from center to flat edge for flat-top hexagons) Returns: @@ -158,7 +160,7 @@ def build_fast_adjacency_list(hex_to_id, max_id): def create_hexagonal_grid_with_list(input_tensor, hex_radius=14): - channels, height, width = input_tensor.shape + _channels, height, width = input_tensor.shape segments = np.zeros((height, width), dtype=np.int32) hex_to_id = {} @@ -185,6 +187,7 @@ def create_hexagonal_grid_with_list(input_tensor, hex_radius=14): def build_adjacency_bitmasks(adj_list): """Převede adjacency list na seznam integerů. + adj_masks[5] bude integer, který má jedničky na pozicích sousedů hexu 5. """ adj_masks = [] @@ -197,8 +200,8 @@ def build_adjacency_bitmasks(adj_list): def create_square_grid(input_tensor, square_size=14, neighborhood=8): - """Create a grid of squares with graph structure representing spatial relationships""" - channels, height, width = input_tensor.shape + """Create a grid of squares with graph structure representing spatial relationships.""" + _channels, height, width = input_tensor.shape segments = np.zeros((height, width), dtype=np.int32) segment_id = 0 @@ -235,7 +238,7 @@ def create_hexagonal_grid(input_tensor, hex_radius=14, neighborhood=6): segments: 2D array mapping pixels to segment IDs adjacency_graph: NetworkX graph of segment relationships """ - channels, height, width = input_tensor.shape + _channels, height, width = input_tensor.shape segments = np.zeros((height, width), dtype=np.int32) # Map axial coordinates (q, r) to unique segment IDs From 0f88d45a1d177d79a5b1dbba431d468f282b326b Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Tue, 24 Feb 2026 11:11:27 +0100 Subject: [PATCH 10/25] refactor: address agents' comments Improve error handling and remove legacy parameters in algorithm functions. --- ciao/algorithm/lookahead_bitset.py | 2 +- ciao/algorithm/mcgs.py | 32 ++++++++++++---------------- ciao/algorithm/mcts.py | 10 +++++---- ciao/algorithm/potential.py | 12 ++++------- ciao/structures/bitmask_graph.py | 2 +- ciao/utils/calculations.py | 27 +++++++++++++----------- ciao/utils/search_utils.py | 7 ++++++ ciao/utils/segmentation.py | 34 +++++++++++++++--------------- 8 files changed, 64 insertions(+), 62 deletions(-) diff --git a/ciao/algorithm/lookahead_bitset.py b/ciao/algorithm/lookahead_bitset.py index e3ecdf4..7293e87 100644 --- a/ciao/algorithm/lookahead_bitset.py +++ b/ciao/algorithm/lookahead_bitset.py @@ -210,7 +210,7 @@ def _find_first_step(base_mask: int, target_mask: int) -> int: # Return the first bit in the difference for seg_id in iter_bits(diff): return seg_id - return -1 # Shouldn't happen + raise ValueError("Could not find first step between base and target mask.") def _evaluate_prefixes( diff --git a/ciao/algorithm/mcgs.py b/ciao/algorithm/mcgs.py index cf0cdb8..3fe5d2f 100644 --- a/ciao/algorithm/mcgs.py +++ b/ciao/algorithm/mcgs.py @@ -429,26 +429,22 @@ def build_hyperpixel_mcgs( uct_result = select_uct_child_rave( node, exploration_c, virtual_loss, rave_k ) - assert uct_result is not None - action, child = uct_result - - # Apply virtual loss to edge - if action not in node.pending_edges: - node.pending_edges[action] = 0 - node.pending_edges[action] += 1 - - actions_taken.append(action) else: uct_result = select_uct_child(node, exploration_c, virtual_loss) - assert uct_result is not None - action, child = uct_result - # Apply virtual loss to edge - if action not in node.pending_edges: - node.pending_edges[action] = 0 - node.pending_edges[action] += 1 + if uct_result is None: + raise RuntimeError( + "Selection failed to find a child, but node is fully expanded." + ) + + action, child = uct_result + + # Apply virtual loss to edge + if action not in node.pending_edges: + node.pending_edges[action] = 0 + node.pending_edges[action] += 1 - actions_taken.append(action) + actions_taken.append(action) node = child path.append(node) @@ -665,9 +661,7 @@ def build_all_hyperpixels_mcgs( hyperpixel_mask = result["mask"] used_mask = result["used_mask"] - hyperpixel_segments = [ - seg_id for seg_id in range(next_id) if hyperpixel_mask & (1 << seg_id) - ] + hyperpixel_segments = list(iter_bits(hyperpixel_mask)) if hyperpixel_segments: hyperpixels.append( diff --git a/ciao/algorithm/mcts.py b/ciao/algorithm/mcts.py index d2b9897..7fa90ff 100644 --- a/ciao/algorithm/mcts.py +++ b/ciao/algorithm/mcts.py @@ -373,7 +373,11 @@ def build_hyperpixel_mcts( else: child = select_uct_child(node, exploration_c, virtual_loss) - assert child is not None + if child is None: + raise RuntimeError( + "Selection failed to find a child, but node is fully expanded." + ) + child.pending += 1 node = child path.append(node) @@ -587,9 +591,7 @@ def build_all_hyperpixels_mcts( hyperpixel_mask = result["mask"] used_mask = result["used_mask"] - hyperpixel_segments = [ - seg_id for seg_id in range(next_id) if hyperpixel_mask & (1 << seg_id) - ] + hyperpixel_segments = list(iter_bits(hyperpixel_mask)) if hyperpixel_segments: hyperpixels.append( diff --git a/ciao/algorithm/potential.py b/ciao/algorithm/potential.py index 39b22db..da738ea 100644 --- a/ciao/algorithm/potential.py +++ b/ciao/algorithm/potential.py @@ -49,7 +49,6 @@ def build_hyperpixel_using_potential( predictor, input_batch, segments, - adj_list: tuple[tuple[int, ...], ...], adj_masks: tuple[int, ...], target_class_idx: int, desired_length: int, @@ -74,7 +73,6 @@ def build_hyperpixel_using_potential( predictor: Model predictor for scoring hyperpixels input_batch: Preprocessed input image tensor [1, C, H, W] segments: Segmentation map [H, W] (pixel -> segment ID) - adj_list: Adjacency list (tuple of tuples, legacy parameter for compatibility) adj_masks: Adjacency bitmasks (adj_masks[i] = neighbors of segment i) target_class_idx: Class to optimize for desired_length: Maximum hyperpixel size before prefix optimization @@ -226,8 +224,9 @@ def build_hyperpixel_using_potential( } +# ruff: noqa: RUF002 def sampling_phase( - S_mask: int, + S_mask: int, # noqa: N803 neighbors: list[int], current_frontier_mask: int, num_simulations: int, @@ -281,7 +280,7 @@ def sampling_phase( # --- Sampling Loop: Generate candidate expansions --- for n in neighbors: - # Start with S ∪ {n} + # Start with S ∪ {n} # noqa: RUF003 extended_mask = add_node(S_mask, n) # Compute frontier for random walk: @@ -432,7 +431,7 @@ def select_best_prefix( def redistribute_history( - H_winner: list[tuple[int, float]], + H_winner: list[tuple[int, float]], # noqa: N803 new_frontier_mask: int, cache: dict[int, list[tuple[int, float]]], ): @@ -472,7 +471,6 @@ def build_all_hyperpixels_potential( predictor, input_batch, segments, - adj_list, adj_masks, target_class_idx, scores, @@ -487,7 +485,6 @@ def build_all_hyperpixels_potential( predictor: Model predictor input_batch: Preprocessed input tensor segments: Segmentation map - adj_list: Adjacency list adj_masks: Adjacency bitmasks target_class_idx: Target class index scores: Individual segment scores @@ -520,7 +517,6 @@ def build_all_hyperpixels_potential( predictor, input_batch, segments, - adj_list, adj_masks, target_class_idx, desired_length, diff --git a/ciao/structures/bitmask_graph.py b/ciao/structures/bitmask_graph.py index 32a6f64..4383919 100644 --- a/ciao/structures/bitmask_graph.py +++ b/ciao/structures/bitmask_graph.py @@ -49,7 +49,7 @@ def remove_node(mask: int, node: int) -> int: def pick_random_set_bit(mask: int) -> int: """Select a random set bit from the mask in O(N) where N is the index of the bit. - + Without allocating a list. Efficient for sparse masks. """ count = mask.bit_count() diff --git a/ciao/utils/calculations.py b/ciao/utils/calculations.py index cad7f98..0b153f0 100644 --- a/ciao/utils/calculations.py +++ b/ciao/utils/calculations.py @@ -64,26 +64,29 @@ def get_replacement_image( """Generate replacement image for masking operations. Args: - input_tensor: Input tensor [3, 224, 224] (ImageNet normalized) + input_tensor: Input tensor [3, H, W] (ImageNet normalized) replacement: Strategy - "mean_color", "interlacing", "blur", or "solid_color" **kwargs: Additional options: - color: For solid_color mode, RGB tuple (0-255). Defaults to black (0, 0, 0) Returns: - replacement_image: torch tensor [3, 224, 224] on same device + replacement_image: torch tensor [3, H, W] on same device """ # Ensure tensor is on correct device input_tensor = input_tensor.to(self.device) + # Extract spatial dimensions from input tensor + _, height, width = input_tensor.shape + if replacement == "mean_color": # Fill entire image with mean color mean_color = self.calculate_image_mean_color(input_tensor) # [3, 1, 1] - replacement_image = mean_color.expand(-1, 224, 224) # [3, 224, 224] + replacement_image = mean_color.expand(-1, height, width) # [3, H, W] elif replacement == "interlacing": # Create interlaced pattern: even columns flipped vertically, then even indices flipped horizontally replacement_image = input_tensor.clone() - even_indices = torch.arange(0, 224, 2) # [0, 2, 4, ..., 222] + even_indices = torch.arange(0, height, 2) # Even row indices # Step 1: Flip even columns vertically (upside down) replacement_image[:, :, even_indices] = torch.flip( @@ -115,7 +118,7 @@ def get_replacement_image( kernel = gaussian_2d.expand(3, 1, kernel_size, kernel_size) # Apply blur with padding to maintain image size - input_batch = input_tensor.unsqueeze(0) # [1, 3, 224, 224] + input_batch = input_tensor.unsqueeze(0) # [1, 3, H, W] padding = kernel_size // 2 replacement_image = F.conv2d( @@ -123,7 +126,7 @@ def get_replacement_image( kernel, padding=padding, groups=3, # Apply same kernel to each channel independently - ).squeeze(0) # [3, 224, 224] + ).squeeze(0) # [3, H, W] elif replacement == "solid_color": # Fill with specified solid color (expects RGB values in 0-255 range) @@ -141,7 +144,7 @@ def get_replacement_image( mean = self.imagenet_mean.squeeze(0) # [3, 1, 1] std = self.imagenet_std.squeeze(0) # [3, 1, 1] normalized_color = (color - mean) / std - replacement_image = normalized_color.expand(-1, 224, 224) # [3, 224, 224] + replacement_image = normalized_color.expand(-1, height, width) # [3, H, W] else: raise ValueError(f"Unknown replacement strategy: {replacement}") @@ -256,7 +259,7 @@ def create_surrogate_dataset( return X, y -def calculate_scores_from_surrogate(X: np.ndarray, y: np.ndarray) -> dict[int, float]: +def calculate_scores_from_surrogate(X: np.ndarray, y: np.ndarray) -> dict[int, float]: # noqa: N803 """Calculate averaged segment importance scores from surrogate dataset. For each segment, averages all delta scores where that segment was masked. @@ -301,7 +304,7 @@ def calculate_hyperpixel_deltas( batch_size: int = 64, ) -> list[float]: """Calculate masking deltas for hyperpixel candidates using batched inference. - + Handles internal batching to prevent memory overflow with large path counts. Args: @@ -332,6 +335,9 @@ def calculate_hyperpixel_deltas( assert predictor.replacement_image is not None replacement_image = predictor.replacement_image + # Convert segments numpy array to GPU tensor once (outside loop) + gpu_segments = torch.from_numpy(segments).to(predictor.device) + # Process in batches to avoid memory overflow all_deltas = [] num_masks = len(hyperpixel_segment_ids_list) @@ -342,9 +348,6 @@ def calculate_hyperpixel_deltas( batch_inputs = input_batch.repeat(current_batch_size, 1, 1, 1) - # Convert segments numpy array to GPU tensor once - gpu_segments = torch.from_numpy(segments).to(predictor.device) - for i, segment_ids in enumerate( hyperpixel_segment_ids_list[batch_start:batch_end] ): diff --git a/ciao/utils/search_utils.py b/ciao/utils/search_utils.py index 81e26fc..225783e 100644 --- a/ciao/utils/search_utils.py +++ b/ciao/utils/search_utils.py @@ -26,6 +26,13 @@ def evaluate_masks( masks: list[int], ) -> list[float]: """Evaluate multiple segment masks by computing class score deltas (batched).""" + # Guard against zero masks which would produce empty segment lists + if any(mask == 0 for mask in masks): + raise ValueError( + "Cannot evaluate zero mask: A mask with value 0 contains no segments. " + "Ensure all masks have at least one bit set." + ) + all_segment_ids = [list(iter_bits(mask)) for mask in masks] rewards = calculate_hyperpixel_deltas( diff --git a/ciao/utils/segmentation.py b/ciao/utils/segmentation.py index 26bc871..ce95f6b 100644 --- a/ciao/utils/segmentation.py +++ b/ciao/utils/segmentation.py @@ -123,40 +123,40 @@ def build_adjacency_graph(segments, neighborhood=8): def build_fast_adjacency_list(hex_to_id, max_id): - """Vytvoří 'static adjacency list' optimalizovaný pro rychlé čtení. + """Create a static adjacency list optimized for fast reading. Args: - hex_to_id: Dict mapující (q, r) -> int_id (0 až N-1) - max_id: Celkový počet segmentů (N) + hex_to_id: Dict mapping (q, r) -> int_id (0 to N-1) + max_id: Total number of segments (N) Returns: adj_list: Tuple of Tuples. - adj_list[5] vrátí např. (4, 6, 12) - sousedy segmentu 5. + adj_list[5] returns e.g. (4, 6, 12) - neighbors of segment 5. """ - # Inicializujeme prázdné listy pro každé ID - # Používáme list listů pro konstrukci + # Initialize empty lists for each ID + # Use list of lists for construction temp_adj = [[] for _ in range(max_id)] - # Offsets pro sousedy (axial coords) + # Offsets for neighbors (axial coords) hex_neighbors = [(+1, 0), (+1, -1), (0, -1), (-1, 0), (-1, +1), (0, +1)] for (q, r), seg_id in hex_to_id.items(): for dq, dr in hex_neighbors: neighbor_key = (q + dq, r + dr) - # Pokud soused existuje (je uvnitř obrázku) + # If neighbor exists (is within the image) if neighbor_key in hex_to_id: neighbor_id = hex_to_id[neighbor_key] temp_adj[seg_id].append(neighbor_id) - # Konverze na tuple of tuples pro maximální rychlost čtení a paměťovou efektivitu - # Seřadíme sousedy (volitelné, ale dobré pro determinismus) + # Convert to tuple of tuples for maximum read speed and memory efficiency + # Sort neighbors (optional, but good for determinism) final_adj = tuple(tuple(sorted(neighbors)) for neighbors in temp_adj) return final_adj -# --- Upravená funkce create_hexagonal_grid --- +# --- Modified create_hexagonal_grid function --- def create_hexagonal_grid_with_list(input_tensor, hex_radius=14): @@ -166,8 +166,8 @@ def create_hexagonal_grid_with_list(input_tensor, hex_radius=14): hex_to_id = {} next_id = 0 - # 1. Mapování pixelů na Hex ID - # (Tohle je nejpomalejší část, ale běží jen jednou při initu) + # 1. Map pixels to Hex ID + # (This is the slowest part, but runs only once during initialization) for y in range(height): for x in range(width): q, r = pixel_to_hex(x, y, hex_radius) @@ -179,16 +179,16 @@ def create_hexagonal_grid_with_list(input_tensor, hex_radius=14): segments[y, x] = hex_to_id[key] - # 2. Vytvoření Rychlého Grafu (žádný NetworkX) + # 2. Create fast graph (no NetworkX) adjacency_list = build_fast_adjacency_list(hex_to_id, next_id) return segments, adjacency_list, next_id def build_adjacency_bitmasks(adj_list): - """Převede adjacency list na seznam integerů. - - adj_masks[5] bude integer, který má jedničky na pozicích sousedů hexu 5. + """Convert adjacency list to a list of integers. + + adj_masks[5] will be an integer with bits set at positions of hex 5's neighbors. """ adj_masks = [] for neighbors in adj_list: From 96eb26c5c0863de457ce0eced5792821d21b0542 Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Wed, 25 Feb 2026 07:54:36 +0100 Subject: [PATCH 11/25] refactor: mypy and ruff pain --- ciao/algorithm/lookahead_bitset.py | 54 ++++++++++++----------- ciao/algorithm/mcgs.py | 65 ++++++++++++++-------------- ciao/algorithm/mcts.py | 63 +++++++++++++-------------- ciao/algorithm/potential.py | 69 ++++++++++++++++-------------- ciao/structures/bitmask_graph.py | 3 +- ciao/structures/nodes.py | 6 +-- ciao/utils/calculations.py | 36 ++++++++-------- ciao/utils/search_utils.py | 4 +- ciao/utils/segmentation.py | 36 ++++++++++------ 9 files changed, 179 insertions(+), 157 deletions(-) diff --git a/ciao/algorithm/lookahead_bitset.py b/ciao/algorithm/lookahead_bitset.py index 7293e87..e2bcb39 100644 --- a/ciao/algorithm/lookahead_bitset.py +++ b/ciao/algorithm/lookahead_bitset.py @@ -1,7 +1,5 @@ -"""Greedy lookahead hyperpixel building with bitmask operations. - -Rolling horizon strategy: Look ahead multiple steps but only commit one step at a time. -""" +import numpy as np +import torch from ciao.structures.bitmask_graph import ( add_node, @@ -9,23 +7,29 @@ iter_bits, mask_to_ids, ) -from ciao.utils.calculations import calculate_hyperpixel_deltas +from ciao.utils.calculations import ModelPredictor, calculate_hyperpixel_deltas + + +"""Greedy lookahead hyperpixel building with bitmask operations. + +Rolling horizon strategy: Look ahead multiple steps but only commit one step at a time. +""" def build_hyperpixel_greedy_lookahead( - predictor, - input_batch, - segments, - adj_masks: tuple, + predictor: ModelPredictor, + input_batch: torch.Tensor, + segments: np.ndarray, + adj_masks: tuple[int, ...], target_class_idx: int, - scores: dict, + scores: dict[int, float], seed_idx: int, desired_length: int, lookahead_distance: int, optimization_sign: int, used_mask: int, batch_size: int = 64, -) -> dict: +) -> dict[str, object]: """Build a single hyperpixel using greedy lookahead with rolling horizon. Strategy: Look ahead up to lookahead_distance steps, evaluate all candidates, @@ -147,7 +151,7 @@ def build_hyperpixel_greedy_lookahead( def _generate_lookahead_candidates( current_mask: int, - adj_masks: tuple, + adj_masks: tuple[int, ...], used_mask: int, lookahead_distance: int, max_total_size: int, @@ -157,7 +161,7 @@ def _generate_lookahead_candidates( Returns: Dict mapping candidate_mask -> first_step_segment """ - candidates = {} # mask -> first_step + candidates: dict[int, int] = {} # mask -> first_step # BFS: Track masks at each depth current_depth_masks = {current_mask} @@ -215,9 +219,9 @@ def _find_first_step(base_mask: int, target_mask: int) -> int: def _evaluate_prefixes( path: list[int], - predictor, - input_batch, - segments, + predictor: ModelPredictor, + input_batch: torch.Tensor, + segments: np.ndarray, target_class_idx: int, optimization_sign: int, batch_size: int, @@ -252,17 +256,17 @@ def _evaluate_prefixes( def build_all_hyperpixels_greedy_lookahead( - predictor, - input_batch, - segments, - adj_masks: tuple, + predictor: ModelPredictor, + input_batch: torch.Tensor, + segments: np.ndarray, + adj_masks: tuple[int, ...], target_class_idx: int, - scores: dict, + scores: dict[int, float], max_hyperpixels: int, desired_length: int, lookahead_distance: int, batch_size: int = 64, -) -> list[dict]: +) -> list[dict[str, object]]: """Build multiple hyperpixels using greedy lookahead. Returns: @@ -303,7 +307,7 @@ def build_all_hyperpixels_greedy_lookahead( ) # Update used_mask - used_mask = result["mask"] | used_mask + used_mask = result["mask"] | used_mask # type: ignore[operator] # Format for compatibility hyperpixel = { @@ -316,9 +320,9 @@ def build_all_hyperpixels_greedy_lookahead( hyperpixels.append(hyperpixel) print( - f"Built hyperpixel with {len(result['segments'])} segments, score={result['score']:.4f}" + f"Built hyperpixel with {len(result['segments'])} segments, score={result['score']:.4f}" # type: ignore[arg-type] ) # Sort by absolute score - hyperpixels.sort(key=lambda x: abs(x["hyperpixel_score"]), reverse=True) + hyperpixels.sort(key=lambda x: abs(x["hyperpixel_score"]), reverse=True) # type: ignore[arg-type] return hyperpixels diff --git a/ciao/algorithm/mcgs.py b/ciao/algorithm/mcgs.py index 3fe5d2f..f65c8dd 100644 --- a/ciao/algorithm/mcgs.py +++ b/ciao/algorithm/mcgs.py @@ -150,7 +150,7 @@ def select_uct_child_rave( def expand_node_eager( node: MCGSNode, - adj_masks: tuple, + adj_masks: tuple[int, ...], used_mask: int, transposition_table: dict[int, MCGSNode], mode: str, @@ -209,7 +209,7 @@ def expand_node_eager( return seg_id, child -def update_edge_stats(node: MCGSNode, action: int, reward: float): +def update_edge_stats(node: MCGSNode, action: int, reward: float) -> None: """Update edge statistics for a specific action (RAVE mode).""" if action not in node.edge_stats: node.init_edge(action) @@ -221,7 +221,7 @@ def update_edge_stats(node: MCGSNode, action: int, reward: float): stats["max_reward"] = max(stats["max_reward"], reward) -def update_rave_stats(node: MCGSNode, action: int, reward: float): +def update_rave_stats(node: MCGSNode, action: int, reward: float) -> None: """Update RAVE statistics for a specific action (RAVE mode).""" if action not in node.rave_stats: node.init_edge(action) @@ -278,7 +278,7 @@ def backup_paths_rave( batch_actions: list[list[int]], batch_masks: list[int], rewards: list[float], - adj_masks: tuple, + adj_masks: tuple[int, ...], used_mask: int, ) -> None: """Backup rewards using edge-level statistics and RAVE updates (RAVE mode). @@ -406,8 +406,9 @@ def build_hyperpixel_mcgs( # --- PHASE 1: BATCH COLLECTION --- batch_paths = [] batch_masks = [] - cached_rewards = [] # Store cached values for visited terminals - needs_gpu_eval = [] # Track which entries need GPU evaluation + cached_rewards: list[ + float | None + ] = [] # Store cached values for visited terminals batch_actions = [] # For RAVE mode: track actions taken for _ in range(batch_size): @@ -469,7 +470,6 @@ def build_hyperpixel_mcgs( ): rollout_mask = node.mask cached_rewards.append(node.max_value) - needs_gpu_eval.append(False) else: if is_terminal(node.mask, adj_masks, used_mask, desired_length): rollout_mask = node.mask @@ -485,7 +485,6 @@ def build_hyperpixel_mcgs( ) cached_rewards.append(None) - needs_gpu_eval.append(True) batch_paths.append(path) batch_masks.append(rollout_mask) @@ -495,12 +494,12 @@ def build_hyperpixel_mcgs( # Separate masks that need GPU evaluation from cached ones masks_to_evaluate = [ (i, batch_masks[i]) - for i, need_eval in enumerate(needs_gpu_eval) - if need_eval + for i, reward in enumerate(cached_rewards) + if reward is None ] # Update statistics - cache_hits = sum(1 for need_eval in needs_gpu_eval if not need_eval) + cache_hits = batch_size - len(masks_to_evaluate) total_cache_hits += cache_hits total_gpu_evaluations += len(masks_to_evaluate) @@ -514,13 +513,13 @@ def build_hyperpixel_mcgs( gpu_rewards = [r * optimization_sign for r in raw_rewards] # Merge GPU results with cached values (cached values are already signed) - batch_rewards = [] + batch_rewards: list[float] = [] gpu_idx = 0 - for i in range(batch_size): - if not needs_gpu_eval[i]: - batch_rewards.append(cached_rewards[i]) + + for cached_val in cached_rewards: + if cached_val is not None: + batch_rewards.append(cached_val) else: - # Use GPU result batch_rewards.append(gpu_rewards[gpu_idx]) gpu_idx += 1 @@ -581,28 +580,28 @@ def build_hyperpixel_mcgs( # Add RAVE-specific data if mode == "rave": - result["stats"]["rave_k"] = rave_k + result["stats"]["rave_k"] = rave_k # type: ignore[index] return result def build_all_hyperpixels_mcgs( - predictor, - input_batch, - segments, - adj_masks, - target_class_idx, - scores, - next_id, - max_hyperpixels=10, - desired_length=30, - num_iterations=100, - mode="standard", - batch_size=64, - exploration_c=1.4, - virtual_loss=1.0, - rave_k=1000.0, -): + predictor: ModelPredictor, + input_batch: torch.Tensor, + segments: np.ndarray, + adj_masks: tuple[int, ...], + target_class_idx: int, + scores: dict[int, float], + next_id: int, + max_hyperpixels: int = 10, + desired_length: int = 30, + num_iterations: int = 100, + mode: str = "standard", + batch_size: int = 64, + exploration_c: float = 1.4, + virtual_loss: float = 1.0, + rave_k: float = 1000.0, +) -> list[dict[str, object]]: """Build multiple hyperpixels using MCGS. Args: diff --git a/ciao/algorithm/mcts.py b/ciao/algorithm/mcts.py index 7fa90ff..6fdbb67 100644 --- a/ciao/algorithm/mcts.py +++ b/ciao/algorithm/mcts.py @@ -63,7 +63,9 @@ def update(self, rollout_mask: int, reward: float) -> None: # ============================================================================ -def is_fully_expanded(node: MCTSNode, adj_masks: tuple, used_mask: int) -> bool: +def is_fully_expanded( + node: MCTSNode, adj_masks: tuple[int, ...], used_mask: int +) -> bool: """Check if all frontier segments have been expanded as children.""" frontier = get_frontier(node.mask, adj_masks, used_mask) @@ -162,7 +164,7 @@ def select_uct_child_rave( def expand_node( node: MCTSNode, - adj_masks: tuple, + adj_masks: tuple[int, ...], used_mask: int, global_stats: GlobalStats | None = None, ) -> MCTSNode | None: @@ -354,8 +356,9 @@ def build_hyperpixel_mcts( # --- PHASE 1: BATCH COLLECTION --- batch_paths = [] batch_rollout_masks = [] - cached_rewards = [] # Store cached values for visited terminals - needs_gpu_eval = [] # Track which entries need GPU evaluation + cached_rewards: list[ + float | None + ] = [] # Store cached values for visited terminals for _ in range(batch_size): # --- SELECTION --- @@ -400,7 +403,6 @@ def build_hyperpixel_mcts( # Reuse cached value - no GPU evaluation needed rollout_mask = node.mask cached_rewards.append(node.max_value) - needs_gpu_eval.append(False) else: # Need GPU evaluation if is_terminal(node.mask, adj_masks, used_mask, desired_length): @@ -417,7 +419,6 @@ def build_hyperpixel_mcts( ) cached_rewards.append(None) - needs_gpu_eval.append(True) batch_paths.append(path) batch_rollout_masks.append(rollout_mask) @@ -426,12 +427,12 @@ def build_hyperpixel_mcts( # Separate masks that need GPU evaluation from cached ones masks_to_evaluate = [ (i, batch_rollout_masks[i]) - for i, need_eval in enumerate(needs_gpu_eval) - if need_eval + for i, reward in enumerate(cached_rewards) + if reward is None ] # Update statistics - cache_hits = sum(1 for need_eval in needs_gpu_eval if not need_eval) + cache_hits = batch_size - len(masks_to_evaluate) total_cache_hits += cache_hits total_gpu_evaluations += len(masks_to_evaluate) @@ -445,13 +446,13 @@ def build_hyperpixel_mcts( gpu_rewards = [r * optimization_sign for r in raw_rewards] # Merge GPU results with cached values (cached values are already signed) - batch_rewards = [] + batch_rewards: list[float] = [] gpu_idx = 0 - for i in range(batch_size): - if not needs_gpu_eval[i]: - batch_rewards.append(cached_rewards[i]) + + for cached_val in cached_rewards: + if cached_val is not None: + batch_rewards.append(cached_val) else: - # Use GPU result batch_rewards.append(gpu_rewards[gpu_idx]) gpu_idx += 1 @@ -511,28 +512,28 @@ def count_nodes(node: MCTSNode) -> int: # Add RAVE-specific data if mode == "rave": - result["stats"]["rave_k"] = rave_k + result["stats"]["rave_k"] = rave_k # type: ignore[index] return result def build_all_hyperpixels_mcts( - predictor, - input_batch, - segments, - adj_masks, - target_class_idx, - scores, - next_id, - max_hyperpixels=10, - desired_length=30, - num_iterations=100, - mode="standard", - batch_size=64, - exploration_c=1.4, - virtual_loss=1.0, - rave_k=1000, -): + predictor: ModelPredictor, + input_batch: torch.Tensor, + segments: np.ndarray, + adj_masks: tuple[int, ...], + target_class_idx: int, + scores: dict[int, float], + next_id: int, + max_hyperpixels: int = 10, + desired_length: int = 30, + num_iterations: int = 100, + mode: str = "standard", + batch_size: int = 64, + exploration_c: float = 1.4, + virtual_loss: float = 1.0, + rave_k: int = 1000, +) -> list[dict[str, object]]: """Build multiple hyperpixels using MCTS. Args: diff --git a/ciao/algorithm/potential.py b/ciao/algorithm/potential.py index da738ea..dde96d9 100644 --- a/ciao/algorithm/potential.py +++ b/ciao/algorithm/potential.py @@ -1,5 +1,8 @@ import time +import numpy as np +import torch + from ciao.structures.bitmask_graph import ( add_node, get_frontier, @@ -8,7 +11,7 @@ remove_node, sample_connected_superset, ) -from ciao.utils.calculations import calculate_hyperpixel_deltas +from ciao.utils.calculations import ModelPredictor, calculate_hyperpixel_deltas def compute_potentials( @@ -32,7 +35,7 @@ def compute_potentials( def select_best_neighbor(potentials: dict[int, list[float]]) -> int: """Select neighbor with highest potential using lexicographical comparison.""" best_neighbor = -1 - best_vector = [] + best_vector: list[float] = [] for node_id, scores in potentials.items(): if not scores: @@ -46,18 +49,18 @@ def select_best_neighbor(potentials: dict[int, list[float]]) -> int: def build_hyperpixel_using_potential( - predictor, - input_batch, - segments, + predictor: ModelPredictor, + input_batch: torch.Tensor, + segments: np.ndarray, adj_masks: tuple[int, ...], target_class_idx: int, desired_length: int, seed_idx: int, num_simulations: int, - used_segments: set | None = None, + used_segments: set[int] | None = None, batch_size: int = 64, optimization_sign: int = 1, -): +) -> dict[str, object]: """Build a hyperpixel using Sequential Monte Carlo with Potential-based Selection. This algorithm grows a connected region on the segmentation graph by: @@ -232,9 +235,9 @@ def sampling_phase( num_simulations: int, desired_length: int, adj_masks: tuple[int, ...], - predictor, - input_batch, - segments, + predictor: ModelPredictor, + input_batch: torch.Tensor, + segments: np.ndarray, target_class_idx: int, batch_size: int, optimization_sign: int, @@ -341,9 +344,9 @@ def sampling_phase( def select_best_prefix( full_structure: list[int], - predictor, - input_batch, - segments, + predictor: ModelPredictor, + input_batch: torch.Tensor, + segments: np.ndarray, target_class_idx: int, batch_size: int, optimization_sign: int, @@ -382,7 +385,7 @@ def select_best_prefix( prefixes.append(mask_to_ids(current_mask)) missing_indices = [] - scores = [None] * len(prefix_masks) + scores: list[float | None] = [None] * len(prefix_masks) for i, mask in enumerate(prefix_masks): if mask in cache: @@ -408,10 +411,10 @@ def select_best_prefix( cache[prefix_masks[i]] = signed_score best_idx = 0 - max_score = -float("inf") + max_score: float = -float("inf") - for i, score in enumerate(scores): - if score > max_score: + for i, score in enumerate(scores): # type: ignore[assignment] + if score is not None and isinstance(score, float) and score > max_score: max_score = score best_idx = i @@ -434,7 +437,7 @@ def redistribute_history( H_winner: list[tuple[int, float]], # noqa: N803 new_frontier_mask: int, cache: dict[int, list[tuple[int, float]]], -): +) -> None: """Redistribute winner's Monte Carlo history to the new frontier. After adding the winning node to the structure, the frontier changes. @@ -468,17 +471,17 @@ def redistribute_history( def build_all_hyperpixels_potential( - predictor, - input_batch, - segments, - adj_masks, - target_class_idx, - scores, - max_hyperpixels=10, - desired_length=30, - num_simulations=50, - batch_size=64, -): + predictor: ModelPredictor, + input_batch: torch.Tensor, + segments: np.ndarray, + adj_masks: tuple[int, ...], + target_class_idx: int, + scores: dict[int, float], + max_hyperpixels: int = 10, + desired_length: int = 30, + num_simulations: int = 50, + batch_size: int = 64, +) -> list[dict[str, object]]: """Build multiple hyperpixels using the potential field method. Args: @@ -496,8 +499,8 @@ def build_all_hyperpixels_potential( Returns: List of hyperpixel dictionaries """ - hyperpixels = [] - processed_segments = set() + hyperpixels: list[dict[str, object]] = [] + processed_segments: set[int] = set() for _ in range(max_hyperpixels): # Find unprocessed segment with highest absolute score @@ -534,13 +537,13 @@ def build_all_hyperpixels_potential( { "segments": hyperpixel_segments, "sign": optimization_sign, - "size": len(hyperpixel_segments), + "size": len(hyperpixel_segments), # type: ignore[arg-type] "hyperpixel_score": result["score"], "stats": result.get( "stats", {} ), # Include potential method statistics } ) - processed_segments.update(hyperpixel_segments) + processed_segments.update(hyperpixel_segments) # type: ignore[arg-type] return hyperpixels diff --git a/ciao/structures/bitmask_graph.py b/ciao/structures/bitmask_graph.py index 4383919..bd97ec1 100644 --- a/ciao/structures/bitmask_graph.py +++ b/ciao/structures/bitmask_graph.py @@ -5,6 +5,7 @@ """ import random +from collections.abc import Iterator import numpy as np @@ -14,7 +15,7 @@ def mask_to_ids(mask: int) -> list[int]: return [i for i in range(mask.bit_length()) if (mask >> i) & 1] -def iter_bits(mask: int): +def iter_bits(mask: int) -> Iterator[int]: """Iterate over set bits in a mask using low-bit isolation. Yields node IDs in arbitrary order (depends on bit positions). diff --git a/ciao/structures/nodes.py b/ciao/structures/nodes.py index bc9e998..3c396b5 100644 --- a/ciao/structures/nodes.py +++ b/ciao/structures/nodes.py @@ -22,10 +22,10 @@ def __init__( self.prior_score = prior_score self.frontier_cache: int | None = None - def mean_value(self): + def mean_value(self) -> float: return self.value_sum / self.visits if self.visits > 0 else 0.0 - def rave_mean(self): + def rave_mean(self) -> float: return self.rave_value_sum / self.rave_visits if self.rave_visits > 0 else 0.0 @@ -45,7 +45,7 @@ def __init__(self, mask: int): self.max_value = -float("inf") self.pending = 0 # virtual loss counter (for non-RAVE modes) - def init_edge(self, action: int): + def init_edge(self, action: int) -> None: if action not in self.edge_stats: self.edge_stats[action] = { "N": 0, diff --git a/ciao/utils/calculations.py b/ciao/utils/calculations.py index 0b153f0..213def0 100644 --- a/ciao/utils/calculations.py +++ b/ciao/utils/calculations.py @@ -1,4 +1,5 @@ import matplotlib.pyplot as plt +import networkx as nx import numpy as np import torch import torch.nn.functional as F @@ -7,7 +8,7 @@ class ModelPredictor: """Handles model predictions and class information.""" - def __init__(self, model, class_names: list[str]): + def __init__(self, model: torch.nn.Module, class_names: list[str]) -> None: self.model = model self.class_names = class_names self.device = next(model.parameters()).device @@ -37,8 +38,8 @@ def predict_image( results = [] for i in range(top_k): - class_idx = top_indices[i].item() - prob = top_probs[i].item() + class_idx = int(top_indices[i].item()) + prob = float(top_probs[i].item()) class_name = ( self.class_names[class_idx] if class_idx < len(self.class_names) @@ -59,15 +60,17 @@ def calculate_image_mean_color(self, input_tensor: torch.Tensor) -> torch.Tensor return normalized_mean.squeeze(0) # Remove batch dimension def get_replacement_image( - self, input_tensor: torch.Tensor, replacement: str = "mean_color", **kwargs + self, + input_tensor: torch.Tensor, + replacement: str = "mean_color", + color: tuple[int, int, int] = (0, 0, 0), ) -> torch.Tensor: """Generate replacement image for masking operations. Args: input_tensor: Input tensor [3, H, W] (ImageNet normalized) replacement: Strategy - "mean_color", "interlacing", "blur", or "solid_color" - **kwargs: Additional options: - - color: For solid_color mode, RGB tuple (0-255). Defaults to black (0, 0, 0) + color: For solid_color mode, RGB tuple (0-255). Defaults to black (0, 0, 0) Returns: replacement_image: torch tensor [3, H, W] on same device @@ -130,20 +133,17 @@ def get_replacement_image( elif replacement == "solid_color": # Fill with specified solid color (expects RGB values in 0-255 range) - color = kwargs.get("color", (0, 0, 0)) # Default to black - # Convert color to torch tensor (always assume 0-255 range) - if isinstance(color, (list, tuple)): - color = torch.tensor(color, dtype=torch.float32, device=self.device) + color_tensor = torch.tensor(color, dtype=torch.float32, device=self.device) # Convert from 0-255 range to 0-1 range - color = color / 255.0 + color_tensor = color_tensor / 255.0 # Apply ImageNet normalization - squeeze to remove batch dimension from constants - color = color.view(3, 1, 1) # [3, 1, 1] + color_tensor = color_tensor.view(3, 1, 1) # [3, 1, 1] mean = self.imagenet_mean.squeeze(0) # [3, 1, 1] std = self.imagenet_std.squeeze(0) # [3, 1, 1] - normalized_color = (color - mean) / std + normalized_color = (color_tensor - mean) / std replacement_image = normalized_color.expand(-1, height, width) # [3, H, W] else: @@ -151,7 +151,7 @@ def get_replacement_image( return replacement_image - def plot_image_mean_color(self, input_tensor): + def plot_image_mean_color(self, input_tensor: torch.Tensor) -> None: normalized_mean = self.calculate_image_mean_color(input_tensor).unsqueeze(0) plt.imshow(normalized_mean[0].permute(1, 2, 0)) plt.show() @@ -174,7 +174,7 @@ def create_surrogate_dataset( predictor: ModelPredictor, input_batch: torch.Tensor, segments: np.ndarray, - graph, # NetworkX graph + graph: nx.Graph, target_class_idx: int, neighborhood_distance: int = 1, batch_size: int = 16, @@ -381,12 +381,12 @@ def calculate_hyperpixel_deltas( def select_top_hyperpixels( - hyperpixels: list[dict], max_hyperpixels: int = 10 -) -> list[dict]: + hyperpixels: list[dict[str, object]], max_hyperpixels: int = 10 +) -> list[dict[str, object]]: """Select top hyperpixels by their primary algorithm-specific score.""" # Use hyperpixel_score return sorted( hyperpixels, - key=lambda hp: abs(hp.get("hyperpixel_score", 0)), + key=lambda hp: abs(hp.get("hyperpixel_score", 0)), # type: ignore[arg-type] reverse=True, )[:max_hyperpixels] diff --git a/ciao/utils/search_utils.py b/ciao/utils/search_utils.py index 225783e..f58ae06 100644 --- a/ciao/utils/search_utils.py +++ b/ciao/utils/search_utils.py @@ -11,7 +11,9 @@ from ciao.utils.calculations import ModelPredictor, calculate_hyperpixel_deltas -def is_terminal(mask: int, adj_masks: tuple, used_mask: int, max_depth: int) -> bool: +def is_terminal( + mask: int, adj_masks: tuple[int, ...], used_mask: int, max_depth: int +) -> bool: """Check if state is terminal (max depth or no frontier).""" return ( mask.bit_count() >= max_depth or get_frontier(mask, adj_masks, used_mask) == 0 diff --git a/ciao/utils/segmentation.py b/ciao/utils/segmentation.py index ce95f6b..10cab91 100644 --- a/ciao/utils/segmentation.py +++ b/ciao/utils/segmentation.py @@ -2,9 +2,10 @@ import networkx as nx import numpy as np +import torch -def hex_round(q, r): +def hex_round(q: float, r: float) -> tuple[int, int]: """Round axial coordinates to nearest hex. Args: @@ -36,7 +37,7 @@ def hex_round(q, r): return int(rx), int(rz) # back to axial (q = x, r = z) -def pixel_to_hex(px, py, size): +def pixel_to_hex(px: float, py: float, size: float) -> tuple[int, int]: """Convert pixel coordinate to axial hex coordinates. Args: @@ -52,7 +53,7 @@ def pixel_to_hex(px, py, size): return hex_round(q, r) -def build_hex_adjacency_graph(hex_to_id): +def build_hex_adjacency_graph(hex_to_id: dict[tuple[int, int], int]) -> nx.Graph: """Build adjacency graph for hexagonal grid using axial coordinate neighbors. Hexagons have exactly 6 neighbors with well-defined axial coordinate offsets: @@ -87,7 +88,7 @@ def build_hex_adjacency_graph(hex_to_id): return adj_graph -def build_adjacency_graph(segments, neighborhood=8): +def build_adjacency_graph(segments: np.ndarray, neighborhood: int = 8) -> nx.Graph: adj_graph = nx.Graph() segment_ids = np.unique(segments) adj_graph.add_nodes_from(segment_ids) @@ -122,7 +123,9 @@ def build_adjacency_graph(segments, neighborhood=8): return adj_graph -def build_fast_adjacency_list(hex_to_id, max_id): +def build_fast_adjacency_list( + hex_to_id: dict[tuple[int, int], int], max_id: int +) -> tuple[tuple[int, ...], ...]: """Create a static adjacency list optimized for fast reading. Args: @@ -135,7 +138,7 @@ def build_fast_adjacency_list(hex_to_id, max_id): """ # Initialize empty lists for each ID # Use list of lists for construction - temp_adj = [[] for _ in range(max_id)] + temp_adj: list[list[int]] = [[] for _ in range(max_id)] # Offsets for neighbors (axial coords) hex_neighbors = [(+1, 0), (+1, -1), (0, -1), (-1, 0), (-1, +1), (0, +1)] @@ -159,7 +162,9 @@ def build_fast_adjacency_list(hex_to_id, max_id): # --- Modified create_hexagonal_grid function --- -def create_hexagonal_grid_with_list(input_tensor, hex_radius=14): +def create_hexagonal_grid_with_list( + input_tensor: torch.Tensor, hex_radius: int = 14 +) -> tuple[np.ndarray, tuple[tuple[int, ...], ...], int]: _channels, height, width = input_tensor.shape segments = np.zeros((height, width), dtype=np.int32) @@ -185,7 +190,7 @@ def create_hexagonal_grid_with_list(input_tensor, hex_radius=14): return segments, adjacency_list, next_id -def build_adjacency_bitmasks(adj_list): +def build_adjacency_bitmasks(adj_list: tuple[tuple[int, ...], ...]) -> tuple[int, ...]: """Convert adjacency list to a list of integers. adj_masks[5] will be an integer with bits set at positions of hex 5's neighbors. @@ -199,7 +204,9 @@ def build_adjacency_bitmasks(adj_list): return tuple(adj_masks) -def create_square_grid(input_tensor, square_size=14, neighborhood=8): +def create_square_grid( + input_tensor: torch.Tensor, square_size: int = 14, neighborhood: int = 8 +) -> tuple[np.ndarray, nx.Graph]: """Create a grid of squares with graph structure representing spatial relationships.""" _channels, height, width = input_tensor.shape segments = np.zeros((height, width), dtype=np.int32) @@ -223,7 +230,9 @@ def create_square_grid(input_tensor, square_size=14, neighborhood=8): return segments, adjacency_graph -def create_hexagonal_grid(input_tensor, hex_radius=14, neighborhood=6): +def create_hexagonal_grid( + input_tensor: torch.Tensor, hex_radius: int = 14, neighborhood: int = 6 +) -> tuple[np.ndarray, nx.Graph]: """Create a grid of hexagons with graph structure representing spatial relationships. Uses axial coordinate system for precise hexagonal tiling (flat-top orientation). @@ -264,8 +273,11 @@ def create_hexagonal_grid(input_tensor, hex_radius=14, neighborhood=6): def create_segmentation( - input_tensor, segmentation_type="hexagonal", segment_size=14, neighborhood=8 -): + input_tensor: torch.Tensor, + segmentation_type: str = "hexagonal", + segment_size: int = 14, + neighborhood: int = 8, +) -> tuple[np.ndarray, nx.Graph]: """Create image segmentation with specified type. Args: From 3bd5e6e5e7478a31f36b8101c6fad6deb81a5fc5 Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Wed, 25 Feb 2026 14:29:30 +0100 Subject: [PATCH 12/25] chore: remove the neighborhood parameter from the hexagonal grid creation --- ciao/utils/segmentation.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/ciao/utils/segmentation.py b/ciao/utils/segmentation.py index 10cab91..9f8c365 100644 --- a/ciao/utils/segmentation.py +++ b/ciao/utils/segmentation.py @@ -231,7 +231,7 @@ def create_square_grid( def create_hexagonal_grid( - input_tensor: torch.Tensor, hex_radius: int = 14, neighborhood: int = 6 + input_tensor: torch.Tensor, hex_radius: int = 14 ) -> tuple[np.ndarray, nx.Graph]: """Create a grid of hexagons with graph structure representing spatial relationships. @@ -241,7 +241,6 @@ def create_hexagonal_grid( Args: input_tensor: Input image tensor [C, H, W] hex_radius: Hex size parameter (distance from center to flat edge, default: 14) - neighborhood: Ignored for hexagons (always 6-connected) Returns: segments: 2D array mapping pixels to segment IDs @@ -284,7 +283,7 @@ def create_segmentation( input_tensor: Input image tensor [C, H, W] segmentation_type: "square" or "hexagonal" segment_size: Size parameter (square_size or hex_radius) - neighborhood: Neighborhood connectivity (4, 6, or 8) + neighborhood: Neighborhood connectivity for squares (4, or 8) Returns: segments: 2D array mapping pixels to segment IDs @@ -296,7 +295,7 @@ def create_segmentation( ) elif segmentation_type == "hexagonal": return create_hexagonal_grid( - input_tensor, hex_radius=segment_size, neighborhood=neighborhood + input_tensor, hex_radius=segment_size ) else: raise ValueError( From 9ec2899a0d4cd524652f3e003f9815cc4738e9dd Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Wed, 25 Feb 2026 14:47:24 +0100 Subject: [PATCH 13/25] refactor: apply agents' suggestions --- ciao/algorithm/lookahead_bitset.py | 8 +++++--- ciao/algorithm/mcts.py | 4 ++-- ciao/structures/bitmask_graph.py | 6 ++++++ ciao/utils/calculations.py | 20 +++++++++++++++++--- ciao/utils/search_utils.py | 9 +++++---- ciao/utils/segmentation.py | 13 ++++++++++--- 6 files changed, 45 insertions(+), 15 deletions(-) diff --git a/ciao/algorithm/lookahead_bitset.py b/ciao/algorithm/lookahead_bitset.py index e2bcb39..dfcce60 100644 --- a/ciao/algorithm/lookahead_bitset.py +++ b/ciao/algorithm/lookahead_bitset.py @@ -192,9 +192,11 @@ def _generate_lookahead_candidates( if mask in candidates: first_step = candidates[mask] else: - # This shouldn't happen in proper BFS, but handle edge case - # This mask came from current_mask, find the connection - first_step = _find_first_step(current_mask, new_mask) + # This shouldn't happen in proper BFS - raise error if it does + raise RuntimeError( + f"BFS inconsistency: mask {mask} not found in candidates at depth {depth}. " + "This indicates a logic error in the BFS traversal." + ) # Only add if not already seen (first path wins) if new_mask not in candidates: diff --git a/ciao/algorithm/mcts.py b/ciao/algorithm/mcts.py index 6fdbb67..4726c91 100644 --- a/ciao/algorithm/mcts.py +++ b/ciao/algorithm/mcts.py @@ -218,7 +218,7 @@ def backup_paths(batch_paths: list[list[MCTSNode]], rewards: list[float]) -> Non - pending (release virtual loss) """ for path, reward in zip(batch_paths, rewards, strict=True): - for node in path: + for node in path[1:]: # Skip root (never incremented, so shouldn't decrement) node.pending -= 1 # Release virtual loss node.visits += 1 node.value_sum += reward # Mean tracking @@ -258,7 +258,7 @@ def backup_paths_rave( if global_stats is not None: global_stats.update(rollout_mask, reward) - for node in path: + for node in path[1:]: # Skip root (never incremented, so shouldn't decrement) # --- STANDARD BACKUP --- node.pending -= 1 # Release virtual loss node.visits += 1 diff --git a/ciao/structures/bitmask_graph.py b/ciao/structures/bitmask_graph.py index bd97ec1..4cddb6f 100644 --- a/ciao/structures/bitmask_graph.py +++ b/ciao/structures/bitmask_graph.py @@ -174,6 +174,12 @@ def _pick_weighted_frontier_segment( Returns: Selected segment ID """ + if temperature <= 0: + raise ValueError( + f"temperature must be positive, got {temperature}. " + "Non-positive values cause division by zero or invalid probabilities." + ) + # Extract frontier segment IDs and their weights frontier_ids = list(iter_bits(frontier)) frontier_weights = segment_weights[frontier_ids] diff --git a/ciao/utils/calculations.py b/ciao/utils/calculations.py index 213def0..120ea22 100644 --- a/ciao/utils/calculations.py +++ b/ciao/utils/calculations.py @@ -152,6 +152,10 @@ def get_replacement_image( return replacement_image def plot_image_mean_color(self, input_tensor: torch.Tensor) -> None: + """Display the mean color of the image. + + Note: The visualization shows the normalized tensor (ImageNet normalization). + """ normalized_mean = self.calculate_image_mean_color(input_tensor).unsqueeze(0) plt.imshow(normalized_mean[0].permute(1, 2, 0)) plt.show() @@ -281,6 +285,11 @@ def calculate_scores_from_surrogate(X: np.ndarray, y: np.ndarray) -> dict[int, f mask = X[:, segment_id] == 1.0 segment_scores = y[mask] + if len(segment_scores) == 0: + raise ValueError( + f"Segment {segment_id} never appears in any local group. " + "This suggests a bug in group generation or segment ID mapping." + ) scores[segment_id] = float(segment_scores.mean()) score_values = list(scores.values()) @@ -332,7 +341,11 @@ def calculate_hyperpixel_deltas( ].item() # Get replacement image using the specified strategy - assert predictor.replacement_image is not None + if predictor.replacement_image is None: + raise RuntimeError( + "replacement_image is not initialized. " + "Call create_replacement_image() before using calculate_hyperpixel_deltas." + ) replacement_image = predictor.replacement_image # Convert segments numpy array to GPU tensor once (outside loop) @@ -374,8 +387,9 @@ def calculate_hyperpixel_deltas( # Memory cleanup del batch_inputs, masked_logits - if torch.cuda.is_available(): - torch.cuda.empty_cache() + # Note: torch.cuda.empty_cache() removed from inner loop for performance. + # Cache clearing here causes allocator churn and synchronization overhead. + # PyTorch's automatic memory management is sufficient for typical use. return all_deltas diff --git a/ciao/utils/search_utils.py b/ciao/utils/search_utils.py index f58ae06..b305250 100644 --- a/ciao/utils/search_utils.py +++ b/ciao/utils/search_utils.py @@ -28,11 +28,12 @@ def evaluate_masks( masks: list[int], ) -> list[float]: """Evaluate multiple segment masks by computing class score deltas (batched).""" - # Guard against zero masks which would produce empty segment lists - if any(mask == 0 for mask in masks): + # Guard against invalid masks (zero or negative) + if any(mask <= 0 for mask in masks): raise ValueError( - "Cannot evaluate zero mask: A mask with value 0 contains no segments. " - "Ensure all masks have at least one bit set." + "Cannot evaluate invalid mask: A mask must be a positive integer. " + "Zero masks contain no segments, and negative masks cause " + "incorrect bit iteration due to two's complement representation." ) all_segment_ids = [list(iter_bits(mask)) for mask in masks] diff --git a/ciao/utils/segmentation.py b/ciao/utils/segmentation.py index 9f8c365..de5b7c5 100644 --- a/ciao/utils/segmentation.py +++ b/ciao/utils/segmentation.py @@ -112,11 +112,12 @@ def build_adjacency_graph(segments: np.ndarray, neighborhood: int = 8) -> nx.Gra if neighborhood == 8: # Add diagonal adjacency for 8-neighborhood for y in range(height - 1): - for x in range(width - 1): + for x in range(width): center_seg = segments[y, x] - # Check diagonal neighbors - if segments[y + 1, x + 1] != center_seg: + # Check down-right diagonal + if x + 1 < width and segments[y + 1, x + 1] != center_seg: adj_graph.add_edge(center_seg, segments[y + 1, x + 1]) + # Check down-left diagonal if x > 0 and segments[y + 1, x - 1] != center_seg: adj_graph.add_edge(center_seg, segments[y + 1, x - 1]) @@ -289,6 +290,12 @@ def create_segmentation( segments: 2D array mapping pixels to segment IDs adjacency_graph: NetworkX graph of segment relationships """ + if segment_size <= 0: + raise ValueError( + f"segment_size must be positive, got {segment_size}. " + "Non-positive values cause division by zero or invalid range operations." + ) + if segmentation_type == "square": return create_square_grid( input_tensor, square_size=segment_size, neighborhood=neighborhood From f7e6348ebf2972a491974d870a6fad634d25aeab Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Wed, 25 Feb 2026 14:57:18 +0100 Subject: [PATCH 14/25] chore: run ruff and mypy checks --- ciao/algorithm/__init__.py | 2 +- ciao/structures/bitmask_graph.py | 2 +- ciao/utils/calculations.py | 2 +- ciao/utils/segmentation.py | 6 ++---- 4 files changed, 5 insertions(+), 7 deletions(-) diff --git a/ciao/algorithm/__init__.py b/ciao/algorithm/__init__.py index 4e33cd4..bf426d1 100644 --- a/ciao/algorithm/__init__.py +++ b/ciao/algorithm/__init__.py @@ -1,3 +1,3 @@ """CIAO algorithm implementations.""" -__all__ = [] +__all__: list[str] = [] diff --git a/ciao/structures/bitmask_graph.py b/ciao/structures/bitmask_graph.py index 4cddb6f..fcb7e76 100644 --- a/ciao/structures/bitmask_graph.py +++ b/ciao/structures/bitmask_graph.py @@ -179,7 +179,7 @@ def _pick_weighted_frontier_segment( f"temperature must be positive, got {temperature}. " "Non-positive values cause division by zero or invalid probabilities." ) - + # Extract frontier segment IDs and their weights frontier_ids = list(iter_bits(frontier)) frontier_weights = segment_weights[frontier_ids] diff --git a/ciao/utils/calculations.py b/ciao/utils/calculations.py index 120ea22..3f7913e 100644 --- a/ciao/utils/calculations.py +++ b/ciao/utils/calculations.py @@ -153,7 +153,7 @@ def get_replacement_image( def plot_image_mean_color(self, input_tensor: torch.Tensor) -> None: """Display the mean color of the image. - + Note: The visualization shows the normalized tensor (ImageNet normalization). """ normalized_mean = self.calculate_image_mean_color(input_tensor).unsqueeze(0) diff --git a/ciao/utils/segmentation.py b/ciao/utils/segmentation.py index de5b7c5..ccbb25d 100644 --- a/ciao/utils/segmentation.py +++ b/ciao/utils/segmentation.py @@ -295,15 +295,13 @@ def create_segmentation( f"segment_size must be positive, got {segment_size}. " "Non-positive values cause division by zero or invalid range operations." ) - + if segmentation_type == "square": return create_square_grid( input_tensor, square_size=segment_size, neighborhood=neighborhood ) elif segmentation_type == "hexagonal": - return create_hexagonal_grid( - input_tensor, hex_radius=segment_size - ) + return create_hexagonal_grid(input_tensor, hex_radius=segment_size) else: raise ValueError( f"Unknown segmentation_type: {segmentation_type}. Use 'square' or 'hexagonal'." From bab85962636c81696bc4f68c1295a5322d38d63e Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Wed, 25 Feb 2026 15:06:57 +0100 Subject: [PATCH 15/25] chore: add .pre-commit-config file to .gitignore --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index 16143c0..a661873 100644 --- a/.gitignore +++ b/.gitignore @@ -169,3 +169,6 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. .idea/ + +# pre-commit +.pre-commit-config.yaml \ No newline at end of file From 07877d2e46ce07fb0e9f83cb6c59068870e4b34a Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Wed, 25 Feb 2026 15:08:37 +0100 Subject: [PATCH 16/25] refactor: replace print statements with logging --- ciao/algorithm/lookahead_bitset.py | 32 ++++++++++++++++++------------ ciao/algorithm/potential.py | 32 +++++++++++++++++------------- ciao/utils/calculations.py | 18 +++++++++-------- 3 files changed, 47 insertions(+), 35 deletions(-) diff --git a/ciao/algorithm/lookahead_bitset.py b/ciao/algorithm/lookahead_bitset.py index dfcce60..292914a 100644 --- a/ciao/algorithm/lookahead_bitset.py +++ b/ciao/algorithm/lookahead_bitset.py @@ -1,3 +1,5 @@ +import logging + import numpy as np import torch @@ -10,6 +12,8 @@ from ciao.utils.calculations import ModelPredictor, calculate_hyperpixel_deltas +logger = logging.getLogger(__name__) + """Greedy lookahead hyperpixel building with bitmask operations. Rolling horizon strategy: Look ahead multiple steps but only commit one step at a time. @@ -57,7 +61,7 @@ def build_hyperpixel_greedy_lookahead( total_evaluations = 0 # Track total number of evaluations num_steps = 0 - print(f" Starting greedy lookahead from seed {seed_idx}") + logger.info(f"Starting greedy lookahead from seed {seed_idx}") # Grow hyperpixel one step at a time while current_mask.bit_count() < desired_length: @@ -74,13 +78,13 @@ def build_hyperpixel_greedy_lookahead( ) if not candidates: - print( - f" Step {num_steps}: No candidates available, stopping at size {current_size}" + logger.info( + f"Step {num_steps}: No candidates available, stopping at size {current_size}" ) break - print( - f" Step {num_steps}: Size={current_size}/{desired_length}, evaluating {len(candidates)} candidates..." + logger.debug( + f"Step {num_steps}: Size={current_size}/{desired_length}, evaluating {len(candidates)} candidates" ) # Batch evaluate all candidates @@ -105,8 +109,8 @@ def build_hyperpixel_greedy_lookahead( best_score = scores_list[best_idx] first_step = candidates[best_mask] - print( - f" Step {num_steps}: Best score={best_score:.4f}, adding segment {first_step}" + logger.debug( + f"Step {num_steps}: Best score={best_score:.4f}, adding segment {first_step}" ) # Commit only the first step @@ -114,7 +118,7 @@ def build_hyperpixel_greedy_lookahead( path.append(first_step) # Evaluate all prefixes and find the best one - print(f" Evaluating {len(path)} prefixes to find best subset...") + logger.debug(f"Evaluating {len(path)} prefixes to find best subset") num_prefix_evaluations = len(path) total_evaluations += num_prefix_evaluations @@ -129,8 +133,8 @@ def build_hyperpixel_greedy_lookahead( ) best_segments = mask_to_ids(best_prefix_mask) - print( - f" Best prefix has {len(best_segments)} segments with score={best_score:.4f}" + logger.info( + f"Best prefix has {len(best_segments)} segments with score={best_score:.4f}" ) return { @@ -290,8 +294,10 @@ def build_all_hyperpixels_greedy_lookahead( seed_score = scores[seed_idx] optimization_sign = 1 if seed_score >= 0 else -1 - print(f"\n--- Hyperpixel {i + 1}/{max_hyperpixels} ---") - print(f"Seed: {seed_idx}, score: {seed_score:.4f}, sign: {optimization_sign}") + logger.info(f"\n--- Hyperpixel {i + 1}/{max_hyperpixels} ---") + logger.info( + f"Seed: {seed_idx}, score: {seed_score:.4f}, sign: {optimization_sign}" + ) result = build_hyperpixel_greedy_lookahead( predictor=predictor, @@ -321,7 +327,7 @@ def build_all_hyperpixels_greedy_lookahead( } hyperpixels.append(hyperpixel) - print( + logger.info( f"Built hyperpixel with {len(result['segments'])} segments, score={result['score']:.4f}" # type: ignore[arg-type] ) diff --git a/ciao/algorithm/potential.py b/ciao/algorithm/potential.py index dde96d9..e2f50e7 100644 --- a/ciao/algorithm/potential.py +++ b/ciao/algorithm/potential.py @@ -1,3 +1,4 @@ +import logging import time import numpy as np @@ -14,6 +15,9 @@ from ciao.utils.calculations import ModelPredictor, calculate_hyperpixel_deltas +logger = logging.getLogger(__name__) + + def compute_potentials( cache: dict[int, list[tuple[int, float]]], ) -> dict[int, list[float]]: @@ -110,8 +114,8 @@ def build_hyperpixel_using_potential( total_evaluations = 0 total_samples = 0 - print("\n=== Sequential Monte Carlo Set Extension ===") - print(f"Seed: {seed_idx}, Target: {desired_length}, Sims: {num_simulations}") + logger.info("\n=== Sequential Monte Carlo Set Extension ===") + logger.info(f"Seed: {seed_idx}, Target: {desired_length}, Sims: {num_simulations}") step = 0 @@ -119,7 +123,7 @@ def build_hyperpixel_using_potential( while len(hyperpixel_structure) < desired_length: step += 1 total_steps += 1 - print( + logger.info( f"\n--- Step {step}: |S| = {len(hyperpixel_structure)}/{desired_length} ---" ) @@ -127,11 +131,11 @@ def build_hyperpixel_using_potential( current_frontier_mask = get_frontier(structure_mask, adj_masks, used_mask) if not current_frontier_mask: - print("Frontier empty. Stopping.") + logger.info("Frontier empty. Stopping.") break - frontier_list = mask_to_ids(current_frontier_mask) - print(f"Frontier size: {len(frontier_list)}") + frontier_list = list(iter_bits(current_frontier_mask)) + logger.debug(f"Frontier size: {len(frontier_list)}") # Phase 1: Sampling - Monte Carlo exploration from each frontier node step_start = time.time() @@ -160,15 +164,15 @@ def build_hyperpixel_using_potential( winner = select_best_neighbor(potentials) if winner == -1: - print("No valid winner found. Stopping.") + logger.info("No valid winner found. Stopping.") break winner_stats = potentials[winner] max_potential = max(winner_stats) if winner_stats else 0 - print( + logger.info( f"Winner: {winner} (samples: {len(winner_stats)}, max: {max_potential:.4f})" ) - print(f"Timing: {sampling_time:.2f}s") + logger.debug(f"Timing: {sampling_time:.2f}s") # Commit: Add winner to hyperpixel structure hyperpixel_structure.append(winner) @@ -184,11 +188,11 @@ def build_hyperpixel_using_potential( redistribute_history(winner_history, new_frontier_mask, potential_cache) recipient_count = len(potential_cache) - print( + logger.debug( f"Pruning: Kept {len(winner_history)} samples, redistributed to {recipient_count} neighbors" ) - print("\n=== Extension Complete ===") + logger.info("\n=== Extension Complete ===") # Post-processing: Find optimal prefix (sometimes full length adds noise) final_hyperpixel, hyperpixel_score = select_best_prefix( @@ -317,7 +321,7 @@ def sampling_phase( return 0, total_samples # --- Batch Evaluation: Score all unique expansions --- - print(f" Evaluating {len(evaluation_queue)} unique samples...") + logger.debug(f"Evaluating {len(evaluation_queue)} unique samples") segment_id_lists = [mask_to_ids(mask) for mask in evaluation_queue] scores = calculate_hyperpixel_deltas( predictor=predictor, @@ -420,11 +424,11 @@ def select_best_prefix( optimized_length = best_idx + 1 if optimized_length < len(full_structure): - print( + logger.info( f"Optimization: Trimmed from {len(full_structure)} to {optimized_length} segments (Score: {max_score:.4f})" ) else: - print( + logger.info( f"Optimization: Kept full length {len(full_structure)} segments (Score: {max_score:.4f})" ) diff --git a/ciao/utils/calculations.py b/ciao/utils/calculations.py index 3f7913e..379378a 100644 --- a/ciao/utils/calculations.py +++ b/ciao/utils/calculations.py @@ -1,3 +1,5 @@ +import logging + import matplotlib.pyplot as plt import networkx as nx import numpy as np @@ -5,6 +7,9 @@ import torch.nn.functional as F +logger = logging.getLogger(__name__) + + class ModelPredictor: """Handles model predictions and class information.""" @@ -211,8 +216,8 @@ def create_surrogate_dataset( original_logit = predictor.get_class_logit_batch(input_batch, target_class_idx)[ 0 ].item() - print(f"Original logit: {original_logit}") - print( + logger.debug(f"Original logit: {original_logit}") + logger.debug( f"Probability of class {target_class_idx}: " f"{predictor.get_predictions(input_batch)[0, target_class_idx].item()}" ) @@ -257,8 +262,8 @@ def create_surrogate_dataset( for segment_id in masked_segments: X[i, segment_id] = 1.0 - print(f"Created surrogate dataset: X shape {X.shape}, y shape {y.shape}") - print(f"Average delta: {y.mean():.4f}, std: {y.std():.4f}") + logger.info(f"Created surrogate dataset: X shape {X.shape}, y shape {y.shape}") + logger.info(f"Average delta: {y.mean():.4f}, std: {y.std():.4f}") return X, y @@ -293,7 +298,7 @@ def calculate_scores_from_surrogate(X: np.ndarray, y: np.ndarray) -> dict[int, f scores[segment_id] = float(segment_scores.mean()) score_values = list(scores.values()) - print(f"Score range: [{min(score_values):.4f}, {max(score_values):.4f}]") + logger.info(f"Score range: [{min(score_values):.4f}, {max(score_values):.4f}]") return scores @@ -387,9 +392,6 @@ def calculate_hyperpixel_deltas( # Memory cleanup del batch_inputs, masked_logits - # Note: torch.cuda.empty_cache() removed from inner loop for performance. - # Cache clearing here causes allocator churn and synchronization overhead. - # PyTorch's automatic memory management is sufficient for typical use. return all_deltas From b4ef5f4a8858cef1a1657180b43af7b68c199fb7 Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Thu, 26 Feb 2026 07:11:36 +0100 Subject: [PATCH 17/25] fix: add virtual loss to root in mcts / mcgs --- ciao/algorithm/mcgs.py | 21 +++++++++++++++++---- ciao/algorithm/mcts.py | 17 +++++++++++------ 2 files changed, 28 insertions(+), 10 deletions(-) diff --git a/ciao/algorithm/mcgs.py b/ciao/algorithm/mcgs.py index f65c8dd..e2a0976 100644 --- a/ciao/algorithm/mcgs.py +++ b/ciao/algorithm/mcgs.py @@ -53,7 +53,9 @@ def select_uct_child( best_action = None best_child = None - parent_visits = node.visits + 1 # +1 for numerical stability + parent_visits = ( + node.visits + node.pending * virtual_loss + 1 + ) # +1 for numerical stability for action, child in node.children.items(): # Get edge statistics (with virtual loss) @@ -103,7 +105,9 @@ def select_uct_child_rave( best_action = None best_child = None - parent_visits = node.visits + 1 # +1 for numerical stability + parent_visits = ( + node.visits + node.pending * virtual_loss + 1 + ) # +1 for numerical stability for action, child in node.children.items(): # Get edge statistics (with virtual loss) @@ -253,6 +257,10 @@ def backup_paths( """ for path, actions, reward in zip(batch_paths, batch_actions, rewards, strict=True): for i, node in enumerate(path): + # Release virtual loss on root + if i == 0: # Root node + node.pending -= 1 + # Update node statistics node.visits += 1 node.value_sum += reward # Mean tracking @@ -308,6 +316,10 @@ def backup_paths_rave( rollout_segments = set(iter_bits(rollout_mask)) for i, node in enumerate(path): + # Release virtual loss on root + if i == 0: # Root node + node.pending -= 1 + # --- STANDARD BACKUP --- node.visits += 1 node.value_sum += reward @@ -417,6 +429,9 @@ def build_hyperpixel_mcgs( path = [node] actions_taken = [] # Track actions for RAVE + # Apply virtual loss to root + root.pending += 1 + # Continue descending until we create a new node or reach terminal while ( expansion_result := expand_node_eager( @@ -592,7 +607,6 @@ def build_all_hyperpixels_mcgs( adj_masks: tuple[int, ...], target_class_idx: int, scores: dict[int, float], - next_id: int, max_hyperpixels: int = 10, desired_length: int = 30, num_iterations: int = 100, @@ -611,7 +625,6 @@ def build_all_hyperpixels_mcgs( adj_masks: Adjacency bitmasks target_class_idx: Target class index scores: Individual segment scores - next_id: Total number of segments max_hyperpixels: Maximum number of hyperpixels to build desired_length: Target segments per hyperpixel num_iterations: Number of MCGS iterations diff --git a/ciao/algorithm/mcts.py b/ciao/algorithm/mcts.py index 4726c91..6f7e0f7 100644 --- a/ciao/algorithm/mcts.py +++ b/ciao/algorithm/mcts.py @@ -84,7 +84,9 @@ def select_uct_child( best_score = -float("inf") best_child = None - parent_visits = node.visits + 1 # +1 for numerical stability + parent_visits = ( + node.visits + node.pending * virtual_loss + 1 + ) # +1 for numerical stability for child in node.children.values(): # Virtual loss: increase effective visit count @@ -127,7 +129,9 @@ def select_uct_child_rave( best_score = -float("inf") best_child = None - parent_visits = node.visits + 1 # +1 for numerical stability + parent_visits = ( + node.visits + node.pending * virtual_loss + 1 + ) # +1 for numerical stability for child in node.children.values(): effective_visits = child.visits + child.pending * virtual_loss @@ -218,7 +222,7 @@ def backup_paths(batch_paths: list[list[MCTSNode]], rewards: list[float]) -> Non - pending (release virtual loss) """ for path, reward in zip(batch_paths, rewards, strict=True): - for node in path[1:]: # Skip root (never incremented, so shouldn't decrement) + for node in path: node.pending -= 1 # Release virtual loss node.visits += 1 node.value_sum += reward # Mean tracking @@ -258,7 +262,7 @@ def backup_paths_rave( if global_stats is not None: global_stats.update(rollout_mask, reward) - for node in path[1:]: # Skip root (never incremented, so shouldn't decrement) + for node in path: # --- STANDARD BACKUP --- node.pending -= 1 # Release virtual loss node.visits += 1 @@ -365,6 +369,9 @@ def build_hyperpixel_mcts( node = root path = [node] + # Apply virtual loss to root + root.pending += 1 + # Standard selection for standard and RAVE modes while is_fully_expanded(node, adj_masks, used_mask) and not is_terminal( node.mask, adj_masks, used_mask, desired_length @@ -524,7 +531,6 @@ def build_all_hyperpixels_mcts( adj_masks: tuple[int, ...], target_class_idx: int, scores: dict[int, float], - next_id: int, max_hyperpixels: int = 10, desired_length: int = 30, num_iterations: int = 100, @@ -543,7 +549,6 @@ def build_all_hyperpixels_mcts( adj_masks: Adjacency bitmasks target_class_idx: Target class index scores: Individual segment scores - next_id: Total number of segments max_hyperpixels: Maximum number of hyperpixels to build desired_length: Target segments per hyperpixel num_iterations: Number of MCTS iterations From 6a68c1c50a6a49c24012015af2aab81a7352ac32 Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Thu, 26 Feb 2026 08:40:51 +0100 Subject: [PATCH 18/25] fix: correct interlacing for non-square images --- ciao/utils/calculations.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/ciao/utils/calculations.py b/ciao/utils/calculations.py index 379378a..616fe19 100644 --- a/ciao/utils/calculations.py +++ b/ciao/utils/calculations.py @@ -92,18 +92,19 @@ def get_replacement_image( replacement_image = mean_color.expand(-1, height, width) # [3, H, W] elif replacement == "interlacing": - # Create interlaced pattern: even columns flipped vertically, then even indices flipped horizontally + # Create interlaced pattern: even columns flipped vertically, then even rows flipped horizontally replacement_image = input_tensor.clone() - even_indices = torch.arange(0, height, 2) # Even row indices + even_row_indices = torch.arange(0, height, 2) # Even row indices + even_col_indices = torch.arange(0, width, 2) # Even column indices # Step 1: Flip even columns vertically (upside down) - replacement_image[:, :, even_indices] = torch.flip( - replacement_image[:, :, even_indices], dims=[1] + replacement_image[:, :, even_col_indices] = torch.flip( + replacement_image[:, :, even_col_indices], dims=[1] ) - # Step 2: Flip even indices horizontally (left-right) - replacement_image[:, even_indices, :] = torch.flip( - replacement_image[:, even_indices, :], dims=[2] + # Step 2: Flip even rows horizontally (left-right) + replacement_image[:, even_row_indices, :] = torch.flip( + replacement_image[:, even_row_indices, :], dims=[2] ) elif replacement == "blur": From 0401fe8fde7d16f2a216ed2e90c65796885a055d Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Thu, 26 Feb 2026 07:35:58 +0100 Subject: [PATCH 19/25] feat: implement data loading and preprocessing --- ciao/data/__init__.py | 6 ++++++ ciao/data/loader.py | 34 +++++++++++++++++++++++++++++++++ ciao/data/preprocessing.py | 39 ++++++++++++++++++++++++++++++++++++++ ciao/utils/segmentation.py | 4 ++-- 4 files changed, 81 insertions(+), 2 deletions(-) create mode 100644 ciao/data/__init__.py create mode 100644 ciao/data/loader.py create mode 100644 ciao/data/preprocessing.py diff --git a/ciao/data/__init__.py b/ciao/data/__init__.py new file mode 100644 index 0000000..0771aa1 --- /dev/null +++ b/ciao/data/__init__.py @@ -0,0 +1,6 @@ +"""Data loading utilities for CIAO.""" + +from ciao.data.loader import get_image_loader + + +__all__ = ["get_image_loader"] diff --git a/ciao/data/loader.py b/ciao/data/loader.py new file mode 100644 index 0000000..6c6ab6d --- /dev/null +++ b/ciao/data/loader.py @@ -0,0 +1,34 @@ +"""Simple image path loading utilities.""" + +from collections.abc import Iterator +from pathlib import Path +from typing import Any + + +def get_image_loader(config: Any) -> Iterator[Path]: + """Create image loader based on configuration. + + Args: + config: Hydra config object + + Returns: + Iterator of Path objects + + Raises: + ValueError: If neither image_path nor batch_path is specified + """ + if config.data.get("image_path"): + # Single image mode + yield Path(config.data.image_path) + + elif config.data.get("batch_path"): + # Directory mode + directory = Path(config.data.batch_path) + extensions = config.data.get( + "image_extensions", [".jpg", ".jpeg", ".png", ".bmp", ".webp"] + ) + for ext in extensions: + yield from directory.glob(f"**/*{ext}") + + else: + raise ValueError("Must specify either image_path or batch_path in config") diff --git a/ciao/data/preprocessing.py b/ciao/data/preprocessing.py new file mode 100644 index 0000000..594eae8 --- /dev/null +++ b/ciao/data/preprocessing.py @@ -0,0 +1,39 @@ +from pathlib import Path + +import torch +import torchvision.transforms as transforms +from PIL import Image + + +# ImageNet preprocessing transforms +preprocess = transforms.Compose( + [ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), + ] +) + + +def load_and_preprocess_image( + image_path: str | Path, device: torch.device | None = None +) -> tuple[torch.Tensor, Image.Image, torch.Tensor]: + """Load and preprocess an image for the model. + + Args: + image_path: Path to image file + device: Device to place tensor on (defaults to cuda if available, else cpu) + + Returns: + Tuple of (input_batch, original_image, input_tensor) + """ + if device is None: + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + image = Image.open(image_path).convert("RGB") + original_image = image.copy() + input_tensor = preprocess(image).to(device) # (3, 224, 224) - on correct device + input_batch = input_tensor.unsqueeze(0) # (1, 3, 224, 224) - already on device + + return input_batch, original_image, input_tensor diff --git a/ciao/utils/segmentation.py b/ciao/utils/segmentation.py index ccbb25d..2c66ee8 100644 --- a/ciao/utils/segmentation.py +++ b/ciao/utils/segmentation.py @@ -165,7 +165,7 @@ def build_fast_adjacency_list( def create_hexagonal_grid_with_list( input_tensor: torch.Tensor, hex_radius: int = 14 -) -> tuple[np.ndarray, tuple[tuple[int, ...], ...], int]: +) -> tuple[np.ndarray, tuple[tuple[int, ...], ...]]: _channels, height, width = input_tensor.shape segments = np.zeros((height, width), dtype=np.int32) @@ -188,7 +188,7 @@ def create_hexagonal_grid_with_list( # 2. Create fast graph (no NetworkX) adjacency_list = build_fast_adjacency_list(hex_to_id, next_id) - return segments, adjacency_list, next_id + return segments, adjacency_list def build_adjacency_bitmasks(adj_list: tuple[tuple[int, ...], ...]) -> tuple[int, ...]: From ecc8d3ad7126b9228df157b165df1f74f93b7553 Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Thu, 26 Feb 2026 07:39:08 +0100 Subject: [PATCH 20/25] feat: implement the explainer --- ciao/explainer/__init__.py | 6 + ciao/explainer/ciao_explainer.py | 271 +++++++++++++++++++++++++++++++ 2 files changed, 277 insertions(+) create mode 100644 ciao/explainer/__init__.py create mode 100644 ciao/explainer/ciao_explainer.py diff --git a/ciao/explainer/__init__.py b/ciao/explainer/__init__.py new file mode 100644 index 0000000..383e505 --- /dev/null +++ b/ciao/explainer/__init__.py @@ -0,0 +1,6 @@ +"""CIAO explainer implementation.""" + +from ciao.explainer.ciao_explainer import CIAOExplainer + + +__all__ = ["CIAOExplainer"] diff --git a/ciao/explainer/ciao_explainer.py b/ciao/explainer/ciao_explainer.py new file mode 100644 index 0000000..5658ed2 --- /dev/null +++ b/ciao/explainer/ciao_explainer.py @@ -0,0 +1,271 @@ +"""CIAO explainer implementation.""" + +from pathlib import Path +from typing import Any + +import torch + +from ciao.algorithm.lookahead_bitset import build_all_hyperpixels_greedy_lookahead +from ciao.algorithm.mcgs import build_all_hyperpixels_mcgs +from ciao.algorithm.mcts import build_all_hyperpixels_mcts +from ciao.algorithm.potential import build_all_hyperpixels_potential +from ciao.data.preprocessing import load_and_preprocess_image +from ciao.utils.calculations import ( + ModelPredictor, + calculate_scores_from_surrogate, + create_surrogate_dataset, + get_predicted_class, + select_top_hyperpixels, +) +from ciao.utils.segmentation import ( + build_adjacency_bitmasks, + create_hexagonal_grid_with_list, + create_segmentation, +) + + +class CIAOExplainer: + """CIAO (Contextual Importance Assessment via Obfuscation) Explainer. + + Generates explanations for image classification models by identifying + influential image regions using mutual information and greedy search. + """ + + def __init__(self) -> None: + """Initialize the CIAO explainer.""" + + def explain( + self, + image_path: str | Path, + predictor: ModelPredictor, + method: str = "lookahead", + target_class_idx: int | None = None, + segment_size: int = 4, + segmentation_type: str = "hexagonal", + max_hyperpixels: int = 10, + desired_length: int = 30, + batch_size: int = 64, + neighborhood: int = 8, + replacement: str = "mean_color", + replacement_kwargs: dict[str, Any] | None = None, + method_params: dict[str, Any] | None = None, + ) -> dict[str, Any]: + """Generate CIAO explanation for an image. + + Args: + image_path: Path to image or PIL Image object + predictor: ModelPredictor instance + method: Hyperpixel construction method. Options: + - "potential": Potential field guided search + - "mcts": Monte Carlo Tree Search + - "mc_rave": MC-RAVE (MCTS with RAVE heuristic) + - "lookahead": Optimized greedy lookahead with bitsets (default) + - "mcgs": Monte Carlo Graph Search + - "mcgs_rave": MCGS with RAVE + target_class_idx: Target class to explain (None = auto-select) + segment_size: Size of segments in pixels + segmentation_type: Type of segmentation ("hexagonal") + max_hyperpixels: Maximum number of hyperpixels to build + desired_length: Target number of segments per hyperpixel (default=30) + batch_size: Batch size for model evaluation + neighborhood: Adjacency neighborhood (6 or 8 for hexagonal) + replacement: Masking strategy for model evaluation + replacement_kwargs: Additional kwargs for replacement method + method_params: Dictionary of method-specific parameters: + + For "potential": + - num_simulations: int (default=50) - Number of simulations + + For "mcts": + - num_iterations: int (default=100) - MCTS iterations + - exploration_c: float (default=1.4) - UCT exploration constant + - mcts_batch_size: int (default=64) - Batch size for MCTS + + For "mc_rave": + - num_iterations: int (default=100) + - exploration_c: float (default=1.4) + - mcts_batch_size: int (default=64) + - rave_k: float (default=1000) + + For "lookahead": + - lookahead_distance: int (default=2) + + For "mcgs": + - num_iterations: int (default=100) + - mcts_batch_size: int (default=64) + - exploration_c: float (default=1.4) + + For "mcgs_rave": + - num_iterations: int (default=100) + - mcts_batch_size: int (default=64) + - exploration_c: float (default=1.4) + - rave_k: float (default=1000) + + Returns: + Dictionary containing: + - input_batch: Preprocessed input tensor + - target_class_idx: Class being explained + - segments: Segmentation map + - scores: Individual segment scores + - hyperpixels: List of all hyperpixels found + - top_hyperpixels: Top-k hyperpixels by score + - class_name: Human-readable class name + - performance_mode: Method identifier + """ + # Initialize method params with defaults + if method_params is None: + method_params = {} + + # Get class names from predictor + class_names = predictor.class_names + + # 1. Load and preprocess image (use same device as predictor's model) + input_batch, _original_image, input_tensor = load_and_preprocess_image( + image_path, device=predictor.device + ) + + # Handle replacement kwargs + if replacement_kwargs is None: + replacement_kwargs = {} + + predictor.replacement_image = predictor.get_replacement_image( + input_tensor, replacement, **replacement_kwargs + ).to(predictor.device) + + # 2. Get target class + if target_class_idx is None: + target_class_idx = get_predicted_class(predictor, input_batch) + print(f"Auto-selected target class: {target_class_idx}") + + # 3. Create segmentation + segments, graph = create_segmentation( + input_tensor, + segmentation_type=segmentation_type, + segment_size=segment_size, + neighborhood=neighborhood, + ) + print( + f"Built {segmentation_type} spatial graph with {graph.number_of_nodes()} " + f"segments and {graph.number_of_edges()} edges" + ) + + # Calculate scores from surrogate dataset + X, y = create_surrogate_dataset( + predictor, + input_batch, + segments, + graph, + target_class_idx, + batch_size=batch_size, + ) + scores = calculate_scores_from_surrogate(X, y) + + # Create adjacency structures (needed by all methods) + segments_list, adj_list = create_hexagonal_grid_with_list( + input_tensor, segment_size + ) + adj_masks = build_adjacency_bitmasks(adj_list) + + # Build hyperpixels based on method + if method == "potential": + hyperpixels = build_all_hyperpixels_potential( + predictor=predictor, + input_batch=input_batch, + segments=segments_list, + adj_masks=adj_masks, + target_class_idx=target_class_idx, + scores=scores, + max_hyperpixels=max_hyperpixels, + desired_length=desired_length, + num_simulations=method_params.get("num_simulations", 50), + batch_size=batch_size, + ) + + elif method in ["mcts", "mc_rave"]: + mode_str = "rave" if method == "mc_rave" else "standard" + + hyperpixels = build_all_hyperpixels_mcts( + predictor=predictor, + input_batch=input_batch, + segments=segments_list, + adj_masks=adj_masks, + target_class_idx=target_class_idx, + scores=scores, + max_hyperpixels=max_hyperpixels, + desired_length=desired_length, + num_iterations=method_params.get("num_iterations", 100), + mode=mode_str, + batch_size=method_params.get("mcts_batch_size", 64), + exploration_c=method_params.get("exploration_c", 1.4), + rave_k=method_params.get("rave_k", 1000), + ) + + elif method == "lookahead": + hyperpixels = build_all_hyperpixels_greedy_lookahead( + predictor=predictor, + input_batch=input_batch, + segments=segments_list, + adj_masks=adj_masks, + target_class_idx=target_class_idx, + scores=scores, + max_hyperpixels=max_hyperpixels, + desired_length=desired_length, + lookahead_distance=method_params.get("lookahead_distance", 2), + batch_size=batch_size, + ) + + elif method in ["mcgs", "mcgs_rave"]: + # Determine mode based on method + mode_str = "rave" if method == "mcgs_rave" else "standard" + + hyperpixels = build_all_hyperpixels_mcgs( + predictor=predictor, + input_batch=input_batch, + segments=segments_list, + adj_masks=adj_masks, + target_class_idx=target_class_idx, + scores=scores, + max_hyperpixels=max_hyperpixels, + desired_length=desired_length, + num_iterations=method_params.get("num_iterations", 100), + mode=mode_str, + batch_size=method_params.get("mcts_batch_size", 64), + exploration_c=method_params.get("exploration_c", 1.4), + rave_k=method_params.get("rave_k", 1000.0), + ) + + else: + raise ValueError( + f"Unknown method: {method}. Valid options: potential, mcts, " + f"mc_rave, lookahead, mcgs, mcgs_rave" + ) + + # Select top hyperpixels + top_hyperpixels = select_top_hyperpixels(hyperpixels, max_hyperpixels) + + print(f"Class name: {class_names[target_class_idx]}") + + # Return results + result = { + "input_batch": input_batch, + "target_class_idx": target_class_idx, + "segments": segments, + "scores": scores, + "hyperpixels": hyperpixels, + "top_hyperpixels": top_hyperpixels, + "class_name": class_names[target_class_idx] + if target_class_idx < len(class_names) + else f"Class {target_class_idx}", + "performance_mode": method, + } + return result + + # just a placeholder for now - can implement visualization later + def visualize( + self, + image: torch.Tensor, + explanation: dict[str, Any], + save_path: str | Path | None = None, + interactive: bool = True, + ) -> Any: + pass From 72d4aaf5401a0b5f7592be53e7066ed7169ff03f Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Thu, 26 Feb 2026 07:48:35 +0100 Subject: [PATCH 21/25] chore: uncomment CIAOExplainer from __init__ --- ciao/__init__.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/ciao/__init__.py b/ciao/__init__.py index 6ea1c58..a189294 100644 --- a/ciao/__init__.py +++ b/ciao/__init__.py @@ -4,4 +4,7 @@ Mutual Information and greedy feature selection. """ -# from ciao.explainer.ciao_explainer import CIAOExplainer +from ciao.explainer.ciao_explainer import CIAOExplainer + + +__all__ = ["CIAOExplainer"] From cb9eafe524a3721c672724d891dbe7b7ec5f8bc2 Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Thu, 26 Feb 2026 07:50:58 +0100 Subject: [PATCH 22/25] chore: add safety checks for virtual loss --- ciao/algorithm/mcgs.py | 8 ++++++++ ciao/algorithm/mcts.py | 14 ++++++++++++-- ciao/structures/nodes.py | 2 +- 3 files changed, 21 insertions(+), 3 deletions(-) diff --git a/ciao/algorithm/mcgs.py b/ciao/algorithm/mcgs.py index e2a0976..8c94e06 100644 --- a/ciao/algorithm/mcgs.py +++ b/ciao/algorithm/mcgs.py @@ -259,6 +259,10 @@ def backup_paths( for i, node in enumerate(path): # Release virtual loss on root if i == 0: # Root node + if node.pending <= 0: + raise RuntimeError( + f"Virtual loss underflow: root.pending={node.pending} (should be > 0)" + ) node.pending -= 1 # Update node statistics @@ -318,6 +322,10 @@ def backup_paths_rave( for i, node in enumerate(path): # Release virtual loss on root if i == 0: # Root node + if node.pending <= 0: + raise RuntimeError( + f"Virtual loss underflow: root.pending={node.pending} (should be > 0)" + ) node.pending -= 1 # --- STANDARD BACKUP --- diff --git a/ciao/algorithm/mcts.py b/ciao/algorithm/mcts.py index 6f7e0f7..b56114d 100644 --- a/ciao/algorithm/mcts.py +++ b/ciao/algorithm/mcts.py @@ -223,7 +223,12 @@ def backup_paths(batch_paths: list[list[MCTSNode]], rewards: list[float]) -> Non """ for path, reward in zip(batch_paths, rewards, strict=True): for node in path: - node.pending -= 1 # Release virtual loss + # Release virtual loss + if node.pending <= 0: + raise RuntimeError( + f"Virtual loss underflow: node.pending={node.pending} (should be > 0)" + ) + node.pending -= 1 node.visits += 1 node.value_sum += reward # Mean tracking node.max_value = max(node.max_value, reward) # MAX backup @@ -264,7 +269,12 @@ def backup_paths_rave( for node in path: # --- STANDARD BACKUP --- - node.pending -= 1 # Release virtual loss + # Release virtual loss + if node.pending <= 0: + raise RuntimeError( + f"Virtual loss underflow: node.pending={node.pending} (should be > 0)" + ) + node.pending -= 1 node.visits += 1 node.value_sum += reward # Mean tracking node.max_value = max(node.max_value, reward) # MAX backup diff --git a/ciao/structures/nodes.py b/ciao/structures/nodes.py index 3c396b5..b9a828d 100644 --- a/ciao/structures/nodes.py +++ b/ciao/structures/nodes.py @@ -43,7 +43,7 @@ def __init__(self, mask: int): self.visits = 0 self.value_sum = 0.0 self.max_value = -float("inf") - self.pending = 0 # virtual loss counter (for non-RAVE modes) + self.pending = 0 # virtual loss counter def init_edge(self, action: int) -> None: if action not in self.edge_stats: From eb626bb8ad1ac98432c3263e0e1c9b4438891684 Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Thu, 26 Feb 2026 08:16:00 +0100 Subject: [PATCH 23/25] chore: add type annotation to replacement_image --- ciao/utils/calculations.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ciao/utils/calculations.py b/ciao/utils/calculations.py index 616fe19..9f7cb47 100644 --- a/ciao/utils/calculations.py +++ b/ciao/utils/calculations.py @@ -17,7 +17,7 @@ def __init__(self, model: torch.nn.Module, class_names: list[str]) -> None: self.model = model self.class_names = class_names self.device = next(model.parameters()).device - self.replacement_image = None + self.replacement_image: torch.Tensor | None = None # Pre-compute normalization constants self.imagenet_mean = ( From 1ee139bbd7617486a76e3d317301f1ba48e18956 Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Thu, 26 Feb 2026 09:08:16 +0100 Subject: [PATCH 24/25] feat: enhance CIAO explainer with input validation and logging; add ModelPredictor to __all__; improve image loader with directory checks; add graph to adjacency list conversion --- ciao/__init__.py | 3 +- ciao/data/loader.py | 5 ++ ciao/explainer/ciao_explainer.py | 85 +++++++++++++++++++++++++++----- ciao/structures/bitmask_graph.py | 12 +++++ ciao/structures/nodes.py | 4 +- ciao/utils/segmentation.py | 21 ++++++++ 6 files changed, 114 insertions(+), 16 deletions(-) diff --git a/ciao/__init__.py b/ciao/__init__.py index a189294..5328594 100644 --- a/ciao/__init__.py +++ b/ciao/__init__.py @@ -5,6 +5,7 @@ """ from ciao.explainer.ciao_explainer import CIAOExplainer +from ciao.utils.calculations import ModelPredictor -__all__ = ["CIAOExplainer"] +__all__ = ["CIAOExplainer", "ModelPredictor"] diff --git a/ciao/data/loader.py b/ciao/data/loader.py index 6c6ab6d..285f0ad 100644 --- a/ciao/data/loader.py +++ b/ciao/data/loader.py @@ -24,6 +24,11 @@ def get_image_loader(config: Any) -> Iterator[Path]: elif config.data.get("batch_path"): # Directory mode directory = Path(config.data.batch_path) + if not directory.is_dir(): + raise ValueError( + f"batch_path must be a valid directory, got: {directory}. " + "Check for typos or incorrect path configuration." + ) extensions = config.data.get( "image_extensions", [".jpg", ".jpeg", ".png", ".bmp", ".webp"] ) diff --git a/ciao/explainer/ciao_explainer.py b/ciao/explainer/ciao_explainer.py index 5658ed2..1c20b91 100644 --- a/ciao/explainer/ciao_explainer.py +++ b/ciao/explainer/ciao_explainer.py @@ -1,5 +1,6 @@ """CIAO explainer implementation.""" +import logging from pathlib import Path from typing import Any @@ -19,11 +20,14 @@ ) from ciao.utils.segmentation import ( build_adjacency_bitmasks, - create_hexagonal_grid_with_list, create_segmentation, + graph_to_adjacency_list, ) +logger = logging.getLogger(__name__) + + class CIAOExplainer: """CIAO (Contextual Importance Assessment via Obfuscation) Explainer. @@ -112,6 +116,38 @@ def explain( - class_name: Human-readable class name - performance_mode: Method identifier """ + # Input validation + valid_methods = [ + "potential", + "mcts", + "mc_rave", + "lookahead", + "mcgs", + "mcgs_rave", + ] + if method not in valid_methods: + raise ValueError( + f"Invalid method: {method}. Valid options are: {', '.join(valid_methods)}" + ) + + if max_hyperpixels <= 0: + raise ValueError(f"max_hyperpixels must be positive, got {max_hyperpixels}") + + if desired_length <= 0: + raise ValueError(f"desired_length must be positive, got {desired_length}") + + if batch_size <= 0: + raise ValueError(f"batch_size must be positive, got {batch_size}") + + if segment_size <= 0: + raise ValueError(f"segment_size must be positive, got {segment_size}") + + if segmentation_type not in ["hexagonal", "square"]: + raise ValueError( + f"Invalid segmentation_type: {segmentation_type}. " + "Valid options are: 'hexagonal', 'square'" + ) + # Initialize method params with defaults if method_params is None: method_params = {} @@ -135,7 +171,17 @@ def explain( # 2. Get target class if target_class_idx is None: target_class_idx = get_predicted_class(predictor, input_batch) - print(f"Auto-selected target class: {target_class_idx}") + logger.info(f"Auto-selected target class: {target_class_idx}") + else: + # Validate target_class_idx if provided + num_classes = len(class_names) if class_names else None + if num_classes and ( + target_class_idx >= num_classes or target_class_idx < 0 + ): + raise ValueError( + f"target_class_idx {target_class_idx} is out of range. " + f"Model has {num_classes} classes (indices 0-{num_classes - 1})" + ) # 3. Create segmentation segments, graph = create_segmentation( @@ -144,7 +190,7 @@ def explain( segment_size=segment_size, neighborhood=neighborhood, ) - print( + logger.info( f"Built {segmentation_type} spatial graph with {graph.number_of_nodes()} " f"segments and {graph.number_of_edges()} edges" ) @@ -161,9 +207,9 @@ def explain( scores = calculate_scores_from_surrogate(X, y) # Create adjacency structures (needed by all methods) - segments_list, adj_list = create_hexagonal_grid_with_list( - input_tensor, segment_size - ) + # Use the same segmentation for consistency between scoring and search + num_segments = graph.number_of_nodes() + adj_list = graph_to_adjacency_list(graph, num_segments) adj_masks = build_adjacency_bitmasks(adj_list) # Build hyperpixels based on method @@ -171,7 +217,7 @@ def explain( hyperpixels = build_all_hyperpixels_potential( predictor=predictor, input_batch=input_batch, - segments=segments_list, + segments=segments, adj_masks=adj_masks, target_class_idx=target_class_idx, scores=scores, @@ -187,7 +233,7 @@ def explain( hyperpixels = build_all_hyperpixels_mcts( predictor=predictor, input_batch=input_batch, - segments=segments_list, + segments=segments, adj_masks=adj_masks, target_class_idx=target_class_idx, scores=scores, @@ -204,7 +250,7 @@ def explain( hyperpixels = build_all_hyperpixels_greedy_lookahead( predictor=predictor, input_batch=input_batch, - segments=segments_list, + segments=segments, adj_masks=adj_masks, target_class_idx=target_class_idx, scores=scores, @@ -221,7 +267,7 @@ def explain( hyperpixels = build_all_hyperpixels_mcgs( predictor=predictor, input_batch=input_batch, - segments=segments_list, + segments=segments, adj_masks=adj_masks, target_class_idx=target_class_idx, scores=scores, @@ -243,7 +289,7 @@ def explain( # Select top hyperpixels top_hyperpixels = select_top_hyperpixels(hyperpixels, max_hyperpixels) - print(f"Class name: {class_names[target_class_idx]}") + logger.info(f"Class name: {class_names[target_class_idx]}") # Return results result = { @@ -260,7 +306,6 @@ def explain( } return result - # just a placeholder for now - can implement visualization later def visualize( self, image: torch.Tensor, @@ -268,4 +313,18 @@ def visualize( save_path: str | Path | None = None, interactive: bool = True, ) -> Any: - pass + """Visualize explanation results. + + Args: + image: Input image tensor + explanation: Explanation dictionary from explain() + save_path: Optional path to save visualization + interactive: Whether to display interactive visualization + + Raises: + NotImplementedError: This method is not yet implemented + """ + raise NotImplementedError( + "The visualize method is not yet implemented. " + "Use external visualization tools or implement custom visualization based on the explanation data." + ) diff --git a/ciao/structures/bitmask_graph.py b/ciao/structures/bitmask_graph.py index fcb7e76..3452e2b 100644 --- a/ciao/structures/bitmask_graph.py +++ b/ciao/structures/bitmask_graph.py @@ -21,10 +21,22 @@ def iter_bits(mask: int) -> Iterator[int]: Yields node IDs in arbitrary order (depends on bit positions). Performance: O(k) where k is the number of set bits. + Args: + mask: Non-negative integer bitmask + + Raises: + ValueError: If mask is negative + Example: mask = 0b10110 # bits 1, 2, 4 are set list(iter_bits(mask)) # [1, 2, 4] """ + if mask < 0: + raise ValueError( + f"mask must be non-negative, got {mask}. " + "Negative masks cause infinite loops due to two's complement representation." + ) + temp = mask while temp: low_bit = temp & -temp diff --git a/ciao/structures/nodes.py b/ciao/structures/nodes.py index b9a828d..6db47c4 100644 --- a/ciao/structures/nodes.py +++ b/ciao/structures/nodes.py @@ -1,9 +1,9 @@ -from typing import Optional +from __future__ import annotations class MCTSNode: def __init__( - self, mask: int, parent: Optional["MCTSNode"] = None, prior_score: float = 0.0 + self, mask: int, parent: MCTSNode | None = None, prior_score: float = 0.0 ): self.mask = mask self.parent = parent diff --git a/ciao/utils/segmentation.py b/ciao/utils/segmentation.py index 2c66ee8..133d0d9 100644 --- a/ciao/utils/segmentation.py +++ b/ciao/utils/segmentation.py @@ -272,6 +272,27 @@ def create_hexagonal_grid( return segments, adjacency_graph +def graph_to_adjacency_list( + graph: nx.Graph, num_segments: int +) -> tuple[tuple[int, ...], ...]: + """Convert NetworkX graph to adjacency list format. + + Args: + graph: NetworkX graph with segment IDs as nodes + num_segments: Total number of segments + + Returns: + Tuple of tuples where adj_list[i] contains neighbors of segment i + """ + temp_adj: list[list[int]] = [[] for _ in range(num_segments)] + + for node in graph.nodes(): + for neighbor in graph.neighbors(node): + temp_adj[node].append(neighbor) + + return tuple(tuple(sorted(neighbors)) for neighbors in temp_adj) + + def create_segmentation( input_tensor: torch.Tensor, segmentation_type: str = "hexagonal", From f919ec9670c4bb4b7dc148bda5b90bf02e913227 Mon Sep 17 00:00:00 2001 From: dhalmazna Date: Thu, 26 Feb 2026 09:13:12 +0100 Subject: [PATCH 25/25] feat: update CIAOExplainer to replace replacement_kwargs with color parameter for solid_color mode --- ciao/explainer/ciao_explainer.py | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/ciao/explainer/ciao_explainer.py b/ciao/explainer/ciao_explainer.py index 1c20b91..1cba9d8 100644 --- a/ciao/explainer/ciao_explainer.py +++ b/ciao/explainer/ciao_explainer.py @@ -51,7 +51,7 @@ def explain( batch_size: int = 64, neighborhood: int = 8, replacement: str = "mean_color", - replacement_kwargs: dict[str, Any] | None = None, + color: tuple[int, int, int] = (0, 0, 0), method_params: dict[str, Any] | None = None, ) -> dict[str, Any]: """Generate CIAO explanation for an image. @@ -74,7 +74,7 @@ def explain( batch_size: Batch size for model evaluation neighborhood: Adjacency neighborhood (6 or 8 for hexagonal) replacement: Masking strategy for model evaluation - replacement_kwargs: Additional kwargs for replacement method + color: RGB color tuple (0-255) for solid_color replacement mode method_params: Dictionary of method-specific parameters: For "potential": @@ -160,12 +160,8 @@ def explain( image_path, device=predictor.device ) - # Handle replacement kwargs - if replacement_kwargs is None: - replacement_kwargs = {} - predictor.replacement_image = predictor.get_replacement_image( - input_tensor, replacement, **replacement_kwargs + input_tensor, replacement, color ).to(predictor.device) # 2. Get target class