From e3fdd47393a77eed29dd3a5de02bfc6bb1366723 Mon Sep 17 00:00:00 2001 From: Magdalena Kotynia Date: Tue, 12 Aug 2025 10:01:28 +0200 Subject: [PATCH 01/17] feat: interfaces for quantity and multiple choice tasks --- .../rai_bench/vlm_benchmark/interfaces.py | 2 +- .../rai_bench/vlm_benchmark/tasks/tasks.py | 136 +++++++++++++++++- 2 files changed, 134 insertions(+), 4 deletions(-) diff --git a/src/rai_bench/rai_bench/vlm_benchmark/interfaces.py b/src/rai_bench/rai_bench/vlm_benchmark/interfaces.py index 97f769b93..2b4916535 100644 --- a/src/rai_bench/rai_bench/vlm_benchmark/interfaces.py +++ b/src/rai_bench/rai_bench/vlm_benchmark/interfaces.py @@ -118,7 +118,7 @@ def get_prompt(self) -> str: pass @abstractmethod - def validate(self, output: BaseModelT) -> bool: + def validate(self, output: BaseModelT) -> float: """Validate result of the task.""" pass diff --git a/src/rai_bench/rai_bench/vlm_benchmark/tasks/tasks.py b/src/rai_bench/rai_bench/vlm_benchmark/tasks/tasks.py index 639b50400..ad48a01bd 100644 --- a/src/rai_bench/rai_bench/vlm_benchmark/tasks/tasks.py +++ b/src/rai_bench/rai_bench/vlm_benchmark/tasks/tasks.py @@ -14,7 +14,7 @@ import logging -from typing import List +from typing import List, Type from pydantic import BaseModel, Field from rai.messages import preprocess_image @@ -31,6 +31,20 @@ class BoolAnswerWithJustification(BaseModel): justification: str +class QuantityAnswerWithJustification(BaseModel): + """A quantity answer to the user question along with justification for the answer.""" + + answer: int + justification: str + + +class MultipleChoiceAnswerWithJustification(BaseModel): + """A multiple choice answer to the user question along with justification for the answer.""" + + answer: List[str] + justification: str + + class BoolImageTaskInput(BaseModel): question: str = Field(..., description="The question to be answered.") images_paths: List[str] = Field( @@ -42,6 +56,38 @@ class BoolImageTaskInput(BaseModel): ) +class QuantityImageTaskInput(BaseModel): + """Input for a task that requires counting objects in an image.""" + + question: str = Field(..., description="The question to be answered.") + images_paths: List[str] = Field( + ..., + description="List of image file paths to be used for answering the question.", + ) + expected_answer: int = Field( + ..., + description="The expected number of objects to be counted in the image.", + ) + + +class MultipleChoiceImageTaskInput(BaseModel): + """Input for a task that requires selecting one or more answers from a list of options.""" + + question: str = Field(..., description="The question to be answered.") + images_paths: List[str] = Field( + ..., + description="List of image file paths to be used for answering the question.", + ) + options: List[str] = Field( + ..., + description="List of possible answers to the question.", + ) + expected_answer: List[str] = Field( + ..., + description="The expected answer to the question being a list of strings chosen from the options.", + ) + + class BoolImageTask(ImageReasoningTask[BoolAnswerWithJustification]): complexity = "easy" @@ -72,5 +118,89 @@ def get_images(self): images = [preprocess_image(image_path) for image_path in self.images_paths] return images - def validate(self, output: BoolAnswerWithJustification) -> bool: - return output.answer == self.expected_answer + def validate(self, output: BoolAnswerWithJustification) -> float: + return float(output.answer == self.expected_answer) + + +class QuantityImageTask(ImageReasoningTask[QuantityAnswerWithJustification]): + """A task that requires counting objects in an image.""" + + complexity = "medium" + + def __init__( + self, + task_input: QuantityImageTaskInput, + logger: loggers_type | None = None, + ) -> None: + super().__init__(logger=logger) + self.question = task_input.question + self.images_paths = task_input.images_paths + self.expected_answer = task_input.expected_answer + + @property + def type(self) -> str: + return "quantity_response_image_task" + + @property + def structured_output(self) -> Type[QuantityAnswerWithJustification]: + return QuantityAnswerWithJustification + + def validate(self, output: QuantityAnswerWithJustification) -> float: + return float(output.answer == self.expected_answer) + + def get_prompt(self) -> str: + return self.question + + def get_images(self): + images = [preprocess_image(image_path) for image_path in self.images_paths] + return images + + +class MultipleChoiceImageTask( + ImageReasoningTask[MultipleChoiceAnswerWithJustification] +): + """A task that requires selecting one or more answers from a set of options.""" + + complexity = "hard" + + def __init__( + self, + task_input: MultipleChoiceImageTaskInput, + logger: loggers_type | None = None, + ) -> None: + super().__init__(logger=logger) + self.question = task_input.question + self.images_paths = task_input.images_paths + self.options = task_input.options + self.expected_answer = task_input.expected_answer + + @property + def type(self) -> str: + return "multiple_choice_response_image_task" + + @property + def structured_output(self) -> Type[MultipleChoiceAnswerWithJustification]: + return MultipleChoiceAnswerWithJustification + + def validate(self, output: MultipleChoiceAnswerWithJustification) -> float: + answers_processed = set([answer.casefold() for answer in output.answer]) + expected_processed = set([answer.casefold() for answer in self.expected_answer]) + + if not answers_processed.issubset(expected_processed): + return 0.0 + + correct_count = len(answers_processed.intersection(expected_processed)) + total_expected = len(expected_processed) + + return float(correct_count / total_expected) if total_expected > 0 else 0.0 + + def get_prompt(self) -> str: + return ( + self.question + + " Choose one or more answers from the options: " + + ", ".join(self.options) + ) + + def get_images(self): + images = [preprocess_image(image_path) for image_path in self.images_paths] + return images From 143e7c3a475690869d45f50c4a0a3ed85538234e Mon Sep 17 00:00:00 2001 From: Magdalena Kotynia Date: Tue, 12 Aug 2025 10:07:09 +0200 Subject: [PATCH 02/17] feat: created new tasks with warehouse simulation images --- .gitattributes | 9 + .../predefined/images/damaged_boxes.png | 3 + .../predefined/images/fallen_barrels_1.png | 3 + .../predefined/images/fallen_barrels_2.png | 3 + .../predefined/images/fallen_barrels_3.png | 3 + .../predefined/images/fallen_human_1.png | 3 + .../predefined/images/fallen_human_2.png | 3 + .../predefined/images/fallen_human_3.png | 3 + .../predefined/images/gauge_level.png | 3 + .../predefined/images/woman_wo_helmet_1.png | 3 + .../predefined/images/woman_wo_helmet_2.png | 3 + .../vlm_benchmark/predefined/tasks.py | 288 +++++++++++++++++- 12 files changed, 325 insertions(+), 2 deletions(-) create mode 100644 .gitattributes create mode 100644 src/rai_bench/rai_bench/vlm_benchmark/predefined/images/damaged_boxes.png create mode 100644 src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_barrels_1.png create mode 100644 src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_barrels_2.png create mode 100644 src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_barrels_3.png create mode 100644 src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_human_1.png create mode 100644 src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_human_2.png create mode 100644 src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_human_3.png create mode 100644 src/rai_bench/rai_bench/vlm_benchmark/predefined/images/gauge_level.png create mode 100644 src/rai_bench/rai_bench/vlm_benchmark/predefined/images/woman_wo_helmet_1.png create mode 100644 src/rai_bench/rai_bench/vlm_benchmark/predefined/images/woman_wo_helmet_2.png diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 000000000..6e9f68dae --- /dev/null +++ b/.gitattributes @@ -0,0 +1,9 @@ +*.png filter=lfs diff=lfs merge=lfs -text +*.jpg filter=lfs diff=lfs merge=lfs -text +*.jpeg filter=lfs diff=lfs merge=lfs -text +*.gif filter=lfs diff=lfs merge=lfs -text +*.webp filter=lfs diff=lfs merge=lfs -text +*.bmp filter=lfs diff=lfs merge=lfs -text +*.tif filter=lfs diff=lfs merge=lfs -text +*.tiff filter=lfs diff=lfs merge=lfs -text +*.svg filter=lfs diff=lfs merge=lfs -text diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/damaged_boxes.png b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/damaged_boxes.png new file mode 100644 index 000000000..8acad7451 --- /dev/null +++ b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/damaged_boxes.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:edd337c10f0c662bfedbd6a5137d6e52decc7d2937134b222d76f7f34f588228 +size 2506357 diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_barrels_1.png b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_barrels_1.png new file mode 100644 index 000000000..9515b3224 --- /dev/null +++ b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_barrels_1.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ece7ce624db2cbe081eacd503de97501b303a2c38c6d2902fefdd577d1046ebd +size 497598 diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_barrels_2.png b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_barrels_2.png new file mode 100644 index 000000000..1137aa7f5 --- /dev/null +++ b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_barrels_2.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:389dc7b475f338bd4bce0aa477127e4e601075d2e5a545cdc414f506c78e0a0f +size 1139409 diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_barrels_3.png b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_barrels_3.png new file mode 100644 index 000000000..067a2cfe3 --- /dev/null +++ b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_barrels_3.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9fd24939958439f2d2b193ef98306382b0eb4700be22ddf964dacf422043eccc +size 232378 diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_human_1.png b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_human_1.png new file mode 100644 index 000000000..8ff32a372 --- /dev/null +++ b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_human_1.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d515a1ebf1849ccdc7d3467c5eaf4483d8300fb672a56a928ea074b55ab8f384 +size 510974 diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_human_2.png b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_human_2.png new file mode 100644 index 000000000..90ce93490 --- /dev/null +++ b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_human_2.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:16a84ef5c9a4ec76c22b665c97be9c17610dee314bd9cfcba087991a8fcc6415 +size 1195412 diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_human_3.png b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_human_3.png new file mode 100644 index 000000000..28e23b70b --- /dev/null +++ b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_human_3.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:44cd817356eb6fc8bd90e4f4ad04a31f8ba2a7c441f8fa2433c24188bd42969d +size 222920 diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/gauge_level.png b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/gauge_level.png new file mode 100644 index 000000000..4a91c9354 --- /dev/null +++ b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/gauge_level.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6d158ca524cb0faa8a3bed64ae46f05f488a9597abd332a9810d800757aaeddc +size 441519 diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/woman_wo_helmet_1.png b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/woman_wo_helmet_1.png new file mode 100644 index 000000000..a254669d5 --- /dev/null +++ b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/woman_wo_helmet_1.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:923a9ab71e68715d7c927e5c218ba406cc47f37e6145ccf4d3dc19512d040fe9 +size 6205972 diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/woman_wo_helmet_2.png b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/woman_wo_helmet_2.png new file mode 100644 index 000000000..91cf34dec --- /dev/null +++ b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/woman_wo_helmet_2.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:09ea1c58a154bc565bb74f2c91f2cec6c95613a5772c65eb99cdd86bf4982bed +size 679496 diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/tasks.py b/src/rai_bench/rai_bench/vlm_benchmark/predefined/tasks.py index bd1d9cd61..b9810e4ad 100644 --- a/src/rai_bench/rai_bench/vlm_benchmark/predefined/tasks.py +++ b/src/rai_bench/rai_bench/vlm_benchmark/predefined/tasks.py @@ -17,7 +17,14 @@ from pydantic import BaseModel from rai_bench.vlm_benchmark.interfaces import ImageReasoningTask -from rai_bench.vlm_benchmark.tasks.tasks import BoolImageTask, BoolImageTaskInput +from rai_bench.vlm_benchmark.tasks.tasks import ( + BoolImageTask, + BoolImageTaskInput, + MultipleChoiceImageTask, + MultipleChoiceImageTaskInput, + QuantityImageTask, + QuantityImageTaskInput, +) IMG_PATH = "src/rai_bench/rai_bench/vlm_benchmark/predefined/images/" true_response_inputs: List[BoolImageTaskInput] = [ @@ -56,7 +63,38 @@ images_paths=[IMG_PATH + "image_7.jpg"], expected_answer=True, ), + BoolImageTaskInput( + question="Do you see something anomalous in the image?", + images_paths=[IMG_PATH + "fallen_human_3.png"], + expected_answer=True, + ), + BoolImageTaskInput( + question="Do you see something anomalous in the image?", + images_paths=[IMG_PATH + "fallen_human_2.png"], + expected_answer=True, + ), + BoolImageTaskInput( + question="Do you see something anomalous in the image?", + images_paths=[IMG_PATH + "fallen_human_1.png"], + expected_answer=True, + ), + BoolImageTaskInput( + question="Is gauge level high?", + images_paths=[IMG_PATH + "gauge_level.png"], + expected_answer=True, + ), + BoolImageTaskInput( + question="Is worker wearing a vest?", + images_paths=[IMG_PATH + "woman_wo_helmet_2.png"], + expected_answer=True, + ), + BoolImageTaskInput( + question="Is any person in the image?", + images_paths=[IMG_PATH + "fallen_barrels_1.png"], + expected_answer=True, + ), ] + false_response_inputs: List[BoolImageTaskInput] = [ BoolImageTaskInput( question="Is the door open?", @@ -93,6 +131,237 @@ images_paths=[IMG_PATH + "image_7.jpg"], expected_answer=False, ), + BoolImageTaskInput( + question="Is worker wearing a helmet?", + images_paths=[IMG_PATH + "woman_wo_helmet_1.png"], + expected_answer=False, + ), + BoolImageTaskInput( + question="Is worker wearing a helmet?", + images_paths=[IMG_PATH + "woman_wo_helmet_2.png"], + expected_answer=False, + ), + BoolImageTaskInput( + question="Is gauge level low?", + images_paths=[IMG_PATH + "gauge_level.png"], + expected_answer=False, + ), +] + +quantity_response_inputs: List[QuantityImageTaskInput] = [ + QuantityImageTaskInput( + question="How many barrels are in the image?", + images_paths=[IMG_PATH + "fallen_barrels_1.png"], + expected_answer=6, + ), + QuantityImageTaskInput( + question="How many fallen barrels are in the image?", + images_paths=[IMG_PATH + "fallen_barrels_1.png"], + expected_answer=2, + ), + QuantityImageTaskInput( + question="How many fallen barrels are in the image?", + images_paths=[IMG_PATH + "fallen_barrels_2.png"], + expected_answer=1, + ), + QuantityImageTaskInput( + question="How many fallen barrels are in the image?", + images_paths=[IMG_PATH + "fallen_barrels_3.png"], + expected_answer=1, + ), + QuantityImageTaskInput( + question="How many barrels are in the image?", + images_paths=[IMG_PATH + "fallen_barrels_3.png"], + expected_answer=6, + ), + QuantityImageTaskInput( + question="How many people are in the image?", + images_paths=[IMG_PATH + "fallen_barrels_2.png"], + expected_answer=0, + ), + QuantityImageTaskInput( + question="How many people are in the image?", + images_paths=[IMG_PATH + "fallen_human_1.png"], + expected_answer=1, + ), + QuantityImageTaskInput( + question="How many people are in the image?", + images_paths=[IMG_PATH + "fallen_human_2.png"], + expected_answer=1, + ), + QuantityImageTaskInput( + question="How many people are in the image?", + images_paths=[IMG_PATH + "fallen_human_3.png"], + expected_answer=1, + ), + QuantityImageTaskInput( + question="How many barrels are in the image?", + images_paths=[IMG_PATH + "gauge_level.png"], + expected_answer=4, + ), + QuantityImageTaskInput( + question="How many damaged boxes are in the image?", + images_paths=[IMG_PATH + "damaged_boxes.png"], + expected_answer=2, + ), + QuantityImageTaskInput( + question="How many pictures are on the wall?", + images_paths=[IMG_PATH + "image_3.jpg"], + expected_answer=3, + ), + QuantityImageTaskInput( + question="How many pictures are on the wall?", + images_paths=[IMG_PATH + "image_4.jpg"], + expected_answer=3, + ), + QuantityImageTaskInput( + question="How many pictures are on the wall?", + images_paths=[IMG_PATH + "image_6.jpg"], + expected_answer=2, + ), + QuantityImageTaskInput( + question="How many windows are visible in the image?", + images_paths=[IMG_PATH + "image_2.jpg"], + expected_answer=2, + ), +] + +multiple_choice_response_inputs: List[MultipleChoiceImageTaskInput] = [ + MultipleChoiceImageTaskInput( + question="What is in the image?", + images_paths=[IMG_PATH + "fallen_barrels_2.png"], + options=[ + "sofa", + "gauge", + "bed", + "armchair", + "barrels", + "lamp with a shade", + "door", + "boxes", + "human", + "desk", + "plant", + "roll container", + "dresser", + ], + expected_answer=["barrels", "boxes", "roll container"], + ), + MultipleChoiceImageTaskInput( + question="What is in the image?", + images_paths=[IMG_PATH + "fallen_human_1.png"], + options=[ + "sofa", + "gauge", + "bed", + "armchair", + "barrels", + "lamp with a shade", + "door", + "boxes", + "human", + "desk", + "plant", + "roll container", + "dresser", + ], + expected_answer=["human", "boxes"], + ), + MultipleChoiceImageTaskInput( + question="What is in the image?", + images_paths=[IMG_PATH + "gauge_level.png"], + options=[ + "sofa", + "gauge", + "bed", + "armchair", + "barrels", + "lamp with a shade", + "door", + "boxes", + "human", + "desk", + "plant", + "roll container", + "dresser", + ], + expected_answer=["gauge", "barrels"], + ), + MultipleChoiceImageTaskInput( + question="What is in the image?", + images_paths=[IMG_PATH + "fallen_human_2.png"], + options=[ + "sofa", + "gauge", + "bed", + "armchair", + "barrels", + "lamp with a shade", + "door", + "boxes", + "human", + "desk", + "plant", + "roll container", + "dresser", + ], + expected_answer=["boxes", "human", "roll container"], + ), + MultipleChoiceImageTaskInput( + question="What is in the image?", + images_paths=[IMG_PATH + "image_1.jpg"], + options=[ + "gauge", + "bed", + "barrels", + "lamp with a shade", + "door", + "boxes", + "human", + "desk", + "plant", + "roll container", + ], + expected_answer=["bed", "lamp with a shade", "door", "desk"], + ), + MultipleChoiceImageTaskInput( + question="What is in the image?", + images_paths=[IMG_PATH + "image_4.jpg"], + options=[ + "sofa", + "gauge", + "bed", + "armchair", + "barrels", + "lamp with a shade", + "door", + "boxes", + "human", + "desk", + "plant", + "roll container", + "dresser", + "TV", + ], + expected_answer=["sofa", "plant", "TV", "dresser"], + ), + MultipleChoiceImageTaskInput( + question="What is in the image?", + images_paths=[IMG_PATH + "image_7.jpg"], + options=[ + "sofa", + "gauge", + "armchair", + "barrels", + "door", + "boxes", + "human", + "desk", + "plant", + "roll container", + ], + expected_answer=["sofa", "armchair", "door", "desk", "plant"], + ), ] @@ -109,4 +378,19 @@ def get_spatial_tasks() -> List[ImageReasoningTask[BaseModel]]: ) for input_item in false_response_inputs ] - return cast(List[ImageReasoningTask[BaseModel]], true_tasks + false_tasks) + quantity_tasks = [ + QuantityImageTask( + task_input=input_item, + ) + for input_item in quantity_response_inputs + ] + multiple_choice_tasks = [ + MultipleChoiceImageTask( + task_input=input_item, + ) + for input_item in multiple_choice_response_inputs + ] + return cast( + List[ImageReasoningTask[BaseModel]], + true_tasks + false_tasks + quantity_tasks + multiple_choice_tasks, + ) From e5652c040cec1326772180759d245d3bcef6ad84 Mon Sep 17 00:00:00 2001 From: Magdalena Kotynia Date: Tue, 12 Aug 2025 10:23:31 +0200 Subject: [PATCH 03/17] feat: added vlm bench config to test many models --- src/rai_bench/rai_bench/__init__.py | 2 ++ src/rai_bench/rai_bench/test_models.py | 29 ++++++++++++++++++++++++++ 2 files changed, 31 insertions(+) diff --git a/src/rai_bench/rai_bench/__init__.py b/src/rai_bench/rai_bench/__init__.py index 9ca566649..e5b2f0cdd 100644 --- a/src/rai_bench/rai_bench/__init__.py +++ b/src/rai_bench/rai_bench/__init__.py @@ -14,6 +14,7 @@ from .test_models import ( ManipulationO3DEBenchmarkConfig, ToolCallingAgentBenchmarkConfig, + VLMBenchmarkConfig, test_dual_agents, test_models, ) @@ -28,6 +29,7 @@ __all__ = [ "ManipulationO3DEBenchmarkConfig", "ToolCallingAgentBenchmarkConfig", + "VLMBenchmarkConfig", "define_benchmark_logger", "get_llm_for_benchmark", "parse_manipulation_o3de_benchmark_args", diff --git a/src/rai_bench/rai_bench/test_models.py b/src/rai_bench/rai_bench/test_models.py index 8be2db7e4..e5f7405a5 100644 --- a/src/rai_bench/rai_bench/test_models.py +++ b/src/rai_bench/rai_bench/test_models.py @@ -23,6 +23,7 @@ import rai_bench.manipulation_o3de as manipulation_o3de import rai_bench.tool_calling_agent as tool_calling_agent +import rai_bench.vlm_benchmark as vlm_benchmark from rai_bench.utils import ( define_benchmark_logger, get_llm_for_benchmark, @@ -77,6 +78,25 @@ def name(self) -> str: return "tool_calling_agent" +class VLMBenchmarkConfig(BenchmarkConfig): + complexities: List[Literal["easy", "medium", "hard"]] = ["easy", "medium", "hard"] + task_types: List[ + Literal[ + "bool_response_image_task", + "quantity_response_image_task", + "multiple_choice_image_task", + ] + ] = [ + "bool_response_image_task", + "quantity_response_image_task", + "multiple_choice_image_task", + ] + + @property + def name(self) -> str: + return "vlm" + + def test_dual_agents( multimodal_llms: List[BaseChatModel], tool_calling_models: List[BaseChatModel], @@ -211,6 +231,15 @@ def test_models( experiment_id=experiment_id, bench_logger=bench_logger, ) + + elif isinstance(bench_conf, VLMBenchmarkConfig): + vlm_tasks = vlm_benchmark.get_spatial_tasks() + vlm_benchmark.run_benchmark( + llm=llm, + out_dir=Path(curr_out_dir), + tasks=vlm_tasks, + bench_logger=bench_logger, + ) except Exception as e: bench_logger.critical(f"BENCHMARK RUN FAILED: {e}") bench_logger.critical( From 2b2e3609278d93f93cbbde20cb21a7bb6a96d06d Mon Sep 17 00:00:00 2001 From: Magdalena Kotynia Date: Tue, 12 Aug 2025 11:06:10 +0200 Subject: [PATCH 04/17] chore: moved old image files from vlm benchmark to lfs --- .../predefined/images/image_1.jpg | Bin 66195 -> 130 bytes .../predefined/images/image_2.jpg | Bin 67825 -> 130 bytes .../predefined/images/image_3.jpg | Bin 79685 -> 130 bytes .../predefined/images/image_4.jpg | Bin 76892 -> 130 bytes .../predefined/images/image_5.jpg | Bin 97851 -> 130 bytes 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z#S-XzB9w-0phK-$NZk6W%ujBlW8a$M{wn^(KN>tlv3PUhSM1NP>hPcw!XeQlyhzE) zxH2y1us3FP6u4Ie6vY?cGg9b9b#>*)_Gzgj1ssBo3R@ z0CF+_`hHcMvd1TWV?~)+d_Rf#PaVWcavhz9GQ{vOb5H)!xOT*DNj(S|HTrX{{5|-2 z;!EhYKN$Qo@UEpej6|0jj)QM2ssc{I=0?XEVsW15-mA;|JbVMYyEm5~0z5ChhCzeF z@kSSna1<(zJ-?Z(WtL5Sqg9y2^glK%gRv4^w(O`Naxi`W07|~M(hP5qd)MAqAF*%j zvhlT~e+&h*qZuvY3&6|HcnvWe{SR8m@hAKf@Ai4t1WjdUujxB?0@_}wyUfloTkSDQ aIL8>r-Z{@0=9XEAeOLAJHd%!Z%>UVDJ(qI; From d56699409e4199ab2226e91cbe3751debeac7497 Mon Sep 17 00:00:00 2001 From: Magdalena Kotynia Date: Tue, 12 Aug 2025 13:20:05 +0200 Subject: [PATCH 05/17] refactor: extracted common logic for vlm tasks inputs and answers to base ImageReasoningTaskInput and ImageReasoningAnswer classes --- .../rai_bench/vlm_benchmark/interfaces.py | 25 +++++++- .../rai_bench/vlm_benchmark/tasks/tasks.py | 59 +++++-------------- 2 files changed, 38 insertions(+), 46 deletions(-) diff --git a/src/rai_bench/rai_bench/vlm_benchmark/interfaces.py b/src/rai_bench/rai_bench/vlm_benchmark/interfaces.py index 2b4916535..6280ecce3 100644 --- a/src/rai_bench/rai_bench/vlm_benchmark/interfaces.py +++ b/src/rai_bench/rai_bench/vlm_benchmark/interfaces.py @@ -18,7 +18,7 @@ from langchain_core.messages import BaseMessage from langchain_core.runnables.config import DEFAULT_RECURSION_LIMIT -from pydantic import BaseModel, ConfigDict, ValidationError +from pydantic import BaseModel, ConfigDict, Field, ValidationError loggers_type = logging.Logger @@ -55,6 +55,29 @@ class TaskValidationError(Exception): pass +AnswerT = TypeVar("AnswerT") + + +class ImageReasoningTaskInput(BaseModel, Generic[AnswerT]): + """Base input for an image reasoning task.""" + + question: str = Field(..., description="The question to be answered.") + images_paths: List[str] = Field( + ..., + description="List of image file paths to be used for answering the question.", + ) + expected_answer: AnswerT = Field( + ..., description="The expected answer to the question." + ) + + +class ImageReasoningAnswer(BaseModel, Generic[AnswerT]): + """Base answer for an image reasoning task.""" + + answer: AnswerT = Field(..., description="The answer to the question.") + justification: str = Field(..., description="Justification for the answer.") + + class ImageReasoningTask(ABC, Generic[BaseModelT]): complexity: Literal["easy", "medium", "hard"] recursion_limit: int = DEFAULT_RECURSION_LIMIT diff --git a/src/rai_bench/rai_bench/vlm_benchmark/tasks/tasks.py b/src/rai_bench/rai_bench/vlm_benchmark/tasks/tasks.py index ad48a01bd..fe76a96f4 100644 --- a/src/rai_bench/rai_bench/vlm_benchmark/tasks/tasks.py +++ b/src/rai_bench/rai_bench/vlm_benchmark/tasks/tasks.py @@ -16,76 +16,45 @@ import logging from typing import List, Type -from pydantic import BaseModel, Field +from pydantic import Field from rai.messages import preprocess_image -from rai_bench.vlm_benchmark.interfaces import ImageReasoningTask +from rai_bench.vlm_benchmark.interfaces import ( + ImageReasoningAnswer, + ImageReasoningTask, + ImageReasoningTaskInput, +) loggers_type = logging.Logger -class BoolAnswerWithJustification(BaseModel): +class BoolAnswerWithJustification(ImageReasoningAnswer[bool]): """A boolean answer to the user question along with justification for the answer.""" - answer: bool - justification: str +class QuantityAnswerWithJustification(ImageReasoningAnswer[int]): + """A quantity answer telling the number of objects to the user question along with justification for the answer.""" -class QuantityAnswerWithJustification(BaseModel): - """A quantity answer to the user question along with justification for the answer.""" - answer: int - justification: str - - -class MultipleChoiceAnswerWithJustification(BaseModel): +class MultipleChoiceAnswerWithJustification(ImageReasoningAnswer[List[str]]): """A multiple choice answer to the user question along with justification for the answer.""" - answer: List[str] - justification: str - -class BoolImageTaskInput(BaseModel): - question: str = Field(..., description="The question to be answered.") - images_paths: List[str] = Field( - ..., - description="List of image file paths to be used for answering the question.", - ) - expected_answer: bool = Field( - ..., description="The expected answer to the question." - ) +class BoolImageTaskInput(ImageReasoningTaskInput[bool]): + """Input for a task that requires a boolean answer to a question about an image.""" -class QuantityImageTaskInput(BaseModel): +class QuantityImageTaskInput(ImageReasoningTaskInput[int]): """Input for a task that requires counting objects in an image.""" - question: str = Field(..., description="The question to be answered.") - images_paths: List[str] = Field( - ..., - description="List of image file paths to be used for answering the question.", - ) - expected_answer: int = Field( - ..., - description="The expected number of objects to be counted in the image.", - ) - -class MultipleChoiceImageTaskInput(BaseModel): +class MultipleChoiceImageTaskInput(ImageReasoningTaskInput[List[str]]): """Input for a task that requires selecting one or more answers from a list of options.""" - question: str = Field(..., description="The question to be answered.") - images_paths: List[str] = Field( - ..., - description="List of image file paths to be used for answering the question.", - ) options: List[str] = Field( ..., description="List of possible answers to the question.", ) - expected_answer: List[str] = Field( - ..., - description="The expected answer to the question being a list of strings chosen from the options.", - ) class BoolImageTask(ImageReasoningTask[BoolAnswerWithJustification]): From 02b459b469e0cf2a6660ddba94e65385356331b5 Mon Sep 17 00:00:00 2001 From: Magdalena Kotynia Date: Tue, 12 Aug 2025 13:22:36 +0200 Subject: [PATCH 06/17] docs: added info about increasing event size for Langfuse tracing of vlm benchmark --- src/rai_bench/README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/rai_bench/README.md b/src/rai_bench/README.md index b9a720aa3..892cd1f44 100644 --- a/src/rai_bench/README.md +++ b/src/rai_bench/README.md @@ -171,6 +171,9 @@ The VLM Benchmark is a benchmark for VLM models. It includes a set of tasks cont To set up tracing backends, please follow the instructions in the [tracing.md](../../docs/tracing.md) document. +> [!IMPORTANT] +> If you are going to use Lanfuse for tracing results, do `export LANGFUSE_MAX_EVENT_SIZE_BYTES=20000000`. By default, the maximum tracing event size is 1MB, but some tasks exceed this limit. + To run the benchmark: ```bash From 466ec59b2b3f3764bbe73e4c9ccd2e5dd216aac2 Mon Sep 17 00:00:00 2001 From: Magdalena Kotynia Date: Tue, 12 Aug 2025 18:14:18 +0200 Subject: [PATCH 07/17] feat: added merging results summaries across all repeats and all models --- src/rai_bench/rai_bench/test_models.py | 112 +++++++++++++++++++++++++ 1 file changed, 112 insertions(+) diff --git a/src/rai_bench/rai_bench/test_models.py b/src/rai_bench/rai_bench/test_models.py index e5f7405a5..88971777a 100644 --- a/src/rai_bench/rai_bench/test_models.py +++ b/src/rai_bench/rai_bench/test_models.py @@ -11,12 +11,14 @@ # # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # # See the License for the specific language governing permissions and # # limitations under the License. +import csv import uuid from abc import abstractmethod from datetime import datetime from pathlib import Path from typing import Any, Dict, List, Literal +import numpy as np from git import Optional from langchain.chat_models.base import BaseChatModel from pydantic import BaseModel @@ -24,12 +26,17 @@ import rai_bench.manipulation_o3de as manipulation_o3de import rai_bench.tool_calling_agent as tool_calling_agent import rai_bench.vlm_benchmark as vlm_benchmark +from rai_bench.base_benchmark import RunSummary +from rai_bench.results_processing.data_loading import SUMMARY_FILE_NAME from rai_bench.utils import ( define_benchmark_logger, get_llm_for_benchmark, get_llm_model_name, ) +REPEATS_SUMMARY_FILE_NAME = "repeats_summary.csv" +BENCHMARK_SUMMARY = "benchmark_summary.csv" + class BenchmarkConfig(BaseModel): repeats: int = 1 @@ -97,6 +104,98 @@ def name(self) -> str: return "vlm" +def merge_model_repeats_summary( + bench_name: str, model_name: str, run_dir: Path +) -> None: + """Merge summary results across all repeats for a single model. + + Parameters + ---------- + bench_name : str + Name of the benchmark + model_name : str + Name of the model + run_dir : Path + Directory containing the benchmark run results + """ + model_dir = run_dir / bench_name / model_name + if not model_dir.exists(): + return + + # TODO (mkotynia): create new BenchSummary model with added std of success rate and time across repeats + summaries: List[RunSummary] = [] + for repeat_dir in model_dir.iterdir(): + if repeat_dir.is_dir() and repeat_dir.name.isdigit(): + summary_file = repeat_dir / SUMMARY_FILE_NAME + if summary_file.exists(): + with open(summary_file, "r") as f: + reader = csv.DictReader(f) + for row in reader: + summaries.append(RunSummary.model_validate(row)) + + if not summaries: + return + + avg_success_rate = np.mean([s.success_rate for s in summaries]) + avg_time = np.mean([s.avg_time for s in summaries]) + total_tasks = np.min( + [s.total_tasks for s in summaries] + ) # NOTE (mkotynia) get the minimum total tasks across repeats. If benchmark breaks for some repeat, it will be noticed in such case + + merged_summary = RunSummary( + model_name=model_name, + success_rate=round(float(avg_success_rate), 2), + avg_time=round(float(avg_time), 3), + total_tasks=total_tasks, + ) + + merged_file = model_dir / REPEATS_SUMMARY_FILE_NAME + with open(merged_file, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=RunSummary.model_fields.keys()) + writer.writeheader() + writer.writerow(merged_summary.model_dump()) + + +def merge_benchmark_summary( + bench_name: str, run_dir: Path, model_names: List[str] +) -> None: + """Merge summary results across all models for a single benchmark. + + Parameters + ---------- + bench_name : str + Name of the benchmark + run_dir : Path + Directory containing the benchmark run results + model_names : List[str] + List of model names to include in the summary + """ + bench_dir = run_dir / bench_name + if not bench_dir.exists(): + return + + all_summaries: List[RunSummary] = [] + for model_name in model_names: + model_dir = bench_dir / model_name + merged_file = model_dir / REPEATS_SUMMARY_FILE_NAME + + if merged_file.exists(): + with open(merged_file, "r") as f: + reader = csv.DictReader(f) + for row in reader: + all_summaries.append(RunSummary.model_validate(row)) + + if not all_summaries: + return + + benchmark_summary_file = bench_dir / BENCHMARK_SUMMARY + with open(benchmark_summary_file, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=RunSummary.model_fields.keys()) + writer.writeheader() + for summary in all_summaries: + writer.writerow(summary.model_dump()) + + def test_dual_agents( multimodal_llms: List[BaseChatModel], tool_calling_models: List[BaseChatModel], @@ -183,6 +282,7 @@ def test_models( # for each bench configuration seperate run folder now = datetime.now() run_name = f"run_{now.strftime('%Y-%m-%d_%H-%M-%S')}" + run_dir = Path(out_dir) / run_name for i, model_name in enumerate(model_names): for u in range(bench_conf.repeats): curr_out_dir = ( @@ -240,8 +340,20 @@ def test_models( tasks=vlm_tasks, bench_logger=bench_logger, ) + except Exception as e: bench_logger.critical(f"BENCHMARK RUN FAILED: {e}") bench_logger.critical( f"{bench_conf.name} benchmark for {model_name}, vendor: {vendors[i]}, execution number: {u + 1}" ) + # TODO (mkotynia): resolve unbound bench_logger + bench_logger.info(f"Merging summaries for benchmark: {bench_conf.name}") + + for model_name in model_names: + merge_model_repeats_summary(bench_conf.name, model_name, run_dir) + + merge_benchmark_summary(bench_conf.name, run_dir, model_names) + + bench_logger.info( + f"Summary merging completed for benchmark: {bench_conf.name}" + ) From 1adc3d413cbbc00403f804c3c1393c9efe6bd949 Mon Sep 17 00:00:00 2001 From: Magdalena Kotynia Date: Wed, 13 Aug 2025 12:54:08 +0200 Subject: [PATCH 08/17] fix: fixed typing after refactor extracting common logic for vlm tasks inputs and answers --- .../rai_bench/vlm_benchmark/interfaces.py | 57 ++++++++++--------- .../rai_bench/vlm_benchmark/tasks/tasks.py | 14 ++--- 2 files changed, 35 insertions(+), 36 deletions(-) diff --git a/src/rai_bench/rai_bench/vlm_benchmark/interfaces.py b/src/rai_bench/rai_bench/vlm_benchmark/interfaces.py index 6280ecce3..e631dce01 100644 --- a/src/rai_bench/rai_bench/vlm_benchmark/interfaces.py +++ b/src/rai_bench/rai_bench/vlm_benchmark/interfaces.py @@ -14,7 +14,7 @@ import logging from abc import ABC, abstractmethod -from typing import Generic, List, Literal, Optional, TypeVar +from typing import Any, Generic, List, Literal, Optional, TypeVar from langchain_core.messages import BaseMessage from langchain_core.runnables.config import DEFAULT_RECURSION_LIMIT @@ -28,29 +28,6 @@ IMAGE_REASONING_SYSTEM_PROMPT = "You are a helpful and knowledgeable AI assistant that specializes in interpreting and analyzing visual content. Your task is to answer questions based on the images provided to you. Please response in requested structured output format." -class LangchainRawOutputModel(BaseModel): - """ - A Pydantic model for wrapping Langchain message parsing results from a structured output agent. See documentation for more details: - https://github.com/langchain-ai/langchain/blob/02001212b0a2b37d90451d8493089389ea220cab/libs/core/langchain_core/language_models/chat_models.py#L1430-L1432 - - - Attributes - ---------- - raw : BaseMessage - The original raw message object from Langchain before parsing. - parsed : BaseModel - The parsed and validated Pydantic model instance derived from the raw message. - parsing_error : Optional[BaseException] - Any exception that occurred during the parsing process, None if parsing - was successful. - """ - - model_config = ConfigDict(arbitrary_types_allowed=True) - raw: BaseMessage - parsed: BaseModel - parsing_error: Optional[BaseException] - - class TaskValidationError(Exception): pass @@ -78,7 +55,30 @@ class ImageReasoningAnswer(BaseModel, Generic[AnswerT]): justification: str = Field(..., description="Justification for the answer.") -class ImageReasoningTask(ABC, Generic[BaseModelT]): +class LangchainRawOutputModel(BaseModel): + """ + A Pydantic model for wrapping Langchain message parsing results from a structured output agent. See documentation for more details: + https://github.com/langchain-ai/langchain/blob/02001212b0a2b37d90451d8493089389ea220cab/libs/core/langchain_core/language_models/chat_models.py#L1430-L1432 + + + Attributes + ---------- + raw : BaseMessage + The original raw message object from Langchain before parsing. + parsed : BaseModel + The parsed and validated Pydantic model instance derived from the raw message. + parsing_error : Optional[BaseException] + Any exception that occurred during the parsing process, None if parsing + was successful. + """ + + model_config = ConfigDict(arbitrary_types_allowed=True) + raw: BaseMessage + parsed: ImageReasoningAnswer[Any] + parsing_error: Optional[BaseException] + + +class ImageReasoningTask(ABC, Generic[AnswerT]): complexity: Literal["easy", "medium", "hard"] recursion_limit: int = DEFAULT_RECURSION_LIMIT @@ -103,13 +103,14 @@ def __init__( self.logger = logging.getLogger(__name__) self.question: str self.images_paths: List[str] + # TODO move here task input def set_logger(self, logger: loggers_type): self.logger = logger @property @abstractmethod - def structured_output(self) -> type[BaseModelT]: + def structured_output(self) -> type[ImageReasoningAnswer[AnswerT]]: """Structured output that agent should return.""" pass @@ -141,7 +142,7 @@ def get_prompt(self) -> str: pass @abstractmethod - def validate(self, output: BaseModelT) -> float: + def validate(self, output: ImageReasoningAnswer[AnswerT]) -> float: """Validate result of the task.""" pass @@ -158,7 +159,7 @@ def get_images(self) -> List[str]: def get_structured_output_from_messages( self, messages: List[BaseMessage] - ) -> BaseModelT | None: + ) -> ImageReasoningAnswer[AnswerT] | None: """Extract and validate structured output from a list of messages. Iterates through messages in reverse order, attempting to find the message that is diff --git a/src/rai_bench/rai_bench/vlm_benchmark/tasks/tasks.py b/src/rai_bench/rai_bench/vlm_benchmark/tasks/tasks.py index fe76a96f4..2d9f8bc8b 100644 --- a/src/rai_bench/rai_bench/vlm_benchmark/tasks/tasks.py +++ b/src/rai_bench/rai_bench/vlm_benchmark/tasks/tasks.py @@ -57,7 +57,7 @@ class MultipleChoiceImageTaskInput(ImageReasoningTaskInput[List[str]]): ) -class BoolImageTask(ImageReasoningTask[BoolAnswerWithJustification]): +class BoolImageTask(ImageReasoningTask[bool]): complexity = "easy" def __init__( @@ -87,11 +87,11 @@ def get_images(self): images = [preprocess_image(image_path) for image_path in self.images_paths] return images - def validate(self, output: BoolAnswerWithJustification) -> float: + def validate(self, output: ImageReasoningAnswer[bool]) -> float: return float(output.answer == self.expected_answer) -class QuantityImageTask(ImageReasoningTask[QuantityAnswerWithJustification]): +class QuantityImageTask(ImageReasoningTask[int]): """A task that requires counting objects in an image.""" complexity = "medium" @@ -114,7 +114,7 @@ def type(self) -> str: def structured_output(self) -> Type[QuantityAnswerWithJustification]: return QuantityAnswerWithJustification - def validate(self, output: QuantityAnswerWithJustification) -> float: + def validate(self, output: ImageReasoningAnswer[int]) -> float: return float(output.answer == self.expected_answer) def get_prompt(self) -> str: @@ -125,9 +125,7 @@ def get_images(self): return images -class MultipleChoiceImageTask( - ImageReasoningTask[MultipleChoiceAnswerWithJustification] -): +class MultipleChoiceImageTask(ImageReasoningTask[List[str]]): """A task that requires selecting one or more answers from a set of options.""" complexity = "hard" @@ -151,7 +149,7 @@ def type(self) -> str: def structured_output(self) -> Type[MultipleChoiceAnswerWithJustification]: return MultipleChoiceAnswerWithJustification - def validate(self, output: MultipleChoiceAnswerWithJustification) -> float: + def validate(self, output: ImageReasoningAnswer[List[str]]) -> float: answers_processed = set([answer.casefold() for answer in output.answer]) expected_processed = set([answer.casefold() for answer in self.expected_answer]) From f791e85a3b16bd641d35d22cd45fc01222882aef Mon Sep 17 00:00:00 2001 From: Magdalena Kotynia Date: Wed, 13 Aug 2025 16:27:46 +0200 Subject: [PATCH 09/17] refactor: reorganized tasks order by images --- .../{fallen_human_3.png => image_10.png} | 0 .../{fallen_barrels_1.png => image_11.png} | 0 .../{fallen_barrels_2.png => image_12.png} | 0 .../{fallen_barrels_3.png => image_13.png} | 0 .../images/{gauge_level.png => image_14.png} | 0 .../{woman_wo_helmet_2.png => image_15.png} | 0 .../{woman_wo_helmet_1.png => image_16.png} | 0 .../{damaged_boxes.png => image_17.png} | 0 .../{fallen_human_1.png => image_8.png} | 0 .../{fallen_human_2.png => image_9.png} | 0 .../vlm_benchmark/predefined/tasks.py | 875 +++++++++++------- 11 files changed, 519 insertions(+), 356 deletions(-) rename src/rai_bench/rai_bench/vlm_benchmark/predefined/images/{fallen_human_3.png => image_10.png} (100%) rename src/rai_bench/rai_bench/vlm_benchmark/predefined/images/{fallen_barrels_1.png => image_11.png} (100%) rename src/rai_bench/rai_bench/vlm_benchmark/predefined/images/{fallen_barrels_2.png => image_12.png} (100%) rename src/rai_bench/rai_bench/vlm_benchmark/predefined/images/{fallen_barrels_3.png => image_13.png} (100%) rename src/rai_bench/rai_bench/vlm_benchmark/predefined/images/{gauge_level.png => image_14.png} (100%) rename src/rai_bench/rai_bench/vlm_benchmark/predefined/images/{woman_wo_helmet_2.png => image_15.png} (100%) rename src/rai_bench/rai_bench/vlm_benchmark/predefined/images/{woman_wo_helmet_1.png => image_16.png} (100%) rename src/rai_bench/rai_bench/vlm_benchmark/predefined/images/{damaged_boxes.png => image_17.png} (100%) rename src/rai_bench/rai_bench/vlm_benchmark/predefined/images/{fallen_human_1.png => image_8.png} (100%) rename src/rai_bench/rai_bench/vlm_benchmark/predefined/images/{fallen_human_2.png => image_9.png} (100%) diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_human_3.png b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/image_10.png similarity index 100% rename from src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_human_3.png rename to src/rai_bench/rai_bench/vlm_benchmark/predefined/images/image_10.png diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_barrels_1.png b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/image_11.png similarity index 100% rename from src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_barrels_1.png rename to src/rai_bench/rai_bench/vlm_benchmark/predefined/images/image_11.png diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_barrels_2.png b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/image_12.png similarity index 100% rename from src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_barrels_2.png rename to src/rai_bench/rai_bench/vlm_benchmark/predefined/images/image_12.png diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_barrels_3.png b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/image_13.png similarity index 100% rename from src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_barrels_3.png rename to src/rai_bench/rai_bench/vlm_benchmark/predefined/images/image_13.png diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/gauge_level.png b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/image_14.png similarity index 100% rename from src/rai_bench/rai_bench/vlm_benchmark/predefined/images/gauge_level.png rename to src/rai_bench/rai_bench/vlm_benchmark/predefined/images/image_14.png diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/woman_wo_helmet_2.png b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/image_15.png similarity index 100% rename from src/rai_bench/rai_bench/vlm_benchmark/predefined/images/woman_wo_helmet_2.png rename to src/rai_bench/rai_bench/vlm_benchmark/predefined/images/image_15.png diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/woman_wo_helmet_1.png b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/image_16.png similarity index 100% rename from src/rai_bench/rai_bench/vlm_benchmark/predefined/images/woman_wo_helmet_1.png rename to src/rai_bench/rai_bench/vlm_benchmark/predefined/images/image_16.png diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/damaged_boxes.png b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/image_17.png similarity index 100% rename from src/rai_bench/rai_bench/vlm_benchmark/predefined/images/damaged_boxes.png rename to src/rai_bench/rai_bench/vlm_benchmark/predefined/images/image_17.png diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_human_1.png b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/image_8.png similarity index 100% rename from src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_human_1.png rename to src/rai_bench/rai_bench/vlm_benchmark/predefined/images/image_8.png diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_human_2.png b/src/rai_bench/rai_bench/vlm_benchmark/predefined/images/image_9.png similarity index 100% rename from src/rai_bench/rai_bench/vlm_benchmark/predefined/images/fallen_human_2.png rename to src/rai_bench/rai_bench/vlm_benchmark/predefined/images/image_9.png diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/tasks.py b/src/rai_bench/rai_bench/vlm_benchmark/predefined/tasks.py index b9810e4ad..1462bed80 100644 --- a/src/rai_bench/rai_bench/vlm_benchmark/predefined/tasks.py +++ b/src/rai_bench/rai_bench/vlm_benchmark/predefined/tasks.py @@ -12,9 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import List, cast - -from pydantic import BaseModel +from typing import Any, List from rai_bench.vlm_benchmark.interfaces import ImageReasoningTask from rai_bench.vlm_benchmark.tasks.tasks import ( @@ -27,370 +25,535 @@ ) IMG_PATH = "src/rai_bench/rai_bench/vlm_benchmark/predefined/images/" -true_response_inputs: List[BoolImageTaskInput] = [ - BoolImageTaskInput( - question="Is the door on the left from the desk?", - images_paths=[IMG_PATH + "image_1.jpg"], - expected_answer=True, - ), - BoolImageTaskInput( - question="Is the light on in the room?", - images_paths=[IMG_PATH + "image_2.jpg"], - expected_answer=True, - ), - BoolImageTaskInput( - question="Do you see the plant?", - images_paths=[IMG_PATH + "image_2.jpg"], - expected_answer=True, - ), - BoolImageTaskInput( - question="Are there any pictures on the wall?", - images_paths=[IMG_PATH + "image_3.jpg"], - expected_answer=True, - ), - BoolImageTaskInput( - question="Are there 3 pictures on the wall?", - images_paths=[IMG_PATH + "image_4.jpg"], - expected_answer=True, - ), - BoolImageTaskInput( - question="Is there a plant behind the rack?", - images_paths=[IMG_PATH + "image_5.jpg"], - expected_answer=True, - ), - BoolImageTaskInput( - question="Is there a pillow on the armchair?", - images_paths=[IMG_PATH + "image_7.jpg"], - expected_answer=True, - ), - BoolImageTaskInput( - question="Do you see something anomalous in the image?", - images_paths=[IMG_PATH + "fallen_human_3.png"], - expected_answer=True, - ), - BoolImageTaskInput( - question="Do you see something anomalous in the image?", - images_paths=[IMG_PATH + "fallen_human_2.png"], - expected_answer=True, - ), - BoolImageTaskInput( - question="Do you see something anomalous in the image?", - images_paths=[IMG_PATH + "fallen_human_1.png"], - expected_answer=True, - ), - BoolImageTaskInput( - question="Is gauge level high?", - images_paths=[IMG_PATH + "gauge_level.png"], - expected_answer=True, - ), - BoolImageTaskInput( - question="Is worker wearing a vest?", - images_paths=[IMG_PATH + "woman_wo_helmet_2.png"], - expected_answer=True, - ), - BoolImageTaskInput( - question="Is any person in the image?", - images_paths=[IMG_PATH + "fallen_barrels_1.png"], - expected_answer=True, + + +## ANOTHER TASKS ORGANIZATION + +image_1 = IMG_PATH + "image_1.jpg" + +image_1_tasks: List[ImageReasoningTask[Any]] = [ + BoolImageTask( + task_input=BoolImageTaskInput( + question="Is the door on the left from the desk?", + images_paths=[image_1], + expected_answer=True, + ) + ), + BoolImageTask( + task_input=BoolImageTaskInput( + question="Is the door open?", + images_paths=[image_1], + expected_answer=False, + ) + ), + BoolImageTask( + task_input=BoolImageTaskInput( + question="Is someone in the room?", + images_paths=[image_1], + expected_answer=False, + ) + ), + MultipleChoiceImageTask( + task_input=MultipleChoiceImageTaskInput( + question="What is in the image?", + images_paths=[image_1], + options=[ + "gauge", + "bed", + "barrels", + "lamp with a shade", + "door", + "boxes", + "human", + "desk", + "plant", + "roll container", + ], + expected_answer=["bed", "lamp with a shade", "door", "desk"], + ) + ), +] + +image_2 = IMG_PATH + "image_2.jpg" + +image_2_tasks: List[ImageReasoningTask[Any]] = [ + BoolImageTask( + task_input=BoolImageTaskInput( + question="Is the light on in the room?", + images_paths=[image_2], + expected_answer=True, + ) + ), + BoolImageTask( + task_input=BoolImageTaskInput( + question="Do you see the plant?", + images_paths=[image_2], + expected_answer=True, + ) + ), + QuantityImageTask( + task_input=QuantityImageTaskInput( + question="How many windows are visible in the image?", + images_paths=[image_2], + expected_answer=2, + ) + ), +] + +image_3 = IMG_PATH + "image_3.jpg" + +image_3_tasks: List[ImageReasoningTask[Any]] = [ + BoolImageTask( + task_input=BoolImageTaskInput( + question="Are there any pictures on the wall?", + images_paths=[image_3], + expected_answer=True, + ) + ), + BoolImageTask( + task_input=BoolImageTaskInput( + question="Do you see the plant?", + images_paths=[image_3], + expected_answer=False, + ) + ), + QuantityImageTask( + task_input=QuantityImageTaskInput( + question="How many pictures are on the wall?", + images_paths=[image_3], + expected_answer=3, + ) + ), +] + +image_4 = IMG_PATH + "image_4.jpg" + +image_4_tasks: List[ImageReasoningTask[Any]] = [ + BoolImageTask( + task_input=BoolImageTaskInput( + question="Are there 3 pictures on the wall?", + images_paths=[image_4], + expected_answer=True, + ) + ), + BoolImageTask( + task_input=BoolImageTaskInput( + question="Are there 4 pictures on the wall?", + images_paths=[image_4], + expected_answer=False, + ) + ), + BoolImageTask( + task_input=BoolImageTaskInput( + question="Is there a rack on the left from the sofa?", + images_paths=[image_4], + expected_answer=False, + ) + ), + QuantityImageTask( + task_input=QuantityImageTaskInput( + question="How many pictures are on the wall?", + images_paths=[image_4], + expected_answer=3, + ) + ), + MultipleChoiceImageTask( + task_input=MultipleChoiceImageTaskInput( + question="What is in the image?", + images_paths=[image_4], + options=[ + "sofa", + "gauge", + "bed", + "armchair", + "barrels", + "lamp with a shade", + "door", + "boxes", + "human", + "desk", + "plant", + "roll container", + "dresser", + "TV", + ], + expected_answer=["sofa", "plant", "TV", "dresser"], + ) + ), +] + +image_5 = IMG_PATH + "image_5.jpg" + +image_5_tasks: List[ImageReasoningTask[Any]] = [ + BoolImageTask( + task_input=BoolImageTaskInput( + question="Is there a plant behind the rack?", + images_paths=[image_5], + expected_answer=True, + ) + ), +] + +image_6 = IMG_PATH + "image_6.jpg" + +image_6_tasks: List[ImageReasoningTask[Any]] = [ + BoolImageTask( + task_input=BoolImageTaskInput( + question="Is there a plant on the right from the window?", + images_paths=[image_6], + expected_answer=False, + ) + ), + QuantityImageTask( + task_input=QuantityImageTaskInput( + question="How many pictures are on the wall?", + images_paths=[image_6], + expected_answer=2, + ) + ), +] + +image_7 = IMG_PATH + "image_7.jpg" + +image_7_tasks: List[ImageReasoningTask[Any]] = [ + BoolImageTask( + task_input=BoolImageTaskInput( + question="Is there a pillow on the armchair?", + images_paths=[image_7], + expected_answer=True, + ) + ), + BoolImageTask( + task_input=BoolImageTaskInput( + question="Is there a red pillow on the armchair?", + images_paths=[image_7], + expected_answer=False, + ) + ), + MultipleChoiceImageTask( + task_input=MultipleChoiceImageTaskInput( + question="What is in the image?", + images_paths=[image_7], + options=[ + "sofa", + "gauge", + "armchair", + "barrels", + "door", + "boxes", + "human", + "desk", + "plant", + "roll container", + ], + expected_answer=["sofa", "armchair", "door", "desk", "plant"], + ) + ), +] + +image_8 = IMG_PATH + "image_8.png" + +image_8_tasks: List[ImageReasoningTask[Any]] = [ + BoolImageTask( + task_input=BoolImageTaskInput( + question="Do you see something anomalous in the image?", + images_paths=[image_8], + expected_answer=True, + ) + ), + QuantityImageTask( + task_input=QuantityImageTaskInput( + question="How many people are in the image?", + images_paths=[image_8], + expected_answer=1, + ) + ), + MultipleChoiceImageTask( + task_input=MultipleChoiceImageTaskInput( + question="What is in the image?", + images_paths=[image_8], + options=[ + "sofa", + "gauge", + "bed", + "armchair", + "barrels", + "lamp with a shade", + "door", + "boxes", + "human", + "desk", + "plant", + "roll container", + "dresser", + ], + expected_answer=["human", "boxes"], + ) + ), +] + +image_9 = IMG_PATH + "image_9.png" + +image_9_tasks: List[ImageReasoningTask[Any]] = [ + BoolImageTask( + task_input=BoolImageTaskInput( + question="Do you see something anomalous in the image?", + images_paths=[image_9], + expected_answer=True, + ) + ), + QuantityImageTask( + task_input=QuantityImageTaskInput( + question="How many people are in the image?", + images_paths=[image_9], + expected_answer=1, + ) + ), + MultipleChoiceImageTask( + task_input=MultipleChoiceImageTaskInput( + question="What is in the image?", + images_paths=[image_9], + options=[ + "sofa", + "gauge", + "bed", + "armchair", + "barrels", + "lamp with a shade", + "door", + "boxes", + "human", + "desk", + "plant", + "roll container", + "dresser", + ], + expected_answer=["boxes", "human", "roll container"], + ) + ), +] + +image_10 = IMG_PATH + "image_10.png" + +image_10_tasks: List[ImageReasoningTask[Any]] = [ + BoolImageTask( + task_input=BoolImageTaskInput( + question="Do you see something anomalous in the image?", + images_paths=[image_10], + expected_answer=True, + ) + ), + QuantityImageTask( + task_input=QuantityImageTaskInput( + question="How many people are in the image?", + images_paths=[image_10], + expected_answer=1, + ) + ), +] + +image_11 = IMG_PATH + "image_11.png" + +image_11_tasks: List[ImageReasoningTask[Any]] = [ + BoolImageTask( + task_input=BoolImageTaskInput( + question="Is any person in the image?", + images_paths=[image_11], + expected_answer=True, + ) + ), + QuantityImageTask( + task_input=QuantityImageTaskInput( + question="How many barrels are in the image?", + images_paths=[image_11], + expected_answer=6, + ) + ), + QuantityImageTask( + task_input=QuantityImageTaskInput( + question="How many fallen barrels are in the image?", + images_paths=[image_11], + expected_answer=2, + ) ), ] -false_response_inputs: List[BoolImageTaskInput] = [ - BoolImageTaskInput( - question="Is the door open?", - images_paths=[IMG_PATH + "image_1.jpg"], - expected_answer=False, - ), - BoolImageTaskInput( - question="Is someone in the room?", - images_paths=[IMG_PATH + "image_1.jpg"], - expected_answer=False, - ), - BoolImageTaskInput( - question="Do you see the plant?", - images_paths=[IMG_PATH + "image_3.jpg"], - expected_answer=False, - ), - BoolImageTaskInput( - question="Are there 4 pictures on the wall?", - images_paths=[IMG_PATH + "image_4.jpg"], - expected_answer=False, - ), - BoolImageTaskInput( - question="Is there a rack on the left from the sofa?", - images_paths=[IMG_PATH + "image_4.jpg"], - expected_answer=False, - ), - BoolImageTaskInput( - question="Is there a plant on the right from the window?", - images_paths=[IMG_PATH + "image_6.jpg"], - expected_answer=False, - ), - BoolImageTaskInput( - question="Is there a red pillow on the armchair?", - images_paths=[IMG_PATH + "image_7.jpg"], - expected_answer=False, - ), - BoolImageTaskInput( - question="Is worker wearing a helmet?", - images_paths=[IMG_PATH + "woman_wo_helmet_1.png"], - expected_answer=False, - ), - BoolImageTaskInput( - question="Is worker wearing a helmet?", - images_paths=[IMG_PATH + "woman_wo_helmet_2.png"], - expected_answer=False, - ), - BoolImageTaskInput( - question="Is gauge level low?", - images_paths=[IMG_PATH + "gauge_level.png"], - expected_answer=False, +image_12 = IMG_PATH + "image_12.png" + +image_12_tasks: List[ImageReasoningTask[Any]] = [ + QuantityImageTask( + task_input=QuantityImageTaskInput( + question="How many fallen barrels are in the image?", + images_paths=[image_12], + expected_answer=1, + ), + ), + QuantityImageTask( + task_input=QuantityImageTaskInput( + question="How many people are in the image?", + images_paths=[image_12], + expected_answer=0, + ) + ), + MultipleChoiceImageTask( + task_input=MultipleChoiceImageTaskInput( + question="What is in the image?", + images_paths=[image_12], + options=[ + "sofa", + "gauge", + "bed", + "armchair", + "barrels", + "lamp with a shade", + "door", + "boxes", + "human", + "desk", + "plant", + "roll container", + "dresser", + ], + expected_answer=["barrels", "boxes", "roll container"], + ), ), ] -quantity_response_inputs: List[QuantityImageTaskInput] = [ - QuantityImageTaskInput( - question="How many barrels are in the image?", - images_paths=[IMG_PATH + "fallen_barrels_1.png"], - expected_answer=6, - ), - QuantityImageTaskInput( - question="How many fallen barrels are in the image?", - images_paths=[IMG_PATH + "fallen_barrels_1.png"], - expected_answer=2, - ), - QuantityImageTaskInput( - question="How many fallen barrels are in the image?", - images_paths=[IMG_PATH + "fallen_barrels_2.png"], - expected_answer=1, - ), - QuantityImageTaskInput( - question="How many fallen barrels are in the image?", - images_paths=[IMG_PATH + "fallen_barrels_3.png"], - expected_answer=1, - ), - QuantityImageTaskInput( - question="How many barrels are in the image?", - images_paths=[IMG_PATH + "fallen_barrels_3.png"], - expected_answer=6, - ), - QuantityImageTaskInput( - question="How many people are in the image?", - images_paths=[IMG_PATH + "fallen_barrels_2.png"], - expected_answer=0, - ), - QuantityImageTaskInput( - question="How many people are in the image?", - images_paths=[IMG_PATH + "fallen_human_1.png"], - expected_answer=1, - ), - QuantityImageTaskInput( - question="How many people are in the image?", - images_paths=[IMG_PATH + "fallen_human_2.png"], - expected_answer=1, - ), - QuantityImageTaskInput( - question="How many people are in the image?", - images_paths=[IMG_PATH + "fallen_human_3.png"], - expected_answer=1, - ), - QuantityImageTaskInput( - question="How many barrels are in the image?", - images_paths=[IMG_PATH + "gauge_level.png"], - expected_answer=4, - ), - QuantityImageTaskInput( - question="How many damaged boxes are in the image?", - images_paths=[IMG_PATH + "damaged_boxes.png"], - expected_answer=2, - ), - QuantityImageTaskInput( - question="How many pictures are on the wall?", - images_paths=[IMG_PATH + "image_3.jpg"], - expected_answer=3, - ), - QuantityImageTaskInput( - question="How many pictures are on the wall?", - images_paths=[IMG_PATH + "image_4.jpg"], - expected_answer=3, - ), - QuantityImageTaskInput( - question="How many pictures are on the wall?", - images_paths=[IMG_PATH + "image_6.jpg"], - expected_answer=2, - ), - QuantityImageTaskInput( - question="How many windows are visible in the image?", - images_paths=[IMG_PATH + "image_2.jpg"], - expected_answer=2, +image_13 = IMG_PATH + "image_13.png" + +image_13_tasks: List[ImageReasoningTask[Any]] = [ + QuantityImageTask( + task_input=QuantityImageTaskInput( + question="How many fallen barrels are in the image?", + images_paths=[IMG_PATH + image_13], + expected_answer=1, + ) + ), + QuantityImageTask( + task_input=QuantityImageTaskInput( + question="How many barrels are in the image?", + images_paths=[IMG_PATH + image_13], + expected_answer=6, + ) ), ] -multiple_choice_response_inputs: List[MultipleChoiceImageTaskInput] = [ - MultipleChoiceImageTaskInput( - question="What is in the image?", - images_paths=[IMG_PATH + "fallen_barrels_2.png"], - options=[ - "sofa", - "gauge", - "bed", - "armchair", - "barrels", - "lamp with a shade", - "door", - "boxes", - "human", - "desk", - "plant", - "roll container", - "dresser", - ], - expected_answer=["barrels", "boxes", "roll container"], - ), - MultipleChoiceImageTaskInput( - question="What is in the image?", - images_paths=[IMG_PATH + "fallen_human_1.png"], - options=[ - "sofa", - "gauge", - "bed", - "armchair", - "barrels", - "lamp with a shade", - "door", - "boxes", - "human", - "desk", - "plant", - "roll container", - "dresser", - ], - expected_answer=["human", "boxes"], - ), - MultipleChoiceImageTaskInput( - question="What is in the image?", - images_paths=[IMG_PATH + "gauge_level.png"], - options=[ - "sofa", - "gauge", - "bed", - "armchair", - "barrels", - "lamp with a shade", - "door", - "boxes", - "human", - "desk", - "plant", - "roll container", - "dresser", - ], - expected_answer=["gauge", "barrels"], - ), - MultipleChoiceImageTaskInput( - question="What is in the image?", - images_paths=[IMG_PATH + "fallen_human_2.png"], - options=[ - "sofa", - "gauge", - "bed", - "armchair", - "barrels", - "lamp with a shade", - "door", - "boxes", - "human", - "desk", - "plant", - "roll container", - "dresser", - ], - expected_answer=["boxes", "human", "roll container"], - ), - MultipleChoiceImageTaskInput( - question="What is in the image?", - images_paths=[IMG_PATH + "image_1.jpg"], - options=[ - "gauge", - "bed", - "barrels", - "lamp with a shade", - "door", - "boxes", - "human", - "desk", - "plant", - "roll container", - ], - expected_answer=["bed", "lamp with a shade", "door", "desk"], - ), - MultipleChoiceImageTaskInput( - question="What is in the image?", - images_paths=[IMG_PATH + "image_4.jpg"], - options=[ - "sofa", - "gauge", - "bed", - "armchair", - "barrels", - "lamp with a shade", - "door", - "boxes", - "human", - "desk", - "plant", - "roll container", - "dresser", - "TV", - ], - expected_answer=["sofa", "plant", "TV", "dresser"], - ), - MultipleChoiceImageTaskInput( - question="What is in the image?", - images_paths=[IMG_PATH + "image_7.jpg"], - options=[ - "sofa", - "gauge", - "armchair", - "barrels", - "door", - "boxes", - "human", - "desk", - "plant", - "roll container", - ], - expected_answer=["sofa", "armchair", "door", "desk", "plant"], +image_14 = IMG_PATH + "image_14.png" + +image_14_tasks: List[ImageReasoningTask[Any]] = [ + BoolImageTask( + task_input=BoolImageTaskInput( + question="Is gauge level high?", + images_paths=[image_14], + expected_answer=True, + ) + ), + BoolImageTask( + task_input=BoolImageTaskInput( + question="Is gauge level low?", + images_paths=[image_14], + expected_answer=False, + ), + ), + QuantityImageTask( + task_input=QuantityImageTaskInput( + question="How many barrels are in the image?", + images_paths=[image_14], + expected_answer=4, + ), + ), + MultipleChoiceImageTask( + task_input=MultipleChoiceImageTaskInput( + question="What is in the image?", + images_paths=[image_14], + options=[ + "sofa", + "gauge", + "bed", + "armchair", + "barrels", + "lamp with a shade", + "door", + "boxes", + "human", + "desk", + "plant", + "roll container", + "dresser", + ], + expected_answer=["gauge", "barrels"], + ) ), ] +image_15 = IMG_PATH + "image_15.png" +image_15_tasks: List[ImageReasoningTask[Any]] = [ + BoolImageTask( + task_input=BoolImageTaskInput( + question="Is worker wearing a vest?", + images_paths=[image_15], + expected_answer=True, + ) + ), + BoolImageTask( + task_input=BoolImageTaskInput( + question="Is worker wearing a helmet?", + images_paths=[image_15], + expected_answer=False, + ) + ), +] + +image_16 = IMG_PATH + "image_16.png" -def get_spatial_tasks() -> List[ImageReasoningTask[BaseModel]]: - true_tasks = [ - BoolImageTask( - task_input=input_item, - ) - for input_item in true_response_inputs - ] - false_tasks = [ - BoolImageTask( - task_input=input_item, - ) - for input_item in false_response_inputs - ] - quantity_tasks = [ - QuantityImageTask( - task_input=input_item, - ) - for input_item in quantity_response_inputs - ] - multiple_choice_tasks = [ - MultipleChoiceImageTask( - task_input=input_item, - ) - for input_item in multiple_choice_response_inputs - ] - return cast( - List[ImageReasoningTask[BaseModel]], - true_tasks + false_tasks + quantity_tasks + multiple_choice_tasks, +image_16_tasks: List[ImageReasoningTask[Any]] = [ + BoolImageTask( + task_input=BoolImageTaskInput( + question="Is worker wearing a helmet?", + images_paths=[IMG_PATH + "woman_wo_helmet_1.png"], + expected_answer=False, + ), ) +] + +image_17 = IMG_PATH + "image_17.png" + + +image_17_tasks: List[ImageReasoningTask[Any]] = [ + QuantityImageTask( + task_input=QuantityImageTaskInput( + question="How many damaged boxes are in the image?", + images_paths=[image_17], + expected_answer=2, + ) + ), +] + + +def get_spatial_tasks() -> List[ImageReasoningTask[Any]]: + """ + Return a flat list with all predefined spatial image reasoning tasks + declared in this module. + """ + all_tasks: List[ImageReasoningTask[Any]] = [] + for task_group in [ + image_1_tasks, + image_2_tasks, + image_3_tasks, + image_4_tasks, + image_5_tasks, + image_6_tasks, + image_7_tasks, + image_8_tasks, + image_9_tasks, + image_10_tasks, + image_11_tasks, + image_12_tasks, + image_13_tasks, + image_14_tasks, + image_15_tasks, + image_16_tasks, + image_17_tasks, + ]: + all_tasks.extend(task_group) + return all_tasks From fe1b5a8c46a99e0567eb1534628328e8981443d5 Mon Sep 17 00:00:00 2001 From: Magdalena Kotynia Date: Wed, 13 Aug 2025 16:32:33 +0200 Subject: [PATCH 10/17] chore: removed unused comment --- src/rai_bench/rai_bench/vlm_benchmark/predefined/tasks.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/tasks.py b/src/rai_bench/rai_bench/vlm_benchmark/predefined/tasks.py index 1462bed80..3ffbf1500 100644 --- a/src/rai_bench/rai_bench/vlm_benchmark/predefined/tasks.py +++ b/src/rai_bench/rai_bench/vlm_benchmark/predefined/tasks.py @@ -27,8 +27,6 @@ IMG_PATH = "src/rai_bench/rai_bench/vlm_benchmark/predefined/images/" -## ANOTHER TASKS ORGANIZATION - image_1 = IMG_PATH + "image_1.jpg" image_1_tasks: List[ImageReasoningTask[Any]] = [ From 90d163b78d698e6349c59b03b8baae217408915c Mon Sep 17 00:00:00 2001 From: Magdalena Kotynia Date: Thu, 14 Aug 2025 10:56:14 +0200 Subject: [PATCH 11/17] refactor: created separate structure for storing summary for the model across all tasks and repeats --- src/rai_bench/rai_bench/base_benchmark.py | 38 +++++++++++++++++++++++ src/rai_bench/rai_bench/test_models.py | 14 ++++----- 2 files changed, 45 insertions(+), 7 deletions(-) diff --git a/src/rai_bench/rai_bench/base_benchmark.py b/src/rai_bench/rai_bench/base_benchmark.py index d8024715b..abee35560 100644 --- a/src/rai_bench/rai_bench/base_benchmark.py +++ b/src/rai_bench/rai_bench/base_benchmark.py @@ -35,6 +35,44 @@ class RunSummary(BaseModel): total_tasks: int = Field(..., description="Total number of executed tasks.") +class ModelSummary(BaseModel): + model_name: str = Field(..., description="Name of the LLM.") + avg_success_rate: float = Field( + ..., + description="Percentage of successfully completed tasks across all repeats.", + ) + avg_total_tasks: float = Field( + ..., description="Average number of tasks executed through all repeats." + ) + avg_time: float = Field( + ..., description="Average time taken across all tasks and repeats." + ) + + repeats: int = Field( + ..., description="Total number of repeats for the model for each task." + ) + + +class TasksSummary(BaseModel): + model_name: str = Field(..., description="Name of the LLM.") + avg_success_rate: float = Field( + ..., description="Average result for task across all repeats." + ) + std_success_rate: float = Field( + ..., description="Standard deviation of the success rate across all repeats." + ) + avg_time: float = Field( + ..., description="Average time taken across all repeats for one task." + ) + std_time: float = Field( + ..., + description="Standard deviation of the time taken across all repeats for one task.", + ) + total_tasks: int = Field( + ..., description="Total number of executed tasks across all repeats per task." + ) + + class TimeoutException(Exception): pass diff --git a/src/rai_bench/rai_bench/test_models.py b/src/rai_bench/rai_bench/test_models.py index 88971777a..7596323cd 100644 --- a/src/rai_bench/rai_bench/test_models.py +++ b/src/rai_bench/rai_bench/test_models.py @@ -26,7 +26,7 @@ import rai_bench.manipulation_o3de as manipulation_o3de import rai_bench.tool_calling_agent as tool_calling_agent import rai_bench.vlm_benchmark as vlm_benchmark -from rai_bench.base_benchmark import RunSummary +from rai_bench.base_benchmark import ModelSummary, RunSummary from rai_bench.results_processing.data_loading import SUMMARY_FILE_NAME from rai_bench.utils import ( define_benchmark_logger, @@ -138,15 +138,15 @@ def merge_model_repeats_summary( avg_success_rate = np.mean([s.success_rate for s in summaries]) avg_time = np.mean([s.avg_time for s in summaries]) - total_tasks = np.min( - [s.total_tasks for s in summaries] - ) # NOTE (mkotynia) get the minimum total tasks across repeats. If benchmark breaks for some repeat, it will be noticed in such case - merged_summary = RunSummary( + total_tasks = np.mean([s.total_tasks for s in summaries]) + + merged_summary = ModelSummary( model_name=model_name, - success_rate=round(float(avg_success_rate), 2), + avg_success_rate=round(float(avg_success_rate), 2), avg_time=round(float(avg_time), 3), - total_tasks=total_tasks, + avg_total_tasks=round(float(total_tasks), 3), + repeats=len(summaries), ) merged_file = model_dir / REPEATS_SUMMARY_FILE_NAME From 01179b686440e0e0ce61e9a4294f3ae1fca48522 Mon Sep 17 00:00:00 2001 From: Magdalena Kotynia Date: Thu, 14 Aug 2025 12:18:03 +0200 Subject: [PATCH 12/17] fix: fixed validation of parsed llm output --- src/rai_bench/rai_bench/vlm_benchmark/interfaces.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/src/rai_bench/rai_bench/vlm_benchmark/interfaces.py b/src/rai_bench/rai_bench/vlm_benchmark/interfaces.py index e631dce01..7ead05bb1 100644 --- a/src/rai_bench/rai_bench/vlm_benchmark/interfaces.py +++ b/src/rai_bench/rai_bench/vlm_benchmark/interfaces.py @@ -191,8 +191,11 @@ def get_structured_output_from_messages( ) parsed = validated_message.parsed - if isinstance(parsed, self.structured_output): - return parsed + expected_output_type = self.structured_output + parsed_valid_output = expected_output_type.model_validate( + parsed.model_dump() + ) + return parsed_valid_output except ValidationError: continue return None From 30c6b6a1481e602ca96755815d8ee85e5c53450b Mon Sep 17 00:00:00 2001 From: Magdalena Kotynia Date: Mon, 18 Aug 2025 14:11:34 +0200 Subject: [PATCH 13/17] refactor: added task id, refactor of storing task_input variables --- src/rai_bench/rai_bench/test_models.py | 87 +++++++++++++++++-- .../rai_bench/vlm_benchmark/benchmark.py | 3 +- .../rai_bench/vlm_benchmark/interfaces.py | 36 +++++++- .../vlm_benchmark/results_tracking.py | 1 + .../rai_bench/vlm_benchmark/tasks/tasks.py | 14 +-- 5 files changed, 119 insertions(+), 22 deletions(-) diff --git a/src/rai_bench/rai_bench/test_models.py b/src/rai_bench/rai_bench/test_models.py index 7596323cd..8820ec88f 100644 --- a/src/rai_bench/rai_bench/test_models.py +++ b/src/rai_bench/rai_bench/test_models.py @@ -26,8 +26,11 @@ import rai_bench.manipulation_o3de as manipulation_o3de import rai_bench.tool_calling_agent as tool_calling_agent import rai_bench.vlm_benchmark as vlm_benchmark -from rai_bench.base_benchmark import ModelSummary, RunSummary -from rai_bench.results_processing.data_loading import SUMMARY_FILE_NAME +from rai_bench.base_benchmark import ModelSummary, RunSummary, TasksSummary +from rai_bench.results_processing.data_loading import ( + DETAILED_FILE_NAME, + SUMMARY_FILE_NAME, +) from rai_bench.utils import ( define_benchmark_logger, get_llm_for_benchmark, @@ -35,6 +38,7 @@ ) REPEATS_SUMMARY_FILE_NAME = "repeats_summary.csv" +TASKS_SUMMARY_FILE_NAME = "tasks_summary.csv" BENCHMARK_SUMMARY = "benchmark_summary.csv" @@ -151,7 +155,7 @@ def merge_model_repeats_summary( merged_file = model_dir / REPEATS_SUMMARY_FILE_NAME with open(merged_file, "w", newline="") as f: - writer = csv.DictWriter(f, fieldnames=RunSummary.model_fields.keys()) + writer = csv.DictWriter(f, fieldnames=ModelSummary.model_fields.keys()) writer.writeheader() writer.writerow(merged_summary.model_dump()) @@ -174,7 +178,7 @@ def merge_benchmark_summary( if not bench_dir.exists(): return - all_summaries: List[RunSummary] = [] + all_summaries: List[ModelSummary] = [] for model_name in model_names: model_dir = bench_dir / model_name merged_file = model_dir / REPEATS_SUMMARY_FILE_NAME @@ -183,19 +187,89 @@ def merge_benchmark_summary( with open(merged_file, "r") as f: reader = csv.DictReader(f) for row in reader: - all_summaries.append(RunSummary.model_validate(row)) + all_summaries.append(ModelSummary.model_validate(row)) if not all_summaries: return benchmark_summary_file = bench_dir / BENCHMARK_SUMMARY with open(benchmark_summary_file, "w", newline="") as f: - writer = csv.DictWriter(f, fieldnames=RunSummary.model_fields.keys()) + writer = csv.DictWriter(f, fieldnames=ModelSummary.model_fields.keys()) writer.writeheader() for summary in all_summaries: writer.writerow(summary.model_dump()) +def merge_tasks_summary(bench_name: str, model_name: str, run_dir: Path) -> None: + """Merge task results across all repeats for a single model, aggregating by task. + + Parameters + ---------- + bench_name : str + Name of the benchmark + model_name : str + Name of the model + run_dir : Path + Directory containing the benchmark run results + """ + model_dir = run_dir / bench_name / model_name + if not model_dir.exists(): + return + + # Collect all task results from all repeats + task_data_by_prompt: Dict[str, Dict[str, List[float]]] = {} + + for repeat_dir in model_dir.iterdir(): + if repeat_dir.is_dir() and repeat_dir.name.isdigit(): + results_file = repeat_dir / DETAILED_FILE_NAME + if results_file.exists(): + # Read detailed results from this repeat + with open(results_file, "r") as f: + reader = csv.DictReader(f) + for row in reader: + task_prompt = row["task_prompt"] + score = float(row["score"]) + total_time = float(row["total_time"]) + + if task_prompt not in task_data_by_prompt: + task_data_by_prompt[task_prompt] = { + "scores": [], + "times": [], + } + + task_data_by_prompt[task_prompt]["scores"].append(score) + task_data_by_prompt[task_prompt]["times"].append(total_time) + + if not task_data_by_prompt: + return + + # Calculate statistics for each task + task_summaries: List[TasksSummary] = [] + for task_prompt, data in task_data_by_prompt.items(): + scores = np.array(data["scores"]) + times = np.array(data["times"]) + + task_summary = TasksSummary( + model_name=model_name, + task_prompt=task_prompt, + avg_success_rate=round(float(scores.mean()), 3), + std_success_rate=round(float(scores.std()), 3), + avg_time=round(float(times.mean()), 3), + std_time=round(float(times.std()), 3), + repeats=len(scores), # TODO (mkotynia) (extract repeats in another way) + ) + task_summaries.append(task_summary) + + # Save task summaries to CSV + tasks_summary_file = model_dir / TASKS_SUMMARY_FILE_NAME + with open(tasks_summary_file, "w", newline="") as f: + if task_summaries: + writer = csv.DictWriter(f, fieldnames=TasksSummary.model_fields.keys()) + writer.writeheader() + for task_summary in task_summaries: + writer.writerow(task_summary.model_dump()) + + def test_dual_agents( multimodal_llms: List[BaseChatModel], tool_calling_models: List[BaseChatModel], @@ -351,6 +425,7 @@ def test_models( for model_name in model_names: merge_model_repeats_summary(bench_conf.name, model_name, run_dir) + merge_tasks_summary(bench_conf.name, model_name, run_dir) merge_benchmark_summary(bench_conf.name, run_dir, model_names) diff --git a/src/rai_bench/rai_bench/vlm_benchmark/benchmark.py b/src/rai_bench/rai_bench/vlm_benchmark/benchmark.py index 8f1c9d699..c2c1f1836 100644 --- a/src/rai_bench/rai_bench/vlm_benchmark/benchmark.py +++ b/src/rai_bench/rai_bench/vlm_benchmark/benchmark.py @@ -133,7 +133,7 @@ def run_next(self, agent: CompiledStateGraph, experiment_id: uuid.UUID) -> None: score = task.validate(output=structured_output) else: errors.append(f"Not valid structured output: {type(structured_output)}") - score = False + score = 0 te = time.perf_counter() total_time = te - ts @@ -141,6 +141,7 @@ def run_next(self, agent: CompiledStateGraph, experiment_id: uuid.UUID) -> None: self.logger.info(f"TASK SCORE: {score}, TOTAL TIME: {total_time:.3f}") task_result = TaskResult( + task_id=task.task_id, task_prompt=task.get_prompt(), system_prompt=task.get_system_prompt(), type=task.type, diff --git a/src/rai_bench/rai_bench/vlm_benchmark/interfaces.py b/src/rai_bench/rai_bench/vlm_benchmark/interfaces.py index 7ead05bb1..83d303152 100644 --- a/src/rai_bench/rai_bench/vlm_benchmark/interfaces.py +++ b/src/rai_bench/rai_bench/vlm_benchmark/interfaces.py @@ -12,13 +12,14 @@ # See the License for the specific language governing permissions and # limitations under the License. +import hashlib import logging from abc import ABC, abstractmethod from typing import Any, Generic, List, Literal, Optional, TypeVar from langchain_core.messages import BaseMessage from langchain_core.runnables.config import DEFAULT_RECURSION_LIMIT -from pydantic import BaseModel, ConfigDict, Field, ValidationError +from pydantic import BaseModel, ConfigDict, Field, ValidationError, computed_field loggers_type = logging.Logger @@ -47,6 +48,13 @@ class ImageReasoningTaskInput(BaseModel, Generic[AnswerT]): ..., description="The expected answer to the question." ) + @computed_field + @property + def task_id(self) -> str: + """Unique identifier for the task based on question and image paths.""" + content = f"{self.question}|{sorted(self.images_paths)}" + return hashlib.sha256(content.encode()).hexdigest() + class ImageReasoningAnswer(BaseModel, Generic[AnswerT]): """Base answer for an image reasoning task.""" @@ -84,6 +92,7 @@ class ImageReasoningTask(ABC, Generic[AnswerT]): def __init__( self, + task_input: ImageReasoningTaskInput[AnswerT], logger: loggers_type | None = None, ) -> None: """ @@ -101,9 +110,28 @@ def __init__( self.logger = logger else: self.logger = logging.getLogger(__name__) - self.question: str - self.images_paths: List[str] - # TODO move here task input + + self._task_input = task_input + + @property + def question(self) -> str: + """The question to be answered.""" + return self._task_input.question + + @property + def images_paths(self) -> List[str]: + """List of image file paths.""" + return self._task_input.images_paths + + @property + def expected_answer(self) -> AnswerT: + """The expected answer to the question.""" + return self._task_input.expected_answer + + @property + def task_id(self) -> str: + """Unique identifier for the task.""" + return self._task_input.task_id def set_logger(self, logger: loggers_type): self.logger = logger diff --git a/src/rai_bench/rai_bench/vlm_benchmark/results_tracking.py b/src/rai_bench/rai_bench/vlm_benchmark/results_tracking.py index 5d8b77b71..2e5a649d2 100644 --- a/src/rai_bench/rai_bench/vlm_benchmark/results_tracking.py +++ b/src/rai_bench/rai_bench/vlm_benchmark/results_tracking.py @@ -19,6 +19,7 @@ class TaskResult(BaseModel): + task_id: str = Field(..., description="Unique identifier for the task object.") task_prompt: str = Field(..., description="The task prompt.") system_prompt: str = Field(..., description="The system prompt.") complexity: str = Field(..., description="Complexity of the task.") diff --git a/src/rai_bench/rai_bench/vlm_benchmark/tasks/tasks.py b/src/rai_bench/rai_bench/vlm_benchmark/tasks/tasks.py index 2d9f8bc8b..5d1a6dffe 100644 --- a/src/rai_bench/rai_bench/vlm_benchmark/tasks/tasks.py +++ b/src/rai_bench/rai_bench/vlm_benchmark/tasks/tasks.py @@ -66,11 +66,9 @@ def __init__( logger: loggers_type | None = None, ) -> None: super().__init__( + task_input=task_input, logger=logger, ) - self.question = task_input.question - self.images_paths = task_input.images_paths - self.expected_answer = task_input.expected_answer @property def structured_output(self) -> type[BoolAnswerWithJustification]: @@ -101,10 +99,7 @@ def __init__( task_input: QuantityImageTaskInput, logger: loggers_type | None = None, ) -> None: - super().__init__(logger=logger) - self.question = task_input.question - self.images_paths = task_input.images_paths - self.expected_answer = task_input.expected_answer + super().__init__(task_input=task_input, logger=logger) @property def type(self) -> str: @@ -135,11 +130,8 @@ def __init__( task_input: MultipleChoiceImageTaskInput, logger: loggers_type | None = None, ) -> None: - super().__init__(logger=logger) - self.question = task_input.question - self.images_paths = task_input.images_paths + super().__init__(task_input=task_input, logger=logger) self.options = task_input.options - self.expected_answer = task_input.expected_answer @property def type(self) -> str: From 084b4841fbf8118a597aa53e25c98825617dd660 Mon Sep 17 00:00:00 2001 From: Magdalena Kotynia Date: Mon, 18 Aug 2025 15:39:08 +0200 Subject: [PATCH 14/17] fix: aggregate tasks repeats results by the task id --- src/rai_bench/rai_bench/base_benchmark.py | 8 +++--- src/rai_bench/rai_bench/test_models.py | 31 +++++++++++++---------- 2 files changed, 22 insertions(+), 17 deletions(-) diff --git a/src/rai_bench/rai_bench/base_benchmark.py b/src/rai_bench/rai_bench/base_benchmark.py index abee35560..bc491a119 100644 --- a/src/rai_bench/rai_bench/base_benchmark.py +++ b/src/rai_bench/rai_bench/base_benchmark.py @@ -55,6 +55,10 @@ class ModelSummary(BaseModel): class TasksSummary(BaseModel): model_name: str = Field(..., description="Name of the LLM.") + task_id: str = Field(..., description="Unique identifier for the task.") + task_prompt: str = Field( + ..., description="The task prompt that identifies the task." + ) avg_success_rate: float = Field( ..., description="Average result for task across all repeats." ) @@ -68,9 +72,7 @@ class TasksSummary(BaseModel): ..., description="Standard deviation of the time taken across all repeats for one task.", ) - total_tasks: int = Field( - ..., description="Total number of executed tasks across all repeats per task." - ) + repeats: int = Field(..., description="Total number of repeats for task.") class TimeoutException(Exception): diff --git a/src/rai_bench/rai_bench/test_models.py b/src/rai_bench/rai_bench/test_models.py index 8820ec88f..02d290363 100644 --- a/src/rai_bench/rai_bench/test_models.py +++ b/src/rai_bench/rai_bench/test_models.py @@ -216,51 +216,52 @@ def merge_tasks_summary(bench_name: str, model_name: str, run_dir: Path) -> None if not model_dir.exists(): return - # Collect all task results from all repeats - task_data_by_prompt: Dict[str, Dict[str, List[float]]] = {} + task_data_by_id: Dict[str, Dict[str, Any]] = {} for repeat_dir in model_dir.iterdir(): if repeat_dir.is_dir() and repeat_dir.name.isdigit(): results_file = repeat_dir / DETAILED_FILE_NAME if results_file.exists(): - # Read detailed results from this repeat with open(results_file, "r") as f: reader = csv.DictReader(f) for row in reader: + task_id = row["task_id"] task_prompt = row["task_prompt"] score = float(row["score"]) total_time = float(row["total_time"]) - if task_prompt not in task_data_by_prompt: - task_data_by_prompt[task_prompt] = { + if task_id not in task_data_by_id: + task_data_by_id[task_id] = { "scores": [], "times": [], + "task_prompt": task_prompt, } - task_data_by_prompt[task_prompt]["scores"].append(score) - task_data_by_prompt[task_prompt]["times"].append(total_time) + task_data_by_id[task_id]["scores"].append(score) + task_data_by_id[task_id]["times"].append(total_time) - if not task_data_by_prompt: + if not task_data_by_id: return # Calculate statistics for each task task_summaries: List[TasksSummary] = [] - for task_prompt, data in task_data_by_prompt.items(): + for task_id, data in task_data_by_id.items(): scores = np.array(data["scores"]) times = np.array(data["times"]) + task_prompt = data["task_prompt"] task_summary = TasksSummary( model_name=model_name, + task_id=task_id, task_prompt=task_prompt, avg_success_rate=round(float(scores.mean()), 3), std_success_rate=round(float(scores.std()), 3), avg_time=round(float(times.mean()), 3), std_time=round(float(times.std()), 3), - repeats=len(scores), # TODO (mkotynia) (extract repeats in another way) + repeats=len(scores), ) task_summaries.append(task_summary) - # Save task summaries to CSV tasks_summary_file = model_dir / TASKS_SUMMARY_FILE_NAME with open(tasks_summary_file, "w", newline="") as f: if task_summaries: @@ -420,8 +421,10 @@ def test_models( bench_logger.critical( f"{bench_conf.name} benchmark for {model_name}, vendor: {vendors[i]}, execution number: {u + 1}" ) - # TODO (mkotynia): resolve unbound bench_logger - bench_logger.info(f"Merging summaries for benchmark: {bench_conf.name}") + merge_results_logger = define_benchmark_logger(out_dir=Path(out_dir)) + merge_results_logger.info( + f"Merging summaries for benchmark: {bench_conf.name}" + ) for model_name in model_names: merge_model_repeats_summary(bench_conf.name, model_name, run_dir) @@ -429,6 +432,6 @@ def test_models( merge_benchmark_summary(bench_conf.name, run_dir, model_names) - bench_logger.info( + merge_results_logger.info( f"Summary merging completed for benchmark: {bench_conf.name}" ) From 8e3c01cb004186d850b6fe2e9f00096f5732373f Mon Sep 17 00:00:00 2001 From: Magdalena Kotynia Date: Mon, 18 Aug 2025 15:44:59 +0200 Subject: [PATCH 15/17] refactor: renamed csv storing model summary --- src/rai_bench/rai_bench/test_models.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/rai_bench/rai_bench/test_models.py b/src/rai_bench/rai_bench/test_models.py index 02d290363..698a523c4 100644 --- a/src/rai_bench/rai_bench/test_models.py +++ b/src/rai_bench/rai_bench/test_models.py @@ -37,7 +37,7 @@ get_llm_model_name, ) -REPEATS_SUMMARY_FILE_NAME = "repeats_summary.csv" +MODEL_SUMMARY_FILE_NAME = "model_summary.csv" TASKS_SUMMARY_FILE_NAME = "tasks_summary.csv" BENCHMARK_SUMMARY = "benchmark_summary.csv" @@ -153,7 +153,7 @@ def merge_model_repeats_summary( repeats=len(summaries), ) - merged_file = model_dir / REPEATS_SUMMARY_FILE_NAME + merged_file = model_dir / MODEL_SUMMARY_FILE_NAME with open(merged_file, "w", newline="") as f: writer = csv.DictWriter(f, fieldnames=ModelSummary.model_fields.keys()) writer.writeheader() @@ -181,7 +181,7 @@ def merge_benchmark_summary( all_summaries: List[ModelSummary] = [] for model_name in model_names: model_dir = bench_dir / model_name - merged_file = model_dir / REPEATS_SUMMARY_FILE_NAME + merged_file = model_dir / MODEL_SUMMARY_FILE_NAME if merged_file.exists(): with open(merged_file, "r") as f: From 54177622f662b465d8787055db55d01477775c2f Mon Sep 17 00:00:00 2001 From: Magdalena Kotynia Date: Mon, 18 Aug 2025 16:06:33 +0200 Subject: [PATCH 16/17] feat: added std success rate and std time for model across all repeats --- src/rai_bench/rai_bench/base_benchmark.py | 8 ++++++++ src/rai_bench/rai_bench/test_models.py | 14 ++++++++++---- 2 files changed, 18 insertions(+), 4 deletions(-) diff --git a/src/rai_bench/rai_bench/base_benchmark.py b/src/rai_bench/rai_bench/base_benchmark.py index bc491a119..fda87ae59 100644 --- a/src/rai_bench/rai_bench/base_benchmark.py +++ b/src/rai_bench/rai_bench/base_benchmark.py @@ -41,12 +41,20 @@ class ModelSummary(BaseModel): ..., description="Percentage of successfully completed tasks across all repeats.", ) + std_success_rate: float = Field( + ..., + description="Standard deviation of success rate across all repeats for this model.", + ) avg_total_tasks: float = Field( ..., description="Average number of tasks executed through all repeats." ) avg_time: float = Field( ..., description="Average time taken across all tasks and repeats." ) + std_time: float = Field( + ..., + description="Standard deviation of time taken across all repeats for this model.", + ) repeats: int = Field( ..., description="Total number of repeats for the model for each task." diff --git a/src/rai_bench/rai_bench/test_models.py b/src/rai_bench/rai_bench/test_models.py index 698a523c4..6fab72a00 100644 --- a/src/rai_bench/rai_bench/test_models.py +++ b/src/rai_bench/rai_bench/test_models.py @@ -126,7 +126,6 @@ def merge_model_repeats_summary( if not model_dir.exists(): return - # TODO (mkotynia): create new BenchSummary model with added std of success rate and time across repeats summaries: List[RunSummary] = [] for repeat_dir in model_dir.iterdir(): if repeat_dir.is_dir() and repeat_dir.name.isdigit(): @@ -140,15 +139,22 @@ def merge_model_repeats_summary( if not summaries: return - avg_success_rate = np.mean([s.success_rate for s in summaries]) - avg_time = np.mean([s.avg_time for s in summaries]) + success_rates = [s.success_rate for s in summaries] + times = [s.avg_time for s in summaries] + total_tasks_list = [s.total_tasks for s in summaries] - total_tasks = np.mean([s.total_tasks for s in summaries]) + avg_success_rate = np.mean(success_rates) + std_success_rate = np.std(success_rates) + avg_time = np.mean(times) + std_time = np.std(times) + total_tasks = np.mean(total_tasks_list) merged_summary = ModelSummary( model_name=model_name, avg_success_rate=round(float(avg_success_rate), 2), + std_success_rate=round(float(std_success_rate), 3), avg_time=round(float(avg_time), 3), + std_time=round(float(std_time), 3), avg_total_tasks=round(float(total_tasks), 3), repeats=len(summaries), ) From 17ba4999ed42fb8872e57c05f65bf9edac5d229c Mon Sep 17 00:00:00 2001 From: Maciej Majek Date: Wed, 27 Aug 2025 14:54:25 +0200 Subject: [PATCH 17/17] fix: image paths --- src/rai_bench/rai_bench/vlm_benchmark/predefined/tasks.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/rai_bench/rai_bench/vlm_benchmark/predefined/tasks.py b/src/rai_bench/rai_bench/vlm_benchmark/predefined/tasks.py index 3ffbf1500..4bd47503d 100644 --- a/src/rai_bench/rai_bench/vlm_benchmark/predefined/tasks.py +++ b/src/rai_bench/rai_bench/vlm_benchmark/predefined/tasks.py @@ -423,14 +423,14 @@ QuantityImageTask( task_input=QuantityImageTaskInput( question="How many fallen barrels are in the image?", - images_paths=[IMG_PATH + image_13], + images_paths=[image_13], expected_answer=1, ) ), QuantityImageTask( task_input=QuantityImageTaskInput( question="How many barrels are in the image?", - images_paths=[IMG_PATH + image_13], + images_paths=[image_13], expected_answer=6, ) ), @@ -508,7 +508,7 @@ BoolImageTask( task_input=BoolImageTaskInput( question="Is worker wearing a helmet?", - images_paths=[IMG_PATH + "woman_wo_helmet_1.png"], + images_paths=[image_16], expected_answer=False, ), )