# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # 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 datetime import os import datasets import evaluate from seametrics.user_friendly.utils import calculate_from_payload import wandb _CITATION = """\ @InProceedings{huggingface:module, title = {A great new module}, authors={huggingface, Inc.}, year={2020} }\ @article{milan2016mot16, title={MOT16: A benchmark for multi-object tracking}, author={Milan, Anton and Leal-Taix{\'e}, Laura and Reid, Ian and Roth, Stefan and Schindler, Konrad}, journal={arXiv preprint arXiv:1603.00831}, year={2016} } """ _DESCRIPTION = """\ The MOT Metrics module is designed to evaluate multi-object tracking (MOT) algorithms by computing various metrics based on predicted and ground truth bounding boxes. It serves as a crucial tool in assessing the performance of MOT systems, aiding in the iterative improvement of tracking algorithms.""" _KWARGS_DESCRIPTION = """ Calculates how good are predictions given some references, using certain scores Args: predictions: list of predictions to score. Each predictions should be a string with tokens separated by spaces. references: list of reference for each prediction. Each reference should be a string with tokens separated by spaces. max_iou (`float`, *optional*): If specified, this is the minimum Intersection over Union (IoU) threshold to consider a detection as a true positive. Default is 0.5. """ @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION) class UserFriendlyMetrics(evaluate.Metric): """TODO: Short description of my evaluation module.""" def _info(self): # TODO: Specifies the evaluate.EvaluationModuleInfo object return evaluate.MetricInfo( # This is the description that will appear on the modules page. module_type="metric", description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, # This defines the format of each prediction and reference features=datasets.Features( { "predictions": datasets.Sequence( datasets.Sequence(datasets.Value("float")) ), "references": datasets.Sequence( datasets.Sequence(datasets.Value("float")) ), } ), # Additional links to the codebase or references codebase_urls=["http://github.com/path/to/codebase/of/new_module"], reference_urls=["http://path.to.reference.url/new_module"], ) def _download_and_prepare(self, dl_manager): """Optional: download external resources useful to compute the scores""" # TODO: Download external resources if needed pass def _compute( self, payload, max_iou: float = 0.5, filters={}, recognition_thresholds=[0.3, 0.5, 0.8], debug: bool = False, ): """Returns the scores""" # TODO: Compute the different scores of the module return calculate_from_payload( payload, max_iou, filters, recognition_thresholds, debug ) # return calculate(predictions, references, max_iou) def wandb( self, results, wandb_section: str = None, wandb_project="user_friendly_metrics", log_plots: bool = True, debug: bool = False, ): """ Logs metrics to Weights and Biases (wandb) for tracking and visualization, including categorized bar charts for global metrics. Args: results (dict): Results dictionary with 'global' and 'per_sequence' keys. wandb_section (str, optional): W&B section for metric grouping. Defaults to None. wandb_project (str, optional): The name of the wandb project. Defaults to 'user_friendly_metrics'. log_plots (bool, optional): Generates categorized bar charts for global metrics. Defaults to True. debug (bool, optional): Logs detailed summaries and histories to the terminal console. Defaults to False. """ current_datetime = datetime.datetime.now() formatted_datetime = current_datetime.strftime("%Y-%m-%d_%H-%M-%S") wandb.login(key=os.getenv("WANDB_API_KEY")) run = wandb.init( project=wandb_project, name=f"evaluation-{formatted_datetime}", reinit=True, settings=wandb.Settings(silent=not debug), ) categories = { "user_friendly_metrics": { "mostly_tracked_score_0.3", "mostly_tracked_score_0.5", "mostly_tracked_score_0.8", }, "evaluation_metrics_dev": { "f1", "recall", "precision", }, "user_friendly_metrics_dev": { "mostly_tracked_count_0.3", "mostly_tracked_count_0.5", "mostly_tracked_count_0.8", "unique_obj_count", }, "predictions_summary": { "fp", "tp", "fn", }, } chart_data = {key: [] for key in categories.keys()} # Log global metrics if "global" in results: for global_key, global_metrics in results["global"].items(): for metric, value in global_metrics["all"].items(): log_key = ( f"{wandb_section}/global/{global_key}/{metric}" if wandb_section else f"global/{global_key}/{metric}" ) run.log({log_key: value}) if debug: print(f" {log_key} = {value}") for category, metrics in categories.items(): if metric in metrics: chart_data[category].append([metric, value]) print("----------------------------------------------------") if log_plots: for category, data in chart_data.items(): if data: table_data = [[label, value] for label, value in data] table = wandb.Table(data=table_data, columns=["metrics", "value"]) run.log( { f"{category}_bar_chart": wandb.plot.bar( table, "metrics", "value", title=f"{category.replace('_', ' ').title()}", ) } ) if "per_sequence" in results: sorted_sequences = sorted( results["per_sequence"].items(), key=lambda x: next(iter(x[1].values()), {}).get("all", {}).get("f1", 0), reverse=True, # Set to True for descending order ) for sequence_name, sequence_data in sorted_sequences: for seq_key, seq_metrics in sequence_data.items(): for metric, value in seq_metrics["all"].items(): log_key = ( f"{wandb_section}/per_sequence/{sequence_name}/{seq_key}/{metric}" if wandb_section else f"per_sequence/{sequence_name}/{seq_key}/{metric}" ) run.log({log_key: value}) if debug: print(f" {log_key} = {value}") print("----------------------------------------------------") if debug: print("\nDebug Mode: Logging Summary and History") print(f"Results Summary:\n{results}") print(f"WandB Settings:\n{run.settings}") print("All metrics have been logged.") run.finish()