ref-metrics.py

# 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 dummy_values()
        # return calculate(predictions, references, max_iou)

    def dummy_values():
        return {
            "model_1": {
                "overall": {
                    "all": {
                        "tp": 50,
                        "fp": 20,
                        "fn": 10,
                        "precision": 0.71,
                        "recall": 0.83,
                        "f1": 0.76
                    },
                    "small": {
                        "tp": 15,
                        "fp": 5,
                        "fn": 2,
                        "precision": 0.75,
                        "recall": 0.88,
                        "f1": 0.81
                    },
                    "medium": {
                        "tp": 25,
                        "fp": 10,
                        "fn": 5,
                        "precision": 0.71,
                        "recall": 0.83,
                        "f1": 0.76
                    },
                    "large": {
                        "tp": 10,
                        "fp": 5,
                        "fn": 3,
                        "precision": 0.67,
                        "recall": 0.77,
                        "f1": 0.71
                    }
                },
                "per_sequence": {
                    "sequence_1": {
                        "all": {
                            "tp": 30,
                            "fp": 15,
                            "fn": 7,
                            "precision": 0.67,
                            "recall": 0.81,
                            "f1": 0.73
                        },
                        "small": {
                            "tp": 10,
                            "fp": 3,
                            "fn": 1,
                            "precision": 0.77,
                            "recall": 0.91,
                            "f1": 0.83
                        },
                        "medium": {
                            "tp": 15,
                            "fp": 7,
                            "fn": 2,
                            "precision": 0.68,
                            "recall": 0.88,
                            "f1": 0.77
                        },
                        "large": {
                            "tp": 5,
                            "fp": 2,
                            "fn": 1,
                            "precision": 0.71,
                            "recall": 0.83,
                            "f1": 0.76
                        }
                    }
                }
            },
            "model_2": {
                "overall": {
                    "all": {
                        "tp": 60,
                        "fp": 25,
                        "fn": 15,
                        "precision": 0.71,
                        "recall": 0.80,
                        "f1": 0.75
                    },
                    "small": {
                        "tp": 20,
                        "fp": 6,
                        "fn": 3,
                        "precision": 0.77,
                        "recall": 0.87,
                        "f1": 0.82
                    },
                    "medium": {
                        "tp": 30,
                        "fp": 12,
                        "fn": 5,
                        "precision": 0.71,
                        "recall": 0.86,
                        "f1": 0.78
                    },
                    "large": {
                        "tp": 10,
                        "fp": 7,
                        "fn": 5,
                        "precision": 0.59,
                        "recall": 0.67,
                        "f1": 0.63
                    }
                },
                "per_sequence": {
                    "sequence_1": {
                        "all": {
                            "tp": 40,
                            "fp": 18,
                            "fn": 8,
                            "precision": 0.69,
                            "recall": 0.83,
                            "f1": 0.75
                        },
                        "small": {
                            "tp": 12,
                            "fp": 4,
                            "fn": 2,
                            "precision": 0.75,
                            "recall": 0.86,
                            "f1": 0.80
                        },
                        "medium": {
                            "tp": 20,
                            "fp": 8,
                            "fn": 3,
                            "precision": 0.71,
                            "recall": 0.87,
                            "f1": 0.78
                        },
                        "large": {
                            "tp": 8,
                            "fp": 6,
                            "fn": 3,
                            "precision": 0.57,
                            "recall": 0.73,
                            "f1": 0.64
                        }
                    }
                }
            }
        }