From c8a95117c0b250b1731cd34e8ebdab55f4293d9b Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Sat, 14 Sep 2024 18:39:44 +0530 Subject: [PATCH 01/18] implemented: basic structured to handle visualQA --- langtest/tasks/task.py | 31 +++++++ langtest/utils/custom_types/__init__.py | 1 + langtest/utils/custom_types/sample.py | 115 ++++++++++++++++++++++++ 3 files changed, 147 insertions(+) diff --git a/langtest/tasks/task.py b/langtest/tasks/task.py index 93af99114..ba8f42ff2 100644 --- a/langtest/tasks/task.py +++ b/langtest/tasks/task.py @@ -851,3 +851,34 @@ def create_sample( class FillMask(BaseTask): pass + + +class VisualQA(BaseTak): + _name = "visual-question-answering" + _default_col = { + "image": ["image"], + "question": ["question"], + "answer": ["answer"], + } + sample_class = samples.VisualQASample + + def create_sample( + cls, + row_data: dict, + image: str = "image", + question: str = "question", + answer: str = "answer", + dataset_name: str = "", + ) -> samples.VisualQASample: + """Create a sample.""" + keys = list(row_data.keys()) + + # auto-detect the default column names from the row_data + column_mapper = cls.column_mapping(keys, [image, question, answer]) + + return samples.VisualQASample( + image=row_data[column_mapper[image]], + question=row_data[column_mapper[question]], + answer=row_data[column_mapper[answer]], + dataset_name=dataset_name, + ) diff --git a/langtest/utils/custom_types/__init__.py b/langtest/utils/custom_types/__init__.py index 41d60e870..82e3e62f0 100644 --- a/langtest/utils/custom_types/__init__.py +++ b/langtest/utils/custom_types/__init__.py @@ -22,6 +22,7 @@ CrowsPairsSample, StereoSetSample, TextGenerationSample, + VisualQASample, ) from .helpers import Span, Transformation from .output import ( diff --git a/langtest/utils/custom_types/sample.py b/langtest/utils/custom_types/sample.py index 8477fb9bb..e0788b646 100644 --- a/langtest/utils/custom_types/sample.py +++ b/langtest/utils/custom_types/sample.py @@ -2751,6 +2751,121 @@ class FillMaskSample(TextGenerationSample): pass +class VisualQASample(BaseModel): + original_image: str + perturbed_image: str + question: str + ground_truth: str + expected_result: str + actual_result: str + dataset_name: str + category: str + test_type: str + state: str + task: str + ran_pass: bool + + def __init__(self, **data): + super().__init__(**data) + + def to_dict(self) -> Dict[str, Any]: + """ + Converts the VisualQASample object to a dictionary. + + Returns: + Dict[str, Any]: A dictionary representation of the VisualQASample object. + """ + result = { + "category": self.category, + "test_type": self.test_type, + "original_image": self.original_image, + "perturbed_image": self.perturbed_image, + "question": self.question, + "ground_truth": self.ground_truth, + "expected_result": self.expected_result, + "actual_result": self.actual_result, + "pass": self.is_pass(), + } + + return result + + def is_pass(self): + """ + Check if the VisualQASample test passes based on evaluation results. + + Returns: + bool: True if the test passes, False otherwise. + """ + if self.ran_pass is not None: + return self.ran_pass + + if self.expected_result.lower().strip() == self.actual_result.lower().strip(): + self.ran_pass = True + else: + self.ran_pass = False + + return self.ran_pass + + def run(self, model, **kwargs): + """ + Run the VisualQASample test using the provided model. + + Args: + model: The model used for VisualQASample testing. + **kwargs: Additional keyword arguments for the model. + + Returns: + bool: True + """ + dataset_name = self.dataset_name.split("-")[0].lower() + prompt_template = kwargs.get( + "user_prompt", + default_user_prompt.get(dataset_name, ""), + ) + + server_prompt = kwargs.get("server_prompt", " ") + + self.expected_result = model( + text={ + "image": self.original_image, + "question": self.question, + }, + prompt={ + "template": prompt_template, + "input_variables": ["image", "question"], + }, + server_prompt=server_prompt, + ) + self.actual_result = model( + text={ + "image": self.perturbed_image, + "question": self.question, + }, + prompt={ + "template": prompt_template, + "input_variables": ["image", "question"], + }, + server_prompt=server_prompt, + ) + return True + + def transform(self, func: Callable, params: Dict, **kwargs): + """ + Transform the original image using a specified function. + + Args: + func (Callable): The transformation function. + params (Dict): Parameters for the transformation function. + **kwargs: Additional keyword arguments for the transformation. + + """ + sens = [self.original_image] + self.perturbed_image = func(sens, **params, **kwargs) + self.category = func.__module__.split(".")[-1] + + return self + + Sample = TypeVar( "Sample", MaxScoreSample, From f7b53e658862e56663bab0fd65291da565ba3e3f Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Sat, 14 Sep 2024 18:47:36 +0530 Subject: [PATCH 02/18] Refactor VisualQASample class to include additional attributes and documentation --- langtest/utils/custom_types/sample.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/langtest/utils/custom_types/sample.py b/langtest/utils/custom_types/sample.py index e0788b646..4ccec423a 100644 --- a/langtest/utils/custom_types/sample.py +++ b/langtest/utils/custom_types/sample.py @@ -2752,6 +2752,18 @@ class FillMaskSample(TextGenerationSample): class VisualQASample(BaseModel): + """ + A class representing a sample for the Visual Question Answering task. + + Attributes: + original_image (str): The original image used for the test. + perturbed_image (str): The perturbed image used for the test. + question (str): The question asked about the image. + ground_truth (str): The ground truth answer to the question. + expected_result (str): The expected result of the test. + actual_result (str): The actual result of the test. + """ + original_image: str perturbed_image: str question: str From 6eec7caef7aa13ca417d52b1344f099a2e3180b9 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Sat, 14 Sep 2024 18:49:06 +0530 Subject: [PATCH 03/18] Refactor llm_modelhandler.py to include PretrainedModelForVisualQA class --- langtest/modelhandler/llm_modelhandler.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/langtest/modelhandler/llm_modelhandler.py b/langtest/modelhandler/llm_modelhandler.py index 35b14691c..a806fb10b 100644 --- a/langtest/modelhandler/llm_modelhandler.py +++ b/langtest/modelhandler/llm_modelhandler.py @@ -452,3 +452,13 @@ class PretrainedModelForSycophancy(PretrainedModelForQA, ModelAPI): """ pass + + +class PretrainedModelForVisualQA(PretrainedModelForQA, ModelAPI): + """A class representing a pretrained model for visual question answering. + + Inherits: + PretrainedModelForQA: The base class for pretrained models. + """ + + pass From b95ecf3334e3c6e3fde966971c8fed688ca363dc Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Sat, 14 Sep 2024 18:50:04 +0530 Subject: [PATCH 04/18] Refactor VisualQA class to fix typo in base class name --- langtest/tasks/task.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/langtest/tasks/task.py b/langtest/tasks/task.py index ba8f42ff2..3d83a0092 100644 --- a/langtest/tasks/task.py +++ b/langtest/tasks/task.py @@ -853,7 +853,7 @@ class FillMask(BaseTask): pass -class VisualQA(BaseTak): +class VisualQA(BaseTask): _name = "visual-question-answering" _default_col = { "image": ["image"], From d3e6fa595a88219e52fae9abea738215a484a128 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Sun, 15 Sep 2024 16:11:45 +0530 Subject: [PATCH 05/18] updated: image handling while loading dataset. --- langtest/datahandler/datasource.py | 10 +++- langtest/tasks/task.py | 10 ++-- langtest/utils/custom_types/sample.py | 67 ++++++++++++++++++++------- 3 files changed, 66 insertions(+), 21 deletions(-) diff --git a/langtest/datahandler/datasource.py b/langtest/datahandler/datasource.py index 4de9999f4..ce7dce368 100644 --- a/langtest/datahandler/datasource.py +++ b/langtest/datahandler/datasource.py @@ -95,6 +95,12 @@ "anti-stereotype": ["anti-stereotype"], "unrelated": ["unrelated"], }, + "visualqa": { + 'image': ['image'], + 'question': ['question'], + 'options': ['options'], + 'answer': ['answer'] + } } @@ -183,7 +189,7 @@ def __init__(self, file_path: Union[str, dict], task: TaskManager, **kwargs) -> raise ValueError(Errors.E024) if "data_source" not in file_path: - raise ValueError(Errors.E025) + raise ValueError(Errors.E025()) self._custom_label = file_path.copy() self._file_path = file_path.get("data_source") self._size = None @@ -1246,6 +1252,7 @@ class HuggingFaceDataset(BaseDataset): "summarization", "ner", "question-answering", + "visualqa", ] LIB_NAME = "datasets" @@ -1709,6 +1716,7 @@ class PandasDataset(BaseDataset): "legal", "factuality", "stereoset", + "visualqa", ] COLUMN_NAMES = {task: COLUMN_MAPPER[task] for task in supported_tasks} diff --git a/langtest/tasks/task.py b/langtest/tasks/task.py index 3d83a0092..22410dd7c 100644 --- a/langtest/tasks/task.py +++ b/langtest/tasks/task.py @@ -854,7 +854,7 @@ class FillMask(BaseTask): class VisualQA(BaseTask): - _name = "visual-question-answering" + _name = "visualqa" _default_col = { "image": ["image"], "question": ["question"], @@ -865,8 +865,9 @@ class VisualQA(BaseTask): def create_sample( cls, row_data: dict, - image: str = "image", + image: str = "image_1", question: str = "question", + options: str = "options", answer: str = "answer", dataset_name: str = "", ) -> samples.VisualQASample: @@ -877,8 +878,9 @@ def create_sample( column_mapper = cls.column_mapping(keys, [image, question, answer]) return samples.VisualQASample( - image=row_data[column_mapper[image]], + original_image=row_data[column_mapper[image]], question=row_data[column_mapper[question]], - answer=row_data[column_mapper[answer]], + options=row_data.get(column_mapper.get(options, "-"), "-"), + expected_result=row_data[column_mapper[answer]], dataset_name=dataset_name, ) diff --git a/langtest/utils/custom_types/sample.py b/langtest/utils/custom_types/sample.py index 4ccec423a..aacf629d0 100644 --- a/langtest/utils/custom_types/sample.py +++ b/langtest/utils/custom_types/sample.py @@ -2763,22 +2763,28 @@ class VisualQASample(BaseModel): expected_result (str): The expected result of the test. actual_result (str): The actual result of the test. """ + from PIL.Image import Image - original_image: str - perturbed_image: str - question: str - ground_truth: str - expected_result: str - actual_result: str - dataset_name: str - category: str - test_type: str - state: str - task: str - ran_pass: bool + original_image: Union[Image, str, Any] = None + perturbed_image: Union[Image, str, Any] = None + question: str = None + options: str = None + ground_truth: str = None + expected_result: str = None + actual_result: str = None + dataset_name: str = None + category: str = None + test_type: str = None + state: str = None + task: str = None + ran_pass: bool = None + + class Config: + arbitrary_types_allowed = True def __init__(self, **data): super().__init__(**data) + self.original_image = self.__load_image(self.original_image) def to_dict(self) -> Dict[str, Any]: """ @@ -2790,13 +2796,13 @@ def to_dict(self) -> Dict[str, Any]: result = { "category": self.category, "test_type": self.test_type, - "original_image": self.original_image, - "perturbed_image": self.perturbed_image, + "original_image": self.convert_image_to_html(self.original_image), + "perturbed_image": self.convert_image_to_html(self.perturbed_image), "question": self.question, "ground_truth": self.ground_truth, "expected_result": self.expected_result, "actual_result": self.actual_result, - "pass": self.is_pass(), + # "pass": self.is_pass(), } return result @@ -2876,7 +2882,36 @@ def transform(self, func: Callable, params: Dict, **kwargs): self.category = func.__module__.split(".")[-1] return self - + + def __load_image(self, image_path): + # check the image path as url using regex + import requests + from PIL import Image + import io + import base64 + + if isinstance(image_path, dict) and "bytes" in image_path: + image = Image.open(io.BytesIO(image_path["bytes"])) + elif isinstance(image_path, str) and re.match(r"^https?://", image_path): + response = requests.get(image_path) + image = Image.open(io.BytesIO(response.content)) + elif isinstance(image_path, str) and re.match(r"^data:image", image_path): + image = Image.open(io.BytesIO(base64.b64decode(image_path.split(",")[1]))) + elif isinstance(image_path, Image): + image = image_path + else: + image = Image.open(image_path) + return image.convert("RGB") + + def convert_image_to_html(self, Image: Image): + import io + import base64 + + if Image is not None: + buffered = io.BytesIO() + Image.save(buffered, format="PNG") + img_str = base64.b64encode(buffered.getvalue()).decode("utf-8") + return f'' Sample = TypeVar( "Sample", From 3ee5f8f3b8a997a445c5253d1adf3d522151fe08 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Sun, 15 Sep 2024 17:16:55 +0530 Subject: [PATCH 06/18] implemented the different tests under robusntess category and supported upto generating testcases. --- langtest/datahandler/datasource.py | 10 +- langtest/langtest.py | 3 + langtest/tasks/task.py | 2 +- langtest/transform/__init__.py | 3 + langtest/transform/image/__init__.py | 3 + langtest/transform/image/robustness.py | 138 +++++++++++++++++++++++++ langtest/transform/utils.py | 2 + langtest/utils/custom_types/sample.py | 27 +++-- 8 files changed, 172 insertions(+), 16 deletions(-) create mode 100644 langtest/transform/image/__init__.py create mode 100644 langtest/transform/image/robustness.py diff --git a/langtest/datahandler/datasource.py b/langtest/datahandler/datasource.py index ce7dce368..be1ee2f16 100644 --- a/langtest/datahandler/datasource.py +++ b/langtest/datahandler/datasource.py @@ -96,11 +96,11 @@ "unrelated": ["unrelated"], }, "visualqa": { - 'image': ['image'], - 'question': ['question'], - 'options': ['options'], - 'answer': ['answer'] - } + "image": ["image"], + "question": ["question"], + "options": ["options"], + "answer": ["answer"], + }, } diff --git a/langtest/langtest.py b/langtest/langtest.py index d7a1f15cd..4bc47b656 100644 --- a/langtest/langtest.py +++ b/langtest/langtest.py @@ -838,6 +838,7 @@ def testcases(self, additional_cols=False) -> pd.DataFrame: "model_name", "category", "test_type", + "original_image", "original", "context", "original_context", @@ -863,7 +864,9 @@ def testcases(self, additional_cols=False) -> pd.DataFrame: "correct_sentence", "incorrect_sentence", "perturbed_context", + "perturbed_image", "perturbed_question", + "question", "ground_truth", "options", "expected_result", diff --git a/langtest/tasks/task.py b/langtest/tasks/task.py index 22410dd7c..e7e6f1e04 100644 --- a/langtest/tasks/task.py +++ b/langtest/tasks/task.py @@ -875,7 +875,7 @@ def create_sample( keys = list(row_data.keys()) # auto-detect the default column names from the row_data - column_mapper = cls.column_mapping(keys, [image, question, answer]) + column_mapper = cls.column_mapping(keys, [image, question, options, answer]) return samples.VisualQASample( original_image=row_data[column_mapper[image]], diff --git a/langtest/transform/__init__.py b/langtest/transform/__init__.py index 3cb59ebd6..0c4f41c9b 100644 --- a/langtest/transform/__init__.py +++ b/langtest/transform/__init__.py @@ -22,6 +22,8 @@ from langtest.transform.grammar import GrammarTestFactory from langtest.transform.safety import SafetyTestFactory +from langtest.transform import image + # Fixing the asyncio event loop nest_asyncio.apply() @@ -47,4 +49,5 @@ SycophancyTestFactory, GrammarTestFactory, SafetyTestFactory, + image, ] diff --git a/langtest/transform/image/__init__.py b/langtest/transform/image/__init__.py new file mode 100644 index 000000000..f02586ce0 --- /dev/null +++ b/langtest/transform/image/__init__.py @@ -0,0 +1,3 @@ +from .robustness import ImageResizing, ImageRotation, ImageBlur, ImageNoise + +__all__ = [ImageResizing, ImageRotation, ImageBlur, ImageNoise] diff --git a/langtest/transform/image/robustness.py b/langtest/transform/image/robustness.py new file mode 100644 index 000000000..656327924 --- /dev/null +++ b/langtest/transform/image/robustness.py @@ -0,0 +1,138 @@ +import random +from typing import Coroutine, List, Tuple, Union +from langtest.logger import logger +from langtest.modelhandler.modelhandler import ModelAPI +from langtest.transform.robustness import BaseRobustness +from langtest.utils.custom_types.sample import Sample +from PIL import Image, ImageFilter + + +class ImageResizing(BaseRobustness): + alias_name = "image_resize" + supported_tasks = ["visualqa"] + + @staticmethod + def transform( + sample_list: List[Sample], + resize: Union[float, Tuple[int, int]] = 0.5, + *args, + **kwargs, + ) -> List[Sample]: + for sample in sample_list: + sample.category = "robustness" + sample.test_type = "image_resize" + if isinstance(resize, float): + sample.perturbed_image = sample.original_image.resize( + ( + int(sample.original_image.width * resize), + int(sample.original_image.height * resize), + ) + ) + else: + sample.perturbed_image = sample.original_image.resize(resize) + + return sample_list + + +class ImageRotation(BaseRobustness): + alias_name = "image_rotate" + supported_tasks = ["visualqa"] + + @staticmethod + def transform( + sample_list: List[Sample], angle: int = 90, *args, **kwargs + ) -> List[Sample]: + for sample in sample_list: + sample.category = "robustness" + sample.test_type = "image_rotate" + sample.perturbed_image = sample.original_image.rotate(angle) + + return sample_list + + +class ImageBlur(BaseRobustness): + alias_name = "image_blur" + supported_tasks = ["visualqa"] + + @staticmethod + def transform( + sample_list: List[Sample], radius: int = 2, *args, **kwargs + ) -> List[Sample]: + for sample in sample_list: + sample.category = "robustness" + sample.test_type = "image_blur" + sample.perturbed_image = sample.original_image.filter( + ImageFilter.GaussianBlur(radius) + ) + + return sample_list + + +class ImageNoise(BaseRobustness): + alias_name = "image_noise" + supported_tasks = ["visualqa"] + + @classmethod + def transform( + cls, sample_list: List[Sample], noise: float = 0.1, *args, **kwargs # Noise level + ) -> List[Sample]: + try: + if noise < 0 or noise > 1: + raise ValueError("Noise level must be in the range [0, 1].") + + # Get image size + for sample in sample_list: + sample.category = "robustness" + sample.test_type = "image_noise" + sample.perturbed_image = cls.add_noise(sample.original_image, noise) + return sample_list + + except Exception as e: + logger.error(f"Error in adding noise to the image: {e}") + raise e + + def add_noise(self, image: Image.Image, noise_level: float) -> Image: + width, height = image.size + + # Create a new image to hold the noisy version + noisy_image = image.copy() + pixels = noisy_image.load() # Access pixel data + + # Check if the image is grayscale or RGB + if image.mode == "L": # Grayscale image + for x in range(width): + for y in range(height): + # Get the pixel value + gray = image.getpixel((x, y)) + + # Generate random noise + noise_gray = int(random.gauss(0, 255 * noise_level)) + + # Add noise and clip the value to stay in [0, 255] + new_gray = max(0, min(255, gray + noise_gray)) + + # Set the new pixel value + pixels[x, y] = new_gray + + elif image.mode == "RGB": # Color image + for x in range(width): + for y in range(height): + r, g, b = image.getpixel((x, y)) # Get the RGB values of the pixel + + # Generate random noise for each channel + noise_r = int(random.gauss(0, 255 * noise_level)) + noise_g = int(random.gauss(0, 255 * noise_level)) + noise_b = int(random.gauss(0, 255 * noise_level)) + + # Add noise to each channel and clip values to stay in range [0, 255] + new_r = max(0, min(255, r + noise_r)) + new_g = max(0, min(255, g + noise_g)) + new_b = max(0, min(255, b + noise_b)) + + # Set the new pixel value + pixels[x, y] = (new_r, new_g, new_b) + + else: + raise ValueError("The input image must be in 'L' (grayscale) or 'RGB' mode.") + + return noisy_image diff --git a/langtest/transform/utils.py b/langtest/transform/utils.py index 4540155bf..0fc2dcd23 100644 --- a/langtest/transform/utils.py +++ b/langtest/transform/utils.py @@ -397,6 +397,8 @@ def filter_unique_samples(task: str, transformed_samples: list, test_name: str): no_transformation_applied_tests[test_name] += 1 else: no_transformation_applied_tests[test_name] = 1 + elif task == "visualqa": + return transformed_samples, no_transformation_applied_tests else: for sample in transformed_samples: if sample.original.replace(" ", "") != sample.test_case.replace(" ", ""): diff --git a/langtest/utils/custom_types/sample.py b/langtest/utils/custom_types/sample.py index aacf629d0..c08781c48 100644 --- a/langtest/utils/custom_types/sample.py +++ b/langtest/utils/custom_types/sample.py @@ -2763,6 +2763,7 @@ class VisualQASample(BaseModel): expected_result (str): The expected result of the test. actual_result (str): The actual result of the test. """ + from PIL.Image import Image original_image: Union[Image, str, Any] = None @@ -2799,12 +2800,14 @@ def to_dict(self) -> Dict[str, Any]: "original_image": self.convert_image_to_html(self.original_image), "perturbed_image": self.convert_image_to_html(self.perturbed_image), "question": self.question, - "ground_truth": self.ground_truth, - "expected_result": self.expected_result, - "actual_result": self.actual_result, + # "expected_result": self.expected_result, + # "actual_result": self.actual_result, # "pass": self.is_pass(), } + if self.options is not None: + result["options"] = self.options + return result def is_pass(self): @@ -2882,13 +2885,13 @@ def transform(self, func: Callable, params: Dict, **kwargs): self.category = func.__module__.split(".")[-1] return self - + def __load_image(self, image_path): # check the image path as url using regex - import requests + import requests from PIL import Image import io - import base64 + import base64 if isinstance(image_path, dict) and "bytes" in image_path: image = Image.open(io.BytesIO(image_path["bytes"])) @@ -2902,17 +2905,20 @@ def __load_image(self, image_path): else: image = Image.open(image_path) return image.convert("RGB") - - def convert_image_to_html(self, Image: Image): + + def convert_image_to_html(self, image: Image): import io import base64 - if Image is not None: + if image is not None: + image = image.copy() buffered = io.BytesIO() - Image.save(buffered, format="PNG") + image.thumbnail((400, 400)) + image.save(buffered, format="PNG") img_str = base64.b64encode(buffered.getvalue()).decode("utf-8") return f'' + Sample = TypeVar( "Sample", MaxScoreSample, @@ -2934,4 +2940,5 @@ def convert_image_to_html(self, Image: Image): LegalSample, CrowsPairsSample, StereoSetSample, + VisualQASample, ) From 3dd6770c3720acbf83f3830b3954c61dac1173c2 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Sun, 15 Sep 2024 18:38:38 +0530 Subject: [PATCH 07/18] Refactor image handling in robustness tests --- langtest/transform/image/robustness.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/langtest/transform/image/robustness.py b/langtest/transform/image/robustness.py index 656327924..60610f4fc 100644 --- a/langtest/transform/image/robustness.py +++ b/langtest/transform/image/robustness.py @@ -40,12 +40,12 @@ class ImageRotation(BaseRobustness): @staticmethod def transform( - sample_list: List[Sample], angle: int = 90, *args, **kwargs + sample_list: List[Sample], angle: int = 90, exapand = True, *args, **kwargs ) -> List[Sample]: for sample in sample_list: sample.category = "robustness" sample.test_type = "image_rotate" - sample.perturbed_image = sample.original_image.rotate(angle) + sample.perturbed_image = sample.original_image.rotate(angle, expand=True) return sample_list @@ -84,14 +84,15 @@ def transform( for sample in sample_list: sample.category = "robustness" sample.test_type = "image_noise" - sample.perturbed_image = cls.add_noise(sample.original_image, noise) + sample.perturbed_image = cls.add_noise(image=sample.original_image, noise_level=noise) return sample_list except Exception as e: logger.error(f"Error in adding noise to the image: {e}") raise e - def add_noise(self, image: Image.Image, noise_level: float) -> Image: + @staticmethod + def add_noise(image: Image.Image, noise_level: float) -> Image: width, height = image.size # Create a new image to hold the noisy version From d95e558c56142a4594f9e3a9526fe73d27bf6fd6 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Sun, 15 Sep 2024 22:06:13 +0530 Subject: [PATCH 08/18] Refactor image handling in robustness tests and add support for multimodal VQA task --- langtest/modelhandler/llm_modelhandler.py | 59 ++++++++++++++++++++++- langtest/transform/image/robustness.py | 9 ++-- langtest/utils/custom_types/sample.py | 58 +++++++++++++++------- 3 files changed, 104 insertions(+), 22 deletions(-) diff --git a/langtest/modelhandler/llm_modelhandler.py b/langtest/modelhandler/llm_modelhandler.py index b808d759c..d71807234 100644 --- a/langtest/modelhandler/llm_modelhandler.py +++ b/langtest/modelhandler/llm_modelhandler.py @@ -13,6 +13,7 @@ import logging from functools import lru_cache from langtest.utils.custom_types.helpers import HashableDict +from langchain.chat_models.base import BaseChatModel class PretrainedModelForQA(ModelAPI): @@ -461,4 +462,60 @@ class PretrainedModelForVisualQA(PretrainedModelForQA, ModelAPI): PretrainedModelForQA: The base class for pretrained models. """ - pass + @lru_cache(maxsize=102400) + def predict( + self, text: Union[str, dict], prompt: dict, images: List[Any], *args, **kwargs + ): + """Perform prediction using the pretrained model. + + Args: + text (Union[str, dict]): The input text or dictionary. + prompt (dict): The prompt configuration. + images (List[Any]): The list of images. + *args: Additional positional arguments. + **kwargs: Additional keyword arguments. + + Returns: + dict: A dictionary containing the prediction result. + - 'result': The prediction result. + """ + try: + if not isinstance(self.model, BaseChatModel): + ValueError("visualQA task is only supported for chat models") + + # if isinstance(text, dict): + # input_variables = list(text.keys()) + # # input_variables in to string enclosed by {} + # template = "\n".join(f"{var.title()}: {{{var}}}" for var in input_variables) + # else: + # input_variables = ["text"] + # template = "{text}" + + # prompt = { + # "input_variables": input_variables, + # "template": f"Answer the question based on the image and the text.\n {template} " + # } + + prompt_template = PromptTemplate(**prompt) + from langchain_core.messages import HumanMessage + + images = [ + { + "type": "image_url", + "image_url": {"url": image}, + } + for image in images + ] + + messages = HumanMessage( + content=[ + {"type": "text", "text": prompt_template.format(**text)}, + *images, + ] + ) + + response = self.model.invoke([messages]) + return response.content + + except Exception as e: + raise ValueError(Errors.E089(error_message=e)) diff --git a/langtest/transform/image/robustness.py b/langtest/transform/image/robustness.py index 60610f4fc..d6508724a 100644 --- a/langtest/transform/image/robustness.py +++ b/langtest/transform/image/robustness.py @@ -1,7 +1,6 @@ import random -from typing import Coroutine, List, Tuple, Union +from typing import List, Tuple, Union from langtest.logger import logger -from langtest.modelhandler.modelhandler import ModelAPI from langtest.transform.robustness import BaseRobustness from langtest.utils.custom_types.sample import Sample from PIL import Image, ImageFilter @@ -40,7 +39,7 @@ class ImageRotation(BaseRobustness): @staticmethod def transform( - sample_list: List[Sample], angle: int = 90, exapand = True, *args, **kwargs + sample_list: List[Sample], angle: int = 90, exapand=True, *args, **kwargs ) -> List[Sample]: for sample in sample_list: sample.category = "robustness" @@ -84,7 +83,9 @@ def transform( for sample in sample_list: sample.category = "robustness" sample.test_type = "image_noise" - sample.perturbed_image = cls.add_noise(image=sample.original_image, noise_level=noise) + sample.perturbed_image = cls.add_noise( + image=sample.original_image, noise_level=noise + ) return sample_list except Exception as e: diff --git a/langtest/utils/custom_types/sample.py b/langtest/utils/custom_types/sample.py index c08781c48..168ba33f7 100644 --- a/langtest/utils/custom_types/sample.py +++ b/langtest/utils/custom_types/sample.py @@ -3,6 +3,8 @@ import importlib from typing import Any, Dict, List, Optional, Tuple, TypeVar, Union, Callable from copy import deepcopy + +from langtest.modelhandler.modelhandler import ModelAPI from ...errors import Errors from pydantic import BaseModel, PrivateAttr, validator, Field from .helpers import Transformation, Span @@ -2827,7 +2829,7 @@ def is_pass(self): return self.ran_pass - def run(self, model, **kwargs): + def run(self, model: ModelAPI, **kwargs): """ Run the VisualQASample test using the provided model. @@ -2838,34 +2840,44 @@ def run(self, model, **kwargs): Returns: bool: True """ + dataset_name = self.dataset_name.split("-")[0].lower() prompt_template = kwargs.get( "user_prompt", - default_user_prompt.get(dataset_name, ""), + default_user_prompt.get(dataset_name, "Question: {question}\n {options}"), ) server_prompt = kwargs.get("server_prompt", " ") - self.expected_result = model( - text={ - "image": self.original_image, - "question": self.question, - }, - prompt={ + text_dict = { + "question": self.question, + } + input_variables = ["question"] + + if self.options is not None: + text_dict["options"] = self.options + input_variables.append("options") + + payload = { + "text": text_dict, + "prompt": { "template": prompt_template, - "input_variables": ["image", "question"], + "input_variables": input_variables, }, + } + + # convert the image to base64 url + orig_image = self.convert_image_to_bas64_url(self.original_image) + pred_image = self.convert_image_to_bas64_url(self.perturbed_image) + + self.expected_result = model( + **payload, + images=(orig_image,), server_prompt=server_prompt, ) self.actual_result = model( - text={ - "image": self.perturbed_image, - "question": self.question, - }, - prompt={ - "template": prompt_template, - "input_variables": ["image", "question"], - }, + **payload, + images=(pred_image,), server_prompt=server_prompt, ) return True @@ -2918,6 +2930,18 @@ def convert_image_to_html(self, image: Image): img_str = base64.b64encode(buffered.getvalue()).decode("utf-8") return f'' + def convert_image_to_bas64_url(self, image: Image): + import io + import base64 + + if image is not None: + image = image.copy() + buffered = io.BytesIO() + image.thumbnail((400, 400)) + image.save(buffered, format="PNG") + img_str = base64.b64encode(buffered.getvalue()).decode("utf-8") + return f"data:image/png;base64,{img_str}" + Sample = TypeVar( "Sample", From ebd7bfd54e816390c3890d3ae7b7e3d83b966c2c Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Sun, 15 Sep 2024 23:30:06 +0530 Subject: [PATCH 09/18] Refactor image handling in robustness tests and update VisualQASample class --- langtest/langtest.py | 3 + langtest/tasks/task.py | 10 +- langtest/utils/custom_types/sample.py | 186 ++++++++++++++++++++++---- 3 files changed, 173 insertions(+), 26 deletions(-) diff --git a/langtest/langtest.py b/langtest/langtest.py index 4bc47b656..09df1b57d 100644 --- a/langtest/langtest.py +++ b/langtest/langtest.py @@ -605,6 +605,7 @@ def generated_results(self) -> Optional[pd.DataFrame]: "model_name", "category", "test_type", + "original_image", "original", "context", "prompt", @@ -613,8 +614,10 @@ def generated_results(self) -> Optional[pd.DataFrame]: "completion", "test_case", "perturbed_context", + "perturbed_image", "perturbed_question", "sentence", + "question", "patient_info_A", "patient_info_B", "case", diff --git a/langtest/tasks/task.py b/langtest/tasks/task.py index e7e6f1e04..0e5134eae 100644 --- a/langtest/tasks/task.py +++ b/langtest/tasks/task.py @@ -877,10 +877,18 @@ def create_sample( # auto-detect the default column names from the row_data column_mapper = cls.column_mapping(keys, [image, question, options, answer]) + options = row_data.get(column_mapper.get(options, "-"), "-") + + if len(options) > 3 and options[0] == "[" and options[-1] == "]": + options = ast.literal_eval(row_data[column_mapper["options"]]) + options = "\n".join( + [f"{chr(65 + i)}. {option}" for i, option in enumerate(options)] + ) + return samples.VisualQASample( original_image=row_data[column_mapper[image]], question=row_data[column_mapper[question]], - options=row_data.get(column_mapper.get(options, "-"), "-"), + options=options, expected_result=row_data[column_mapper[answer]], dataset_name=dataset_name, ) diff --git a/langtest/utils/custom_types/sample.py b/langtest/utils/custom_types/sample.py index 168ba33f7..dab2db532 100644 --- a/langtest/utils/custom_types/sample.py +++ b/langtest/utils/custom_types/sample.py @@ -2773,14 +2773,21 @@ class VisualQASample(BaseModel): question: str = None options: str = None ground_truth: str = None - expected_result: str = None - actual_result: str = None + expected_results: str = None + actual_results: str = None dataset_name: str = None category: str = None test_type: str = None state: str = None task: str = None ran_pass: bool = None + metric_name: str = None + config: Union[str, dict] = None + state: str = None + task: str = Field(default="visualqa", const=True) + distance_result: float = None + eval_model: str = None + feedback: str = None class Config: arbitrary_types_allowed = True @@ -2796,38 +2803,35 @@ def to_dict(self) -> Dict[str, Any]: Returns: Dict[str, Any]: A dictionary representation of the VisualQASample object. """ + self.__update_params() + result = { "category": self.category, "test_type": self.test_type, "original_image": self.convert_image_to_html(self.original_image), "perturbed_image": self.convert_image_to_html(self.perturbed_image), "question": self.question, - # "expected_result": self.expected_result, - # "actual_result": self.actual_result, - # "pass": self.is_pass(), } if self.options is not None: result["options"] = self.options - return result - - def is_pass(self): - """ - Check if the VisualQASample test passes based on evaluation results. - - Returns: - bool: True if the test passes, False otherwise. - """ - if self.ran_pass is not None: - return self.ran_pass - - if self.expected_result.lower().strip() == self.actual_result.lower().strip(): - self.ran_pass = True - else: - self.ran_pass = False + if self.state == "done": + if self.expected_results is not None and self.actual_results is not None: + result.update( + { + "expected_result": self.expected_results, + "actual_result": self.actual_results, + "pass": self.is_pass(), + } + ) + if "evaluation" in self.config and "metric" in self.config["evaluation"]: + if self.config["evaluation"]["metric"].lower() == "prometheus_eval": + result.update({"feedback": self.feedback}) + elif self.config["evaluation"]["metric"].lower() != "llm_eval": + result.update({"eval_score": self.distance_result}) - return self.ran_pass + return result def run(self, model: ModelAPI, **kwargs): """ @@ -2844,7 +2848,38 @@ def run(self, model: ModelAPI, **kwargs): dataset_name = self.dataset_name.split("-")[0].lower() prompt_template = kwargs.get( "user_prompt", - default_user_prompt.get(dataset_name, "Question: {question}\n {options}"), + default_user_prompt.get( + dataset_name, + ( + """You are an AI Vision bot specializing in providing accurate and concise answers to multiple-choice questions. You will be presented with a question and options. Choose the correct answer. + +Example: + +Question: What is the capital of France ? + +Options: +A. Berlin +B. Madrid +C. Paris +D. Rome + +Answer: C. Paris. + +Example 2: + +Question: What is in the image ? + +Options: +A. Dog +B. Cat +C. Elephant +D. Ear + +Answer: UnRecognizable. +""" + " Similary \n Question: {question}\nOptions: {options}\n Answer:" + ), + ), ) server_prompt = kwargs.get("server_prompt", " ") @@ -2870,12 +2905,12 @@ def run(self, model: ModelAPI, **kwargs): orig_image = self.convert_image_to_bas64_url(self.original_image) pred_image = self.convert_image_to_bas64_url(self.perturbed_image) - self.expected_result = model( + self.expected_results = model( **payload, images=(orig_image,), server_prompt=server_prompt, ) - self.actual_result = model( + self.actual_results = model( **payload, images=(pred_image,), server_prompt=server_prompt, @@ -2942,6 +2977,107 @@ def convert_image_to_bas64_url(self, image: Image): img_str = base64.b64encode(buffered.getvalue()).decode("utf-8") return f"data:image/png;base64,{img_str}" + def __update_params(self): + from ...langtest import HARNESS_CONFIG as harness_config + + self.config = harness_config + self.metric_name = ( + self.config.get("evaluation", {}).get("metric", "llm_eval").lower() + ) + + if self.state == "done": + from ...langtest import EVAL_MODEL + + if ( + "evaluation" in harness_config + and "metric" in harness_config["evaluation"] + ): + if harness_config["evaluation"]["metric"].lower() == "llm_eval": + model = harness_config["evaluation"].get("model", None) + hub = harness_config["evaluation"].get("hub", None) + if model and hub: + from ...tasks import TaskManager + + load_eval_model = TaskManager(self.task) + self.eval_model = load_eval_model.model( + model, hub, **harness_config.get("model_parameters", {}) + ) + + else: + self.eval_model = EVAL_MODEL + + def is_pass(self) -> bool: + """Checks if the sample has passed the evaluation. + + Returns: + bool: True if the sample passed the evaluation, False otherwise. + """ + + if self.ran_pass is not None: + return self.ran_pass + elif self.expected_results.strip().lower() == self.actual_results.strip().lower(): + self.ran_pass = True + return True + else: + self.__update_params() + try: + metric_module = importlib.import_module( + "langtest.utils.custom_types.helpers" + ) + metric_function = getattr(metric_module, f"is_pass_{self.metric_name}") + except (ImportError, AttributeError): + raise ValueError(f"Metric '{self.metric_name}' not found.") + + if self.metric_name == "string_distance": + selected_distance = self.config["evaluation"].get("distance", "jaro") + threshold = self.config["evaluation"].get("threshold") + + elif self.metric_name == "embedding_distance": + selected_distance = self.config["evaluation"].get("distance", "cosine") + threshold = self.config["evaluation"].get("threshold") + + if self.metric_name in ( + "string_distance", + "embedding_distance", + ): + self.distance_result, result = metric_function( + answer=self.expected_results, + prediction=self.actual_results, + selected_distance=selected_distance, + threshold=threshold, + ) + self.ran_pass = result + return result + elif self.metric_name == "llm_eval": + if isinstance(self.eval_model, dict): + self.eval_model = list(self.eval_model.values())[-1] + result = metric_function( + eval_model=self.eval_model, + dataset_name=self.dataset_name, + original_question=" " + self.question, + answer=self.expected_results, + perturbed_question=" " + self.question, + prediction=self.actual_results, + ) + + # self.ran_pass = result + # return result + # elif self.metric_name == "prometheus_eval": + # result = metric_function( + # task=self.task, + # original_question=self.original_question, + # expected_results=self.expected_results, + # actual_results=self.actual_results, + # category=self.category, + # original_context=self.original_context, + # options=self.options, + # ) + # self.ran_pass = result.get("score", False) + # self.feedback = result.get("feedback", None) + # return self.ran_pass + else: + raise ValueError(f"Metric '{self.metric_name}' not found.") + Sample = TypeVar( "Sample", From 45384908bcce51faed3e1a730665e8d77ce44a42 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Sun, 15 Sep 2024 23:43:16 +0530 Subject: [PATCH 10/18] Refactor image handling in robustness tests and exclude image-related perturbations --- tests/test_robustness.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/test_robustness.py b/tests/test_robustness.py index 70e6bd78f..a25d18028 100644 --- a/tests/test_robustness.py +++ b/tests/test_robustness.py @@ -469,7 +469,8 @@ def setUp(self) -> None: test: list(scenarios.keys()) for test, scenarios in test_scenarios.items() } - self.perturbations_list = self.available_tests["robustness"] + self.perturbations_list = [i for i in self.available_tests["robustness"] if not i.startswith("image_")] + self.supported_tests = self.available_test() self.samples = { "question-answering": [ From 41f0db26aeee7ac268f44ad51670190b8f46bf37 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Sun, 15 Sep 2024 23:47:49 +0530 Subject: [PATCH 11/18] fixed: format issues. --- tests/test_robustness.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tests/test_robustness.py b/tests/test_robustness.py index a25d18028..8b332db87 100644 --- a/tests/test_robustness.py +++ b/tests/test_robustness.py @@ -469,7 +469,9 @@ def setUp(self) -> None: test: list(scenarios.keys()) for test, scenarios in test_scenarios.items() } - self.perturbations_list = [i for i in self.available_tests["robustness"] if not i.startswith("image_")] + self.perturbations_list = [ + i for i in self.available_tests["robustness"] if not i.startswith("image_") + ] self.supported_tests = self.available_test() self.samples = { From 3521927d2a851f67504229b20803c43d45cb9eac Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Mon, 16 Sep 2024 11:35:42 +0530 Subject: [PATCH 12/18] Refactor image handling in robustness tests and remove commented code --- langtest/modelhandler/llm_modelhandler.py | 14 +------------- langtest/utils/custom_types/sample.py | 18 +++--------------- 2 files changed, 4 insertions(+), 28 deletions(-) diff --git a/langtest/modelhandler/llm_modelhandler.py b/langtest/modelhandler/llm_modelhandler.py index d71807234..968928e12 100644 --- a/langtest/modelhandler/llm_modelhandler.py +++ b/langtest/modelhandler/llm_modelhandler.py @@ -483,19 +483,7 @@ def predict( if not isinstance(self.model, BaseChatModel): ValueError("visualQA task is only supported for chat models") - # if isinstance(text, dict): - # input_variables = list(text.keys()) - # # input_variables in to string enclosed by {} - # template = "\n".join(f"{var.title()}: {{{var}}}" for var in input_variables) - # else: - # input_variables = ["text"] - # template = "{text}" - - # prompt = { - # "input_variables": input_variables, - # "template": f"Answer the question based on the image and the text.\n {template} " - # } - + # prepare prompt prompt_template = PromptTemplate(**prompt) from langchain_core.messages import HumanMessage diff --git a/langtest/utils/custom_types/sample.py b/langtest/utils/custom_types/sample.py index dab2db532..fa0e686da 100644 --- a/langtest/utils/custom_types/sample.py +++ b/langtest/utils/custom_types/sample.py @@ -3060,21 +3060,9 @@ def is_pass(self) -> bool: prediction=self.actual_results, ) - # self.ran_pass = result - # return result - # elif self.metric_name == "prometheus_eval": - # result = metric_function( - # task=self.task, - # original_question=self.original_question, - # expected_results=self.expected_results, - # actual_results=self.actual_results, - # category=self.category, - # original_context=self.original_context, - # options=self.options, - # ) - # self.ran_pass = result.get("score", False) - # self.feedback = result.get("feedback", None) - # return self.ran_pass + self.ran_pass = result + return result + else: raise ValueError(f"Metric '{self.metric_name}' not found.") From a87e96c6b113ed7ed35ad3e8a00e14223708b1da Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Mon, 16 Sep 2024 15:00:16 +0530 Subject: [PATCH 13/18] Refactor image handling in robustness tests and update VisualQASample class for huggingface DataSource. --- langtest/datahandler/datasource.py | 2 +- langtest/utils/custom_types/sample.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/langtest/datahandler/datasource.py b/langtest/datahandler/datasource.py index be1ee2f16..c12a11662 100644 --- a/langtest/datahandler/datasource.py +++ b/langtest/datahandler/datasource.py @@ -96,7 +96,7 @@ "unrelated": ["unrelated"], }, "visualqa": { - "image": ["image"], + "image": ["image", "image_1"], "question": ["question"], "options": ["options"], "answer": ["answer"], diff --git a/langtest/utils/custom_types/sample.py b/langtest/utils/custom_types/sample.py index fa0e686da..f6e088b39 100644 --- a/langtest/utils/custom_types/sample.py +++ b/langtest/utils/custom_types/sample.py @@ -2936,7 +2936,7 @@ def transform(self, func: Callable, params: Dict, **kwargs): def __load_image(self, image_path): # check the image path as url using regex import requests - from PIL import Image + from PIL.Image import Image import io import base64 @@ -2960,7 +2960,7 @@ def convert_image_to_html(self, image: Image): if image is not None: image = image.copy() buffered = io.BytesIO() - image.thumbnail((400, 400)) + image.thumbnail((200, 200)) image.save(buffered, format="PNG") img_str = base64.b64encode(buffered.getvalue()).decode("utf-8") return f'' From 04e18e34059acc11abde791c4944b4899758461c Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Mon, 16 Sep 2024 15:00:50 +0530 Subject: [PATCH 14/18] - added new tests in image robustness. --- langtest/transform/image/robustness.py | 146 +++++++++++++++++++++++++ 1 file changed, 146 insertions(+) diff --git a/langtest/transform/image/robustness.py b/langtest/transform/image/robustness.py index d6508724a..3444abfe9 100644 --- a/langtest/transform/image/robustness.py +++ b/langtest/transform/image/robustness.py @@ -138,3 +138,149 @@ def add_noise(image: Image.Image, noise_level: float) -> Image: raise ValueError("The input image must be in 'L' (grayscale) or 'RGB' mode.") return noisy_image + + +class ImageConstrast(BaseRobustness): + alias_name = "image_contrast" + supported_tasks = ["visualqa"] + + @staticmethod + def transform( + sample_list: List[Sample], contrast_factor: float = 0.5, *args, **kwargs + ) -> List[Sample]: + from PIL import ImageEnhance + + if contrast_factor < 0: + raise ValueError("Contrast factor must be above 0.") + + for sample in sample_list: + sample.category = "robustness" + sample.test_type = "image_contrast" + img = ImageEnhance.Contrast(sample.original_image) + sample.perturbed_image = img.enhance(contrast_factor) + + return sample_list + + +class ImageBrightness(BaseRobustness): + alias_name = "image_brightness" + supported_tasks = ["visualqa"] + + @staticmethod + def transform( + sample_list: List[Sample], brightness_factor: float = 0.3, *args, **kwargs + ) -> List[Sample]: + from PIL import ImageEnhance + + if brightness_factor < 0: + raise ValueError("Brightness factor must be above 0.") + + for sample in sample_list: + sample.category = "robustness" + sample.test_type = "image_brightness" + enchancer = ImageEnhance.Brightness(sample.original_image) + sample.perturbed_image = enchancer.enhance(brightness_factor) + + return sample_list + + +class ImageSharpness(BaseRobustness): + alias_name = "image_sharpness" + supported_tasks = ["visualqa"] + + @staticmethod + def transform( + sample_list: List[Sample], sharpness_factor: float = 1.5, *args, **kwargs + ) -> List[Sample]: + from PIL import ImageEnhance + + if sharpness_factor < 0: + raise ValueError("Sharpness factor must be above 0.") + + for sample in sample_list: + sample.category = "robustness" + sample.test_type = "image_sharpness" + enchancer = ImageEnhance.Sharpness(sample.original_image) + sample.perturbed_image = enchancer.enhance(sharpness_factor) + + return sample_list + + +class ImageColor(BaseRobustness): + 3 + alias_name = "image_color" + supported_tasks = ["visualqa"] + + @staticmethod + def transform( + sample_list: List[Sample], color_factor: float = 0, *args, **kwargs + ) -> List[Sample]: + from PIL import ImageEnhance + + if color_factor < 0: + raise ValueError("Color factor must be in the range [0, inf].") + + for sample in sample_list: + sample.category = "robustness" + sample.test_type = "image_color" + enchancer = ImageEnhance.Color(sample.original_image) + sample.perturbed_image = enchancer.enhance(color_factor) + + return sample_list + + +class ImageFlip(BaseRobustness): + alias_name = "image_flip" + supported_tasks = ["visualqa"] + + @staticmethod + def transform( + sample_list: List[Sample], flip: str = "horizontal", *args, **kwargs + ) -> List[Sample]: + if flip not in ["horizontal", "vertical"]: + raise ValueError("Flip must be either 'horizontal' or 'vertical'.") + + for sample in sample_list: + sample.category = "robustness" + sample.test_type = "image_flip" + if flip == "horizontal": + sample.perturbed_image = sample.original_image.transpose( + Image.FLIP_LEFT_RIGHT + ) + else: + sample.perturbed_image = sample.original_image.transpose( + Image.FLIP_TOP_BOTTOM + ) + + return sample_list + + +class ImageCrop(BaseRobustness): + alias_name = "image_crop" + supported_tasks = ["visualqa"] + + @staticmethod + def transform( + sample_list: List[Sample], + crop_size: Union[float, Tuple[int, int]] = (100, 100), + *args, + **kwargs, + ) -> List[Sample]: + for sample in sample_list: + sample.category = "robustness" + sample.test_type = "image_crop" + if isinstance(crop_size, float): + sample.perturbed_image = sample.original_image.crop( + ( + 0, + 0, + int(sample.original_image.width * crop_size), + int(sample.original_image.height * crop_size), + ) + ) + else: + sample.perturbed_image = sample.original_image.crop( + (0, 0, crop_size[0], crop_size[1]) + ) + + return sample_list From 8039ef8047a23ec0089110d9dc1a18c0d60dee16 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Mon, 16 Sep 2024 15:10:45 +0530 Subject: [PATCH 15/18] Add pillow library to pyproject.toml --- poetry.lock | 148 +++++++++++++++++++++++++++++-------------------- pyproject.toml | 1 + 2 files changed, 89 insertions(+), 60 deletions(-) diff --git a/poetry.lock b/poetry.lock index eb8658718..b22bb4574 100644 --- a/poetry.lock +++ b/poetry.lock @@ -3032,72 +3032,100 @@ files = [ [[package]] name = "pillow" -version = "10.0.0" +version = "10.4.0" description = "Python Imaging Library (Fork)" -optional = true +optional = false python-versions = ">=3.8" files = [ - {file = "Pillow-10.0.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:1f62406a884ae75fb2f818694469519fb685cc7eaff05d3451a9ebe55c646891"}, - {file = "Pillow-10.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = 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)"]},{"cell_type":"markdown","metadata":{"id":"_8dMBi8UNtg1"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/Visual_QA.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"_EzC6SKhjdk7"},"source":["**LangTest** is an open-source python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. Whether you are using **John Snow Labs, Hugging Face, Spacy** models or **OpenAI, Cohere, AI21, Hugging Face Inference API and Azure-OpenAI** based LLMs, it has got you covered. You can test any Named Entity Recognition (NER), Text Classification, fill-mask, Translation model using the library. We also support testing LLMS for Question-Answering, Visual question-answering, Summarization and text-generation tasks on benchmark datasets. The library supports 60+ out of the box tests. For a complete list of supported test categories, please refer to the [documentation](http://langtest.org/docs/pages/docs/test_categories).\n","\n","Metrics are calculated by comparing the model's extractions in the original list of sentences against the extractions carried out in the noisy list of sentences. The original annotated labels are not used at any point, we are simply comparing the model against itself in a 2 settings."]},{"cell_type":"markdown","metadata":{"id":"v9Yd7KhpZOTF"},"source":["# Getting started with LangTest"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kJ-dxTWu7bcA"},"outputs":[],"source":["!pip install langtest==2.4.0"]},{"cell_type":"markdown","metadata":{"id":"cXOI5kBFlO6w"},"source":["# Harness and its Parameters\n","\n","The Harness class is a testing class for Natural Language Processing (NLP) models. It evaluates the performance of a NLP model on a given task using test data and generates a report with test results.Harness can be imported from the LangTest library in the following way."]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":4291,"status":"ok","timestamp":1692340616139,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"w1g27-uxl1AA"},"outputs":[],"source":["#Import Harness from the LangTest library\n","from langtest import Harness"]},{"cell_type":"markdown","metadata":{"id":"PXBMpFHIl7n9"},"source":["It imports the Harness class from within the module, that is designed to provide a blueprint or framework for conducting NLP testing, and that instances of the Harness class can be customized or configured for different testing scenarios or environments.\n","\n","Here is a list of the different parameters that can be passed to the Harness function:\n","\n","
\n","\n","\n","\n","| Parameter | Description |\n","| - | - |\n","| **task** | Task for which the model is to be evaluated (Visual Question Answering) |\n","| **model** | Specifies the model(s) to be evaluated. This parameter can be provided as either a dictionary or a list of dictionaries. Each dictionary should contain the following keys:
  • model (mandatory): \tPipelineModel or path to a saved model or pretrained LLM pipeline/model from hub.
  • hub (mandatory): Hub (library) to use in back-end for loading model from public models hub or from path
|\n","| **data** | The data to be used for evaluation. A dictionary providing flexibility and options for data sources. It should include the following keys:
  • data_source (mandatory): The source of the data.
  • subset (optional): The subset of the data.
  • feature_column (optional): The column containing the features.
  • target_column (optional): The column containing the target labels.
  • split (optional): The data split to be used.
  • source (optional): Set to 'huggingface' when loading Hugging Face dataset.
|\n","| **config** | Configuration for the tests to be performed, specified in the form of a YAML file. |\n","\n","\n","
\n","
"]},{"cell_type":"markdown","metadata":{"id":"KLC_lBv09ZuN"},"source":["# Robustness Testing\n","\n","Model robustness can be described as the ability of a model to maintain similar levels of accuracy, precision, and recall when perturbations are made to the data it is predicting on. For example, In the case of images, the goal is to understand how modifications such as resizing, rotation, noise addition, or color adjustments affect the model's performance compared to the original images it was trained on.\n","\n","\n","**`Supported Robustness tests :`**
\n","\n","### Text\n","\n","| **Test Name** | **Short Description** |\n","|-------------------------------|----------------------------------------------------------------------------------------|\n","| **`uppercase`** | Capitalization of the text set is turned into uppercase |\n","| **`lowercase`** | Capitalization of the text set is turned into lowercase |\n","| **`titlecase`** | Capitalization of the text set is turned into title case |\n","| **`add_punctuation`** | Adds punctuation to the text set |\n","| **`strip_punctuation`** | Removes punctuation from the text set |\n","| **`add_typo`** | Introduces typographical errors into the text |\n","| **`swap_entities`** | Swaps named entities in the text |\n","| **`american_to_british`** | Converts American English spellings to British English |\n","| **`british_to_american`** | Converts British English spellings to American English |\n","| **`add_context`** | Adds additional context to the text set |\n","| **`add_contraction`** | Introduces contractions (e.g., do not → don't) |\n","| **`dyslexia_word_swap`** | Swaps words in a way that mimics dyslexic reading errors |\n","| **`number_to_word`** | Converts numbers to words in the text set (e.g., 1 → one) |\n","| **`add_ocr_typo`** | Adds optical character recognition (OCR) specific typos to the text |\n","| **`add_abbreviation`** | Replaces certain words with their abbreviations |\n","| **`add_speech_to_text_typo`** | Adds speech-to-text transcription errors |\n","| **`add_slangs`** | Introduces slang terms into the text |\n","| **`multiple_perturbations`** | Applies multiple perturbations to the text at once |\n","| **`adjective_synonym_swap`** | Swaps adjectives in the text with their synonyms |\n","| **`adjective_antonym_swap`** | Swaps adjectives in the text with their antonyms |\n","| **`strip_all_punctuation`** | Removes all punctuation from the text |\n","| **`randomize_age`** | Randomizes the age mentioned in the text |\n","| **`add_new_lines`** | Inserts new lines into the text set |\n","| **`add_tabs`** | Inserts tab characters into the text set |\n","\n","### Images\n","\n","| **Test Name** | **Short Description** |\n","|----------------------|--------------------------------------------------------|\n","| **`image_resize`** | Resizes the image to a different dimension |\n","| **`image_rotate`** | Rotates the image by a specified angle |\n","| **`image_blur`** | Applies a blur filter to the image |\n","| **`image_noise`** | Adds random noise to the image |\n","| **`image_contrast`** | Adjusts the contrast of the image |\n","| **`image_brightness`**| Adjusts the brightness of the image |\n","| **`image_sharpness`** | Adjusts the sharpness of the image |\n","| **`image_color`** | Adjusts the color balance of the image |\n","| **`image_flip`** | Flips the image either horizontally or vertically |\n","| **`image_crop`** | Crops a portion of the image |\n","\n","
"]},{"cell_type":"markdown","metadata":{"id":"cVIzXdGMjX47"},"source":["## Testing robustness of a pretrained LLM models\n","\n","Testing a LLM model's robustness gives us an idea on how our data may need to be modified to make the model more robust. We can use a pretrained model/pipeline or define our own custom pipeline or load a saved pre trained model to test.\n","\n","Here we are directly passing a pretrained model/pipeline from hub as the model parameter in harness and running the tests."]},{"cell_type":"markdown","metadata":{"id":"78THAZm3cRu7"},"source":["### Test Configuration\n","\n","Test configuration can be passed in the form of a YAML file as shown below or using .configure() method\n","\n","\n","**Config YAML format** :\n","```\n","tests: \n"," {\n"," \"defaults\": {\n"," \"min_pass_rate\": 0.5,\n"," },\n"," \"robustness\": {\n"," \"image_noise\": {\n"," \"min_pass_rate\": 0.5,\n"," \"parameters\": {\n"," \"noise_level\": 0.5\n"," }\n","\n"," },\n"," \"image_rotate\": {\n"," \"min_pass_rate\": 0.5,\n"," \"parameters\": {\n"," \"angle\": 45\n"," }\n"," },\n"," \"image_blur\": {\n"," \"min_pass_rate\": 0.5,\n"," \"parameters\": {\n"," \"radius\": 5\n"," }\n"," },\n"," \"image_resize\": {\n"," \"min_pass_rate\": 0.5,\n"," \"parameters\": {\n"," \"resize\": 0.5 # 0.01 to 1.0 means 1% to 100% of the original size\n"," }\n"," },\n"," }\n"," }\n"," \n","```\n","\n","If config file is not present, we can also use the **.configure()** method to manually configure the harness to perform the needed tests.\n"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[],"source":["import os \n","os.environ['OPENAI_API_KEY'] = \"sk-XXXXXXXX\""]},{"cell_type":"markdown","metadata":{},"source":["## Visual Question Answering (VQA)\n","\n","This notebook demonstrates how to perform a Visual Question Answering (VQA) using the `PIL` library to load images and a harness for running the task. The model being used is `gpt-4o-mini` from the OpenAI hub, and the data comes from the MMMU dataset, specifically the `Clinical_Medicine` subset."]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c274bf01644a432fb0e254fd1e8ebb75","version_major":2,"version_minor":0},"text/plain":["Resolving data files: 0%| | 0/60 [00:00\n"," \n"," \n"," \n"," category\n"," test_type\n"," original_image\n"," perturbed_image\n"," question\n"," options\n"," \n"," \n"," \n"," \n"," 3\n"," robustness\n"," image_noise\n"," \n"," \n"," What person's name is associated with the fracture shown below? \n"," A. Monteggia\\nB. Bennett\\nC. Jones\\nD. Smith\n"," \n"," \n"," 15\n"," robustness\n"," image_resize\n"," \n"," \n"," Identify the following rhythm: \n"," A. Sinus Rhythm with PAC's\\nB. Junctional Rhythm\\nC. 2nd Degree AV Block, Type I\\nD. 3rd Degree AV Block\\nE. Normal Sinus Rhythm with PVC's\\nF. Idioventricular Rhythm\n"," \n"," \n"," 6\n"," robustness\n"," image_rotate\n"," \n"," \n"," A 56-year-old woman is undergoing chemotherapy for treatment of breast carcinoma. The gross appearance of her skin shown here is most typical for which of the following conditions? \n"," A. Thrombocytopenia\\nB. Gangrene\\nC. Congestive heart failure\\nD. Metastatic breast carcinoma\n"," \n"," \n"," 18\n"," robustness\n"," image_resize\n"," \n"," \n"," What person's name is associated with the fracture shown below? \n"," A. Monteggia\\nB. Bennett\\nC. Jones\\nD. Smith\n"," \n"," \n"," 17\n"," robustness\n"," image_resize\n"," \n"," \n"," Based on , what's the most likely diagnosis?\n"," A. first degree atrioventricular block\\nB. third degree atrioventricular block\\nC. Second degree type II atrioventricular block\\nD. atrial flutter\n"," \n"," \n",""],"text/plain":[""]},"metadata":{},"output_type":"display_data"}],"source":["from IPython.display import display, HTML\n","\n","\n","df = harness.testcases()\n","html=df.sample(5).to_html(escape=False)\n","\n","display(HTML(html))"]},{"cell_type":"markdown","metadata":{"id":"fRyNPRBokXNZ"},"source":["### Running the tests."]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":68268,"status":"ok","timestamp":1692340564519,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"3kUPTsNvjkgr","outputId":"4c4815e4-4cab-4dbf-99ba-1a231656f1e3"},"outputs":[{"name":"stderr","output_type":"stream","text":["Running testcases... : 100%|██████████| 20/20 [00:44<00:00, 2.21s/it]\n"]},{"data":{"text/plain":[]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["harness.run()"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"data":{"text/html":["\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
categorytest_typeoriginal_imageperturbed_imagequestionoptionsexpected_resultactual_resultpass
5robustnessimage_rotateIdentify the following rhythm: A. Sinus Rhythm with PAC's\\nB. Junctional Rhythm\\nC. 2nd Degree AV Block, Type I\\nD. 3rd Degree AV Block\\nE. Normal Sinus Rhythm with PVC's\\nF. Idioventricular RhythmAnswer: UnRecognizable.Answer: UnRecognizable.True
4robustnessimage_noiseThe best diagnosis for the appendix is: A. simple appendicitis\\nB. appendix abscess\\nC. normal appendix\\nD. cellulite appendicitisAnswer: UnRecognizable.I'm unable to recognize the content of the image. Thus, I cannot determine the correct diagnosis for the appendix. \\n\\nAnswer: UnRecognizable.False
7robustnessimage_rotateBased on , what's the most likely diagnosis?A. first degree atrioventricular block\\nB. third degree atrioventricular block\\nC. Second degree type II atrioventricular block\\nD. atrial flutterAnswer: UnRecognizable.Answer: UnRecognizable.True
9robustnessimage_rotateThe best diagnosis for the appendix is: A. simple appendicitis\\nB. appendix abscess\\nC. normal appendix\\nD. cellulite appendicitisAnswer: UnRecognizable.Answer: A. simple appendicitis.False
0robustnessimage_noiseIdentify the following rhythm: A. Sinus Rhythm with PAC's\\nB. Junctional Rhythm\\nC. 2nd Degree AV Block, Type I\\nD. 3rd Degree AV Block\\nE. Normal Sinus Rhythm with PVC's\\nF. Idioventricular RhythmAnswer: UnRecognizable.Answer: UnRecognizable.True
"],"text/plain":[""]},"metadata":{},"output_type":"display_data"}],"source":["from IPython.display import display, HTML\n","\n","\n","df = harness.generated_results()\n","html=df.sample(5).to_html(escape=False)\n","\n","display(HTML(html))"]},{"cell_type":"markdown","metadata":{},"source":["Called after harness.generate() and is to used to run all the tests. Returns a pass/fail flag for each test."]},{"cell_type":"markdown","metadata":{"id":"106TE41ffw43"},"source":["This method returns the generated results in the form of a pandas dataframe, which provides a convenient and easy-to-use format for working with the test results. You can use this method to quickly identify the test cases that failed and to determine where fixes are needed."]},{"cell_type":"markdown","metadata":{"id":"_0gnozMlkoF0"},"source":["### Report of the tests"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"executionInfo":{"elapsed":22,"status":"ok","timestamp":1692340564522,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"YKFvMs0RGHO7","outputId":"3a0ed33b-aa59-4e98-86d0-8d407391b0e4"},"outputs":[{"data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
categorytest_typefail_countpass_countpass_rateminimum_pass_ratepass
0robustnessimage_noise3240%50%False
1robustnessimage_rotate2360%50%True
2robustnessimage_blur2360%50%True
3robustnessimage_resize2360%50%True
\n","
"],"text/plain":[" category test_type fail_count pass_count pass_rate \\\n","0 robustness image_noise 3 2 40% \n","1 robustness image_rotate 2 3 60% \n","2 robustness image_blur 2 3 60% \n","3 robustness image_resize 2 3 60% \n","\n"," minimum_pass_rate pass \n","0 50% False \n","1 50% True \n","2 50% True \n","3 50% True "]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["harness.report()"]},{"cell_type":"markdown","metadata":{"id":"bSP2QL6agTH_"},"source":["Called after harness.run() and it summarizes the results giving information about pass and fail counts and overall test pass/fail flag."]}],"metadata":{"accelerator":"GPU","colab":{"machine_shape":"hm","provenance":[],"toc_visible":true},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.10"}},"nbformat":4,"nbformat_minor":0} From b337d2b6ce5c8357df881fc24c0b2bc5a4f6c6d0 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Tue, 17 Sep 2024 18:18:55 +0530 Subject: [PATCH 18/18] Update pillow version to 10.0.0 and make it a required dependency --- poetry.lock | 4 ++-- pyproject.toml | 1 + 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/poetry.lock b/poetry.lock index 146f0a60b..b3655893c 100644 --- a/poetry.lock +++ b/poetry.lock @@ -3344,7 +3344,7 @@ files = [ name = "pillow" version = "10.0.0" description = "Python Imaging Library (Fork)" -optional = true +optional = false python-versions = ">=3.8" files = [ {file = "Pillow-10.0.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:1f62406a884ae75fb2f818694469519fb685cc7eaff05d3451a9ebe55c646891"}, @@ -5753,4 +5753,4 @@ transformers = ["accelerate", "datasets", "torch", "transformers"] [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "f43231a0fd18c0d2b740ccad37045fd68294240109c0744b13973dc3ec2f445d" \ No newline at end of file +content-hash = "7c8dc3eabf8a4d28f97b9be0f2a9fb70261baef10e3d2ef996fe56a906c36a45" diff --git a/pyproject.toml b/pyproject.toml index 724645aab..074ef4b92 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -82,6 +82,7 @@ importlib-resources = "^6.4.0" click = "^8.1.7" openpyxl = "^3.1.5" tables = "3.8.0" +pillow = "10.0.0" [tool.poetry.extras] transformers = ["transformers", "torch", "accelerate", "datasets"]