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VidEB/DVSC23/LICENSE

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+MIT License
+
+Copyright (c) Meta Platforms, Inc. and affiliates.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

VidEB/DVSC23/README.md

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+# DVSC23 evaluation
+
+This is the codebase for the [2023 Video Similarity Challenge](https://sites.google.com/view/vcdw2023/video-similarity-challenge) and
+the associated dataset.
+
+The Video Similarity Challenge will be featured at the
+[VCDW Workshop at CVPR 2023](https://sites.google.com/view/vcdw2023/video-similarity-challenge)!
+
+The design and results of the challenge can be found in the
+[paper](https://drive.google.com/file/d/1MujZDupmVVJC1h9GTU1LS4az-KoqbpZm/view).
+
+## Running evaluations
+
+### Descriptor eval
+
+```
+$ ./descriptor_eval.py --query_features vsc_eval_data/queries.npz --ref_features vsc_eval_data/refs.npz --ground_truth vsc_eval_data/gt.csv
+Starting Descriptor level eval
+...
+2022-11-09 17:00:33 INFO     Descriptor track micro-AP (uAP): 0.4754
+```
+
+
+## License
+
+The VSC codebase is released under the [MIT license](LICENSE).

VidEB/DVSC23/descriptor_eval.py

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+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Descriptor track evaluation script.
+"""
+import logging
+from argparse import ArgumentParser, Namespace
+
+from vsc.descriptor_eval_lib import evaluate_descriptor_track
+from vsc.metrics import CandidatePair
+
+parser = ArgumentParser()
+parser.add_argument(
+    "--query_features",
+    help="Path containing query features",
+    type=str,
+    required=True,
+)
+parser.add_argument(
+    "--ref_features",
+    help="Path containing reference features",
+    type=str,
+    required=True,
+)
+parser.add_argument(
+    "--candidates_output",
+    help="Path to write candidates (optional)",
+    type=str,
+)
+parser.add_argument("--ground_truth", help="Path containing Groundtruth", type=str)
+
+
+logging.basicConfig(
+    format="%(asctime)s %(levelname)-8s %(message)s",
+    level=logging.INFO,
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
+logger = logging.getLogger("descriptor_eval_lib.py")
+logger.setLevel(logging.INFO)
+
+
+def main(args: Namespace):
+    ap, candidates = evaluate_descriptor_track(
+        args.query_features, args.ref_features, args.ground_truth
+    )
+
+    if args.candidates_output:
+        logger.info(f"Storing candidates to {args.candidates_output}")
+        CandidatePair.write_csv(candidates, args.candidates_output)
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    main(args)

VidEB/DVSC23/vsc/__init__.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+

VidEB/DVSC23/vsc/candidates.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from abc import ABC, abstractmethod
+from typing import List
+
+import numpy as np
+from vsc.index import PairMatches, VideoFeature, VideoIndex
+from vsc.metrics import CandidatePair
+
+
+class ScoreAggregation(ABC):
+    @abstractmethod
+    def aggregate(self, match: PairMatches) -> float:
+        pass
+
+    def score(self, match: PairMatches) -> CandidatePair:
+        score = self.aggregate(match)
+        return CandidatePair(query_id=match.query_id, ref_id=match.ref_id, score=score)
+
+
+class MaxScoreAggregation(ScoreAggregation):
+    def aggregate(self, match: PairMatches) -> float:
+        return np.max([m.score for m in match.matches])
+
+
+class CandidateGeneration:
+    def __init__(self, references: List[VideoFeature], aggregation: ScoreAggregation):
+        self.aggregation = aggregation
+        dim = references[0].dimensions()
+        self.index = VideoIndex(dim)
+        self.index.add(references)
+
+    def query(self, queries: List[VideoFeature], global_k: int) -> List[CandidatePair]:
+        matches = self.index.search(queries, global_k=global_k)
+        candidates = [self.aggregation.score(match) for match in matches]
+        candidates = sorted(candidates, key=lambda match: match.score, reverse=True)
+        return candidates

VidEB/DVSC23/vsc/descriptor_eval_lib.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+from typing import List, Optional, Tuple
+
+from vsc.candidates import CandidateGeneration, MaxScoreAggregation
+from vsc.metrics import (
+    AveragePrecision,
+    CandidatePair,
+    Dataset,
+    Match,
+    average_precision,
+)
+from vsc.storage import load_features
+
+logger = logging.getLogger("descriptor_eval_lib.py")
+logger.setLevel(logging.INFO)
+
+
+RETRIEVAL_CANDIDATES_PER_QUERY = 20 * 60  # similar to K=20 for ~60 second videos
+AGGREGATED_CANDIDATES_PER_QUERY = 25
+
+
+def evaluate_descriptor_track(
+    query_feature_filename: str,
+    ref_feature_filename: str,
+    ground_truth_filename: Optional[str],
+) -> Tuple[AveragePrecision, List[CandidatePair]]:
+    logger.info("Starting Descriptor level eval")
+    query_features = load_features(query_feature_filename, Dataset.QUERIES)
+    logger.info(f"Loaded {len(query_features)} query features")
+    ref_features = load_features(ref_feature_filename, Dataset.REFS)
+    logger.info(f"Loaded {len(ref_features)} ref features")
+
+    retrieval_candidates = int(RETRIEVAL_CANDIDATES_PER_QUERY * len(query_features))
+    num_candidates = int(AGGREGATED_CANDIDATES_PER_QUERY * len(query_features))
+
+    logger.info(f"Performing search for {retrieval_candidates} nearest vectors")
+    cg = CandidateGeneration(ref_features, MaxScoreAggregation())
+    candidates = cg.query(query_features, global_k=retrieval_candidates)
+    logger.info(f"Got {len(candidates)} unique video pairs.")
+    if len(candidates) > num_candidates:
+        logger.info(f"Limiting to {num_candidates} highest score pairs.")
+        score_candidates = candidates[:num_candidates]
+    else:
+        score_candidates = candidates
+
+    if ground_truth_filename is None:
+        return None, score_candidates
+
+    gt_matches = Match.read_csv(ground_truth_filename, is_gt=True)
+    gt_pairs = CandidatePair.from_matches(gt_matches)
+    logger.info(f"Loaded ground truth from {ground_truth_filename}")
+    ap = average_precision(gt_pairs, score_candidates)
+    logger.info(f"Descriptor track micro-AP (uAP): {ap.ap:.4f}")
+
+    return ap, score_candidates

VidEB/DVSC23/vsc/index.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import collections
+import logging
+from dataclasses import dataclass
+from typing import Iterable, List, NamedTuple, Tuple
+
+import faiss  # @manual
+import numpy as np
+from faiss.contrib import exhaustive_search  # @manual
+
+SearchIndices = Tuple[int, int, float]
+
+
+@dataclass
+class VideoMetadata:
+    video_id: str
+    timestamps: np.ndarray  # either Nx2 (start and end timestamps) or N
+
+    def __len__(self):
+        return self.timestamps.shape[0]
+
+    def get_timestamps(self, idx: int) -> Tuple[float, float]:
+        t = self.timestamps[idx]
+        if len(self.timestamps.shape) == 1:
+            return (t, t)
+        return (t[0], t[1])
+
+
+@dataclass
+class VideoFeature(VideoMetadata):
+    feature: np.ndarray
+
+    def __post_init__(self):
+        assert self.feature.shape[0] == len(
+            self.timestamps
+        ), "Mismatched timestamps / feature size"
+
+    def metadata(self):
+        return VideoMetadata(video_id=self.video_id, timestamps=self.timestamps)
+
+    def dimensions(self):
+        return self.feature.shape[1]
+
+
+class PairMatch(NamedTuple):
+    query_timestamps: Tuple[float, float]
+    ref_timestamps: Tuple[float, float]
+    score: float
+
+
+@dataclass
+class PairMatches:
+    query_id: str
+    ref_id: str
+    matches: List[PairMatch]
+
+    def records(self):
+        for match in self.matches:
+            yield {
+                "query_id": self.query_id,
+                "ref_id": self.ref_id,
+                "query_start": match.query_timestamps[0],
+                "query_end": match.query_timestamps[1],
+                "ref_start": match.ref_timestamps[0],
+                "ref_end": match.ref_timestamps[1],
+                "score": match.score,
+            }
+
+
+class VideoIndex:
+    def __init__(
+        self,
+        dim: int,
+        codec_str: str = "Flat",
+        metric: int = faiss.METRIC_INNER_PRODUCT,
+    ):
+        self.dim = dim
+        self.index = faiss.index_factory(self.dim, codec_str, metric)
+        self.video_clip_idx = []
+        self.video_clip_to_video_ids = []
+        self.video_metadata = {}
+
+    def add(self, db: List[VideoFeature]):
+        for vf in db:
+            self.video_clip_idx.extend(list(range(vf.feature.shape[0])))
+            self.video_clip_to_video_ids.extend(
+                [vf.video_id for _ in range(vf.feature.shape[0])]
+            )
+            self.video_metadata[vf.video_id] = vf.metadata()
+            self.index.add(vf.feature)
+
+    def search(
+        self,
+        queries: List[VideoFeature],
+        global_k: int,
+    ) -> List[PairMatches]:
+        query_ids = []
+        query_indices = []
+        for q in queries:
+            query_ids.extend([q.video_id] * len(q))
+            query_indices.extend(range(len(q)))
+        query_metadatas = {q.video_id: q.metadata() for q in queries}
+        query_features = np.concatenate([q.feature for q in queries])
+        if global_k < 0:
+            # Negative values cause us to use vanilla KNN search
+            k = -global_k
+            logging.warn(
+                "Using local k for KNN search. Warning: this is against the "
+                "VSC rules, since predictions for a query-ref pair are not "
+                "independent of other references. KNN search is provided for "
+                "comparison."
+            )
+            search_indices = self._knn_search(query_features, k)
+        else:
+            search_indices = self._global_threshold_knn_search(query_features, global_k)
+
+        pair_nns = collections.defaultdict(list)
+
+        for i, j, score in search_indices:
+            query_id = query_ids[i]
+            query_idx = query_indices[i]
+            query_metadata = query_metadatas[query_id]
+            ref_id = self.video_clip_to_video_ids[j]
+            ref_idx = self.video_clip_idx[j]
+            ref_metadata = self.video_metadata[ref_id]
+            match = PairMatch(
+                query_timestamps=query_metadata.get_timestamps(query_idx),
+                ref_timestamps=ref_metadata.get_timestamps(ref_idx),
+                score=score,
+            )
+            pair_nns[query_id, ref_id].append(match)
+
+        return [
+            PairMatches(query_id, ref_id, matches)
+            for ((query_id, ref_id), matches) in pair_nns.items()
+        ]
+
+    def _global_threshold_knn_search(
+        self, query_features: np.ndarray, global_k: int
+    ) -> Iterable[SearchIndices]:
+        use_similarity = self.index.metric_type == faiss.METRIC_INNER_PRODUCT
+        initial_radius = -1e10 if use_similarity else 1e10
+        _, limits, similarity, indices = exhaustive_search.range_search_max_results(
+            self.index,
+            exhaustive_search.exponential_query_iterator(query_features),
+            initial_radius,
+            max_results=2 * global_k,
+            min_results=global_k,
+            ngpu=-1,  # use GPU if available
+        )
+        nq = query_features.shape[0]
+        search_indices = []
+
+        for i in range(nq):
+            for j in range(limits[i], limits[i + 1]):
+                search_indices.append((i, indices[j], similarity[j]))
+
+        search_indices.sort(key=lambda x: x[2], reverse=use_similarity)
+        if len(search_indices) > global_k:
+            search_indices = search_indices[:global_k]
+        return search_indices
+
+    def _knn_search(self, query_features: np.ndarray, k) -> Iterable[SearchIndices]:
+        index = self.index
+        if faiss.get_num_gpus() > 0:
+            logging.info("Moving index to GPU")
+            index = faiss.index_cpu_to_all_gpus(self.index)
+
+        logging.info("Performing KNN search")
+        similarity, ids = index.search(query_features, k)
+        for i in range(ids.shape[0]):
+            for j in range(ids.shape[1]):
+                yield (i, ids[i, j], similarity[i, j])

VidEB/DVSC23/vsc/metrics.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import collections
+import dataclasses
+import enum
+import itertools
+from collections import defaultdict
+from math import sqrt
+from typing import Collection, Dict, List, NamedTuple, Optional, TextIO, Tuple, Union
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+from sklearn.metrics import average_precision_score
+
+
+class Dataset(enum.Enum):
+    QUERIES = "Q"
+    REFS = "R"
+
+
+def format_video_id(video_id: Union[str, int], dataset: Optional[Dataset]) -> str:
+    if isinstance(video_id, (int, np.integer)):
+        if dataset is None:
+            raise ValueError(
+                "Unable to convert integer video_id without a Dataset enum"
+            )
+        return f"{dataset.value}{video_id:06d}"
+    assert isinstance(
+        video_id, str
+    ), f"unexpected video_id: {video_id} of type {type(video_id)}"
+    if dataset is not None:
+        assert (
+            video_id[0] == dataset.value
+        ), f"dataset mismatch? got {video_id} for dataset {dataset}"
+    return video_id
+
+
+@dataclasses.dataclass
+class CandidatePair:
+    query_id: str
+    ref_id: str
+    score: float
+
+    @classmethod
+    def to_dataframe(
+        cls,
+        candidates: Collection["CandidatePair"],
+    ) -> pd.DataFrame:
+        return pd.DataFrame(
+            [
+                {
+                    "query_id": format_video_id(c.query_id, Dataset.QUERIES),
+                    "ref_id": format_video_id(c.ref_id, Dataset.REFS),
+                    "score": c.score,
+                }
+                for c in candidates
+            ],
+        )
+
+    @classmethod
+    def write_csv(
+        cls, candidates: Collection["CandidatePair"], file: Union[str, TextIO]
+    ):
+        df = cls.to_dataframe(candidates)
+        df.to_csv(file, index=False)
+
+    @classmethod
+    def read_csv(cls, file: Union[str, TextIO]) -> List["CandidatePair"]:
+        df = pd.read_csv(file)
+        pairs = []
+        for _, row in df.iterrows():
+            query_id = format_video_id(row.query_id, Dataset.QUERIES)
+            ref_id = format_video_id(row.ref_id, Dataset.REFS)
+            pairs.append(
+                CandidatePair(query_id=query_id, ref_id=ref_id, score=row.score)
+            )
+        return pairs
+
+    @classmethod
+    def from_matches(cls, matches: Collection["Match"]) -> List["CandidatePair"]:
+        scores = collections.defaultdict(float)
+        for match in matches:
+            key = (match.query_id, match.ref_id)
+            scores[key] = max(match.score, scores[key])
+        return [
+            CandidatePair(query_id=query_id, ref_id=ref_id, score=score)
+            for ((query_id, ref_id), score) in scores.items()
+        ]
+
+
+@dataclasses.dataclass
+class PrecisionRecallCurve:
+    precisions: np.ndarray
+    recalls: np.ndarray
+    scores: np.ndarray
+
+    def plot(self, ax=None, **kwargs):
+        if ax is None:
+            _, ax = plt.subplots()
+            ax.set_xlabel("recall")
+            ax.set_ylabel("precision")
+            ax.set_xlim(0, 1.05)
+            ax.set_ylim(0, 1.05)
+        ax.plot(self.recalls, self.precisions, **kwargs)
+        return ax
+
+
+@dataclasses.dataclass
+class AveragePrecision:
+    ap: float
+    pr_curve: PrecisionRecallCurve
+    simple_ap: Optional[float] = None
+
+
+class Intervals:
+
+    # Non-overlapping, ordered by interval start.
+    intervals: List[Tuple[float, float]]
+
+    def __init__(self, intervals: Optional[List[Tuple[float, float]]] = None):
+        self.intervals = intervals or []
+        self._dedup()
+
+    def add(self, interval: Tuple[float, float]):
+        """Add an interval."""
+        self.intervals.append(interval)
+        self._dedup()
+
+    def union(self, intervals: "Intervals") -> "Intervals":
+        return Intervals(self.intervals + intervals.intervals)
+
+    def total_length(self) -> float:
+        length = 0.0
+        for start, end in self.intervals:
+            length += end - start
+        return length
+
+    def intersect_length(self, intervals: "Intervals") -> float:
+        """Compute the total_length of the intersection of two Intervals.
+
+        This works by taking the sum of their lengths, and subtracting
+        the length of their union.
+
+        |A n B| = |A| + |B| - |A U B|
+        """
+        union = self.union(intervals)
+        return self.total_length() + intervals.total_length() - union.total_length()
+
+    def _dedup(self):
+        if len(self.intervals) <= 1:
+            return
+        deduped = []
+        intervals = sorted(self.intervals)
+        current_start, current_end = intervals[0]
+        for start, end in intervals[1:]:
+            if start <= current_end:
+                # Overlap case
+                current_end = max(end, current_end)
+            else:
+                # Non-overlap case
+                deduped.append((current_start, current_end))
+                current_start, current_end = start, end
+        deduped.append((current_start, current_end))
+        self.intervals = deduped
+
+    def __str__(self):
+        return str(self.intervals)
+
+    __repr__ = __str__
+
+
+class Axis(enum.Enum):
+    QUERY = enum.auto()
+    REF = enum.auto()
+
+
+class Match(NamedTuple):
+    """A ground-truth match or predicted match."""
+
+    query_id: str
+    ref_id: str
+    score: float
+    query_start: float
+    query_end: float
+    ref_start: float
+    ref_end: float
+
+    def pair_id(self):
+        return (self.query_id, self.ref_id)
+
+    def interval(self, axis: Axis) -> Tuple[float, float]:
+        if axis == Axis.QUERY:
+            return (self.query_start, self.query_end)
+        else:
+            return (self.ref_start, self.ref_end)
+
+    def intersection_area(self, bbox: "Match") -> float:
+        # Compute the intersection boarders
+        inter_q_start = max(self.query_start, bbox.query_start)
+        inter_r_start = max(self.ref_start, bbox.ref_start)
+        inter_q_end = min(self.query_end, bbox.query_end)
+        inter_r_end = min(self.ref_end, bbox.ref_end)
+
+        # Compute the area of intersection rectangle
+        return abs(
+            max((inter_q_end - inter_q_start, 0))
+            * max((inter_r_end - inter_r_start), 0)
+        )
+
+    def overlaps(self, bbox: "Match") -> bool:
+        return self.intersection_area(bbox) > 0.0
+
+    @classmethod
+    def write_csv(cls, matches: Collection["Match"], file: Union[str, TextIO]):
+        df = pd.DataFrame([match._asdict() for match in matches], columns=cls._fields)
+        df.to_csv(file, index=False)
+
+    @classmethod
+    def read_csv(
+        cls, file: Union[str, TextIO], is_gt=False, check=True
+    ) -> List["Match"]:
+        df = pd.read_csv(file)
+        df["query_id"] = df.query_id.map(lambda x: format_video_id(x, Dataset.QUERIES))
+        df["ref_id"] = df.ref_id.map(lambda x: format_video_id(x, Dataset.REFS))
+        if is_gt:
+            df["score"] = 1.0
+        if check:
+            for field in cls._fields:
+                assert not df[field].isna().any()
+        return [Match(**record) for record in df.to_dict("records")]
+
+
+class VideoPair:
+    """A video pair item that contains information regarding the gt and pred bboxes.
+
+    Provide functionalities for the combination of new predictions with the
+    existing ones and the computation of their intersection with the gt bboxes,
+    ignoring the gt bboxes that do not overlap with any prediction.
+    """
+
+    gts: List[Match]
+    preds: List[Match]
+
+    def __init__(
+        self,
+    ):
+        self.intersections = {axis: 0.0 for axis in Axis}
+        self.totals = {axis: 0.0 for axis in Axis}
+        self.gts = []
+        self.preds = []
+
+    def total_gt_length(self, axis: Axis) -> float:
+        return Intervals([gt.interval(axis) for gt in self.gts]).total_length()
+
+    def total_pred_length(self, axis: Axis) -> float:
+        return Intervals([pred.interval(axis) for pred in self.preds]).total_length()
+
+    def gt_overlaps(self, gt: Match) -> bool:
+        """Checks if the provided gt bbox overlaps with at least one pred bbox."""
+        for pred in self.preds:
+            if gt.overlaps(pred):
+                return True
+        return False
+
+    def add_gt(self, bbox: Match):
+        self.gts.append(bbox)
+
+    def add_prediction(
+        self, bbox: Match
+    ) -> Tuple[Dict[Axis, float], Dict[Axis, float]]:
+        """Add a prediction to the corresponding list and calculates the
+        differences in the intersections with the gt and the total video
+        length covered for both query and reference axes.
+        """
+        self.preds.append(bbox)
+
+        # A subset of GTs to consider for intersection (but not total GT length).
+        gts_to_consider = [gt for gt in self.gts if self.gt_overlaps(gt)]
+
+        intersect_deltas = {}
+        total_deltas = {}
+
+        for axis in Axis:
+            pred_ints = Intervals([pred.interval(axis) for pred in self.preds])
+            gt_ints = Intervals([gt.interval(axis) for gt in gts_to_consider])
+            # New intersection and total length on this axis
+            intersect_length = pred_ints.intersect_length(gt_ints)
+            prediction_length = pred_ints.total_length()
+            # Compute differences
+            intersect_deltas[axis] = intersect_length - self.intersections[axis]
+            total_deltas[axis] = prediction_length - self.totals[axis]
+            # Update with new values
+            self.intersections[axis] = intersect_length
+            self.totals[axis] = prediction_length
+
+        return intersect_deltas, total_deltas
+
+
+def match_metric(
+    gts: Collection[Match],
+    predictions: Collection[Match],
+) -> AveragePrecision:
+    """Matching track metric:
+
+    Computes the AP based on the VCSL approach for the
+    calculation of Precision and Recall.
+
+    AP = \sum_{i=1}^N P(i) ΔR(i)
+
+    where, P(i) = sqrt(P_q * P_r) and R(i) = sqrt(R_q * R_r)
+    calculated as in the VCSL.
+    """  # noqa: W605
+
+    predictions = sorted(predictions, key=lambda x: x.score, reverse=True)
+
+    # Initialize video pairs and load their gt bboxs
+    video_pairs = defaultdict(VideoPair)
+    for gt in gts:
+        video_pairs[gt.pair_id()].add_gt(gt)
+
+    # Get the total gt length for each axis
+    gt_total_lengths = {axis: 0.0 for axis in Axis}
+    for _, v in video_pairs.items():
+        for axis in Axis:
+            gt_total_lengths[axis] += v.total_gt_length(axis)
+
+    # Loop through the predictions
+    recall = 0.0
+    metric = 0.0
+    intersections = {axis: 0.0 for axis in Axis}
+    totals = {axis: 0.0 for axis in Axis}
+    pr_recalls = []
+    pr_precisions = []
+    pr_scores = []
+
+    # Update metrics for all predictions with the same score as a group.
+    for score, prediction_group in itertools.groupby(
+        predictions, key=lambda x: x.score
+    ):
+        for prediction in prediction_group:
+            # Given new predictions, we only need the differences in the intersection with
+            # gt and total video length covered for both query and reference axes.
+            # This derives from the sum of differences for every pair id
+            intersection_deltas, total_deltas = video_pairs[
+                prediction.pair_id()
+            ].add_prediction(prediction)
+            for axis in Axis:
+                # Accumulate the differences to the corresponding values
+                intersections[axis] += intersection_deltas[axis]
+                totals[axis] += total_deltas[axis]
+
+        recalls = {}
+        precisions = {}
+        for axis in Axis:
+            recalls[axis] = intersections[axis] / gt_total_lengths[axis]
+            precisions[axis] = intersections[axis] / totals[axis]
+
+        new_recall = sqrt(recalls[Axis.QUERY] * recalls[Axis.REF])
+        precision = sqrt(precisions[Axis.QUERY] * precisions[Axis.REF])
+
+        # Compute metric
+        delta_recall = new_recall - recall
+        metric += precision * delta_recall
+        recall = new_recall
+        if delta_recall > 0:
+            pr_recalls.append(recall)
+            pr_precisions.append(precision)
+            pr_scores.append(score)
+
+    curve = PrecisionRecallCurve(
+        np.array(pr_precisions), np.array(pr_recalls), np.array(pr_scores)
+    )
+    return AveragePrecision(metric, curve)
+
+
+@dataclasses.dataclass
+class MatchingTrackMetrics:
+    # Our main evaluation metric.
+    segment_ap: AveragePrecision
+    # This metric reflects only pairwise matching, and not localization.
+    pairwise_micro_ap: AveragePrecision
+
+
+def evaluate_matching_track(
+    ground_truth_filename: str, predictions_filename: str
+) -> MatchingTrackMetrics:
+    """Matching track evaluation.
+
+    Predictions are expected to be a CSV file, with a column names in the header.
+    The following columns must be present, in any order:
+        query_id: str, the ID of the query for this match
+        ref_id: str, the ID of the reference for this match
+        query_start: float, the start of the query segment in seconds
+        query_end: float, the end of the query segment in seconds
+        ref_start: float, the start of the reference segment in seconds
+        ref_end: float, the end of the reference segment in seconds
+        score: float, the score of this prediction (a higher score indicates a
+            more confident prediction)
+
+    Note that ground-truth matches are specified using the same format, but score
+    is not used.
+    """
+    gt = Match.read_csv(ground_truth_filename, is_gt=True)
+    predictions = Match.read_csv(predictions_filename)
+    metric = match_metric(gt, predictions)
+    # Auxiliary metric: pairwise uAP
+    gt_pairs = CandidatePair.from_matches(gt)
+    pairs = CandidatePair.from_matches(predictions)
+    pair_ap = average_precision(gt_pairs, pairs)
+    return MatchingTrackMetrics(segment_ap=metric, pairwise_micro_ap=pair_ap)
+
+
+def average_precision(
+    ground_truth: Collection[CandidatePair], predictions: Collection[CandidatePair]
+) -> AveragePrecision:
+    gt_pairs = {(pair.query_id, pair.ref_id) for pair in ground_truth}
+    if len(gt_pairs) != len(ground_truth):
+        raise AssertionError("Duplicates detected in ground truth")
+    predicted_pairs = {(pair.query_id, pair.ref_id) for pair in predictions}
+    if len(predicted_pairs) != len(predictions):
+        raise AssertionError("Duplicates detected in predictions")
+
+    # AP calculation that aligns with DrivenData's backend implementation.
+    canonical_ap = drivendata_average_precision(
+        predicted=CandidatePair.to_dataframe(predictions),
+        ground_truth=CandidatePair.to_dataframe(ground_truth),
+    )
+
+    predictions = sorted(predictions, key=lambda x: x.score, reverse=True)
+    scores = np.array([pair.score for pair in predictions])
+    correct = np.array(
+        [(pair.query_id, pair.ref_id) in gt_pairs for pair in predictions]
+    )
+    total_pairs = len(gt_pairs)
+    # precision = correct_so_far / total_pairs_so_far
+    cumulative_correct = np.cumsum(correct)
+    cumulative_predicted = np.arange(len(correct)) + 1
+    recall = cumulative_correct / total_pairs
+    precision = cumulative_correct / cumulative_predicted
+    # Simple AP computation.
+    simple_ap = np.sum(precision * correct) / total_pairs
+    # Get precision and recall where correct is true
+    indices = np.nonzero(correct)[0]
+    curve = PrecisionRecallCurve(precision[indices], recall[indices], scores[indices])
+    return AveragePrecision(ap=canonical_ap, pr_curve=curve, simple_ap=simple_ap)
+
+
+def drivendata_average_precision(
+    predicted: pd.DataFrame,
+    ground_truth: pd.DataFrame,
+):
+    """Canonical AP implementation used for the challenge."""
+    SCORE_COL = "score"
+    QUERY_ID_COL = "query_id"
+    DATABASE_ID_COL = "ref_id"
+    actual = ground_truth[["query_id", "ref_id"]]
+    if (
+        not np.isfinite(predicted[SCORE_COL]).all()
+        or np.isnan(predicted[SCORE_COL]).any()
+    ):
+        raise ValueError("Scores must be finite.")
+
+    predicted = predicted.sort_values(SCORE_COL, ascending=False)
+
+    merged = predicted.merge(
+        right=actual.assign(actual=1.0),
+        how="left",
+        on=[QUERY_ID_COL, DATABASE_ID_COL],
+    ).fillna({"actual": 0.0})
+
+    # We may not predict for every ground truth, so calculate unadjusted AP then adjust it
+    unadjusted_ap = (
+        average_precision_score(merged["actual"].values, merged[SCORE_COL].values)
+        if merged["actual"].sum()
+        else 0.0
+    )
+    # Rescale average precisions based on total ground truth positive counts
+    predicted_n_pos = int(merged["actual"].sum())
+
+    # avoid rows added to validate query ids, will have blank ref_id
+    actual_n_pos = int(actual[DATABASE_ID_COL].notna().sum())
+
+    adjusted_ap = unadjusted_ap * (predicted_n_pos / actual_n_pos)
+    return adjusted_ap

VidEB/DVSC23/vsc/storage.py

@@ -0,0 +1,72 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import List, Dict, Optional
+
+import numpy as np
+from vsc.index import VideoFeature
+from vsc.metrics import Dataset, format_video_id
+
+
+def store_features(f, features: List[VideoFeature], dataset: Optional[Dataset] = None):
+    video_ids = []
+    feats = []
+    timestamps = []
+    for feature in features:
+        video_id = format_video_id(feature.video_id, dataset)
+        video_ids.append(np.full(len(feature), video_id))
+        feats.append(feature.feature)
+        timestamps.append(feature.timestamps)
+    video_ids = np.concatenate(video_ids)
+    feats = np.concatenate(feats)
+    timestamps = np.concatenate(timestamps)
+    np.savez(f, video_ids=video_ids, features=feats, timestamps=timestamps)
+
+
+def same_value_ranges(values):
+    start = 0
+    value = values[start]
+
+    for i, v in enumerate(values):
+        if v == value:
+            continue
+        yield value, start, i
+        start = i
+        value = values[start]
+
+    yield value, start, len(values)
+
+
+def load_features(f, dataset: Optional[Dataset] = None):
+    data = np.load(f, allow_pickle=False)
+    video_ids = data["video_ids"]
+    feats = data["features"]
+    timestamps = data["timestamps"]
+
+    ts_dims = len(timestamps.shape)
+    if timestamps.shape[0] != feats.shape[0]:
+        raise ValueError(
+            f"Expected the same number of timestamps as features: got "
+            f"{timestamps.shape[0]} timestamps for {feats.shape[0]} features"
+        )
+    if not (ts_dims == 1 or timestamps.shape[1:] == (2,)):
+        print(f"timestamps.shape[1:]: {timestamps.shape[1:]}")
+        print(f"timestamps.shape[1:] == [2]: {timestamps.shape[1:] == [2]}")
+        raise ValueError(f"Unexpected timestamp shape. Got {timestamps.shape}")
+
+    results = []
+    for video_id, start, end in same_value_ranges(video_ids):
+        video_id = format_video_id(video_id, dataset)
+        item = VideoFeature(
+            video_id=video_id,
+            timestamps=timestamps[start:end],
+            feature=feats[start:end, :],
+        )
+        results.append(item)
+    return results
+
+
+def convert_to_dict(features: List[VideoFeature]) -> Dict[str, VideoFeature]:
+    return {m.video_id: m for m in features}

VidEB/FIVR-5K/LICENSE

@@ -0,0 +1,202 @@
+Apache License
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+   6. Trademarks. This License does not grant permission to use the trade
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+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
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+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "{}"
+      replaced with your own identifying information. (Don't include
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+   Copyright 2019 Giorgos Kordopatis-Zilos. All rights reserved.
+
+   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
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+   limitations under the License.
+

VidEB/FIVR-5K/README.md

@@ -0,0 +1,89 @@
+# FIVR-5K
+<img src="https://raw.githubusercontent.com/MKLab-ITI/FIVR-200K/master/banner.png" width="100%">
+
+
+## Dataset format
+
+* The video annotations are in file annotation.json that has the following format:
+```bash
+{
+  "5MBA_7vDhII": {
+    "ND": [
+      "_0uCw0B2AgM",
+      ...],
+    "DS": [
+      "hc0XIE1aY0U",
+      ...],
+    "CS": [
+      "ydEqiuDiuyc",
+      ...],    
+    "IS": [
+      "d_ZNjE7B4Wo",
+      ...],
+    "DA": [
+      "rLvVYdtc73Q",
+      ...],
+  },
+  ....
+}
+```
+
+
+## Evaluation
+
+1. Generation of the result file 
+    * A file that contains a dictionary with keys the YT ids of the query videos and values another dictionary with keys the YT ids of the dataset videos and values their similarity to the query.
+
+    * Results can be stored in a JSON file with the following format:
+    ```bash
+    {
+      "wrC_Uqk3juY": {
+        "KQh6RCW_nAo": 0.716,
+        "0q82oQa3upE": 0.300,
+          ...},
+      "k_NT43aJ_Jw": {
+        "-KuR8y1gjJQ": 1.0,
+        "Xb19O5Iur44": 0.417,
+          ...},
+      ....
+    }
+    ```
+
+   * An implementation for the generation of the JSON file can be found [here](https://github.com/MKLab-ITI/FIVR-200K/blob/1c0c093f29eea6c71f8f154408b2a371b74cb427/calculate_similarities.py#L79)
+
+2. Evaluation of the results
+    * Run the following command to run the evaluation:
+    ```bash
+    python evaluation.py --result_file RESULT_FILE --relevant_labels RELEVANT_LABELS
+    ```
+
+    * An example to run the evaluation script:
+    ```bash
+    python evaluation.py --result_file ./results/lbow_vgg.json --relevant_labels ND,DS
+    ```
+    
+    * Add flag `--help` to display the detailed description for the arguments of the evaluation script
+    
+3. Evaluation on the three retrieval task
+    * Provide different values to the `relevant_labels` argument to evaluate your results for the three visual-based retrieval task
+    ```bash
+    DSVR: ND,DS
+    CSVR: ND,DS,CS
+    ISVR: ND,DS,CS,IS
+    ```
+    * For the Duplicate Audio Video Retrieval (DAVR) task provide `DA` to the `relevant_labels` argument
+
+
+## Citation
+If you use FIVR-200K dataset for your research, please consider citing the papers:
+```bibtex
+@article{kordopatis2019fivr,
+  title={{FIVR}: Fine-grained Incident Video Retrieval},
+  author={Kordopatis-Zilos, Giorgos and Papadopoulos, Symeon and Patras, Ioannis and Kompatsiaris, Ioannis},
+  journal={IEEE Transactions on Multimedia},
+  year={2019}
+}
+```
+
+## License
+This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details

VidEB/FIVR-5K/evaluation.py

@@ -0,0 +1,162 @@
+# Copyright 2019 Giorgos Kordopatis-Zilos. All Rights Reserved.
+#
+# 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.
+# ==============================================================================
+"""
+Implementation of the FIVR evaluation process
+"""
+
+from __future__ import division
+from __future__ import print_function
+
+import json
+import argparse
+import numpy as np
+import matplotlib.pyplot as plt
+
+from tqdm import tqdm
+from argparse import RawTextHelpFormatter
+from future.utils import viewitems, lrange
+from sklearn.metrics import precision_recall_curve
+
+
+def plot_pr_curve(pr_curve):
+    """
+      Function that plots the interpolated PR-curve.
+
+      Args:
+        pr_curve: the values of precision for each recall step
+    """
+    plt.figure(figsize=(16, 9))
+    plt.plot(np.arange(0.0, 1.05, 0.05),
+             pr_curve, color='r', marker='o', linewidth=3, markersize=10)
+    plt.grid(True, linestyle='dotted')
+    plt.xlabel('Recall', color='k', fontsize=27)
+    plt.ylabel('Precision', color='k', fontsize=27)
+    plt.yticks(color='k', fontsize=20)
+    plt.xticks(color='k', fontsize=20)
+    plt.ylim([0.0, 1.05])
+    plt.xlim([0.0, 1.0])
+    plt.tight_layout()
+    plt.show()
+
+
+def evaluate(annotations, results, relevant_labels, dataset, quiet):
+    """
+      Calculate of mAP and interpolated PR-curve based on the FIVR evaluation process.
+
+      Args:
+        annotations: the annotation labels for each query
+        results: the similarities of each query with the videos in the dataset
+        relevant_labels: labels that are considered positives
+        dataset: video ids contained in the dataset
+      Returns:
+        mAP: the mean Average Precision
+        ps_curve: the values of the PR-curve
+    """
+    pr, mAP = [], []
+    iterations = viewitems(annotations) if not quiet else tqdm(viewitems(annotations))
+    for query, gt_sets in iterations:
+        if query not in results: print('WARNING: Query {} is missing from the result file'.format(query)); continue
+        if query not in dataset: print('WARNING: Query {} is not in the dataset'.format(query)); continue
+
+        # set of relevant videos
+        query_gt = set(sum([gt_sets[label] for label in relevant_labels if label in gt_sets], []))
+        query_gt = query_gt.intersection(dataset)
+        if not query_gt: print('WARNING: Empty annotation set for query {}'.format(query)); continue
+
+        # calculation of mean Average Precision (Eq. 6)
+        i, ri, s = 0.0, 0, 0.0
+        y_target, y_score = [], []
+        for video, sim in sorted(viewitems(results[query]), key=lambda x: x[1], reverse=True):
+            if video != query and video in dataset:
+                y_score.append(sim)
+                y_target.append(1.0 if video in query_gt else 0.0)
+                ri += 1
+                if video in query_gt:
+                    i += 1.0
+                    s += i / ri
+        mAP.append(s / len(query_gt))
+        if not quiet:
+            print('Query:{}\t\tAP={:.4f}'.format(query, s / len(query_gt)))
+
+        # add the dataset videos that are missing from the result file
+        missing = len(query_gt) - y_target.count(1)
+        y_target += [1.0 for _ in lrange(missing)] # add 1. for the relevant videos
+        y_target += [0.0 for _ in lrange(len(dataset) - len(y_target))] # add 0. for the irrelevant videos
+        y_score += [0.0 for _ in lrange(len(dataset) - len(y_score))]
+
+        # calculation of interpolate PR-curve (Eq. 5)
+        precision, recall, thresholds = precision_recall_curve(y_target, y_score)
+        p = []
+        for i in lrange(20, -1, -1):
+            idx = np.where((recall >= i * 0.05))[0]
+            p.append(np.max(precision[idx]))
+        pr.append(p)
+
+    return mAP, np.mean(pr, axis=0)[::-1]
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(formatter_class=RawTextHelpFormatter)
+    parser.add_argument('-r', '--result_file',
+                        required=True,
+                        help='File of where the results are stored. It must be in JSON format')
+    parser.add_argument('-rl', '--relevant_labels',
+                        default='ND,DS',
+                        help='Labels of the videos that considered relevant depending on the retrieval task'
+                             '\nDSVR: ND,DS\nCSVR: ND,DS,CS\nISVR: ND,DS,CS,IS')
+    parser.add_argument('-a', '--annotations_file',
+                        default='dataset/annotation.json',
+                        help='File that contains the video annotations of the FIVR-200K dataset')
+    parser.add_argument('-d', '--dataset_ids',
+                        default='dataset/youtube_ids.txt',
+                        help='File that contains the Youtube IDs of the videos in FIVR-200K dataset')
+    parser.add_argument('-e', '--export_file',
+                        default='mAP_PRcurve_points.csv',
+                        help='File where the results will be stored')
+    parser.add_argument('-s', '--save_results',
+                        action='store_true',
+                        help='Flag that indicated whether the results will be stored')
+    parser.add_argument('-q', '--quiet',
+                        action='store_true',
+                        help='Flag that indicated whether the results per query will be printed')
+    parser.add_argument('-p', '--plot_pr_curve',
+                        action='store_true',
+                        help='Flag that indicated whether the PR-curve will be displayed')
+    args = parser.parse_args()
+
+    # load all the necessary files
+    with open(args.result_file, 'r') as f:
+        if not args.quiet: print('Loading results from file:', args.result_file)
+        results = json.load(f)
+    with open(args.annotations_file, 'r') as f:
+        if not args.quiet: print('Loading annotations from file:', args.annotations_file)
+        annotations = json.load(f)
+    dataset = set(np.loadtxt(args.dataset_ids, dtype=str))
+    relevant_labels = args.relevant_labels.split(',')
+
+    # run the evaluation process
+    mAP, pr_curve = evaluate(annotations, results, relevant_labels, dataset, args.quiet)
+
+    # report the results
+    print('==========================================')
+    print('Total queries: {}\t\tmAP={:.4f}'.format(len(mAP), np.mean(mAP)))
+    if args.plot_pr_curve: plot_pr_curve(pr_curve)
+
+    # save the numeric values in a csv file
+    if args.save_results:
+        with open(args.export_file, 'w') as f:
+            f.write('mAP,{}\n\n'.format(np.mean(mAP)))
+            f.write('Recall,{}\n'.format(','.join(map(str, np.arange(0.0, 1.05, 0.05)))))
+            f.write('Precision,{}'.format(','.join(map(str, pr_curve))))

VidEB/FIVR-5K/requirements.txt

@@ -0,0 +1,6 @@
+numpy>=1.9
+tqdm>=4.2
+scikit-learn>=0.19
+scipy>=1.0
+matplotlib>=2.2
+future>=0.16

VidEB/annotations/DVSC23/database/class_map.json

@@ -0,0 +1 @@
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VidEB/annotations/FIVR-5K/download_dataset.py

@@ -0,0 +1,101 @@
+# Copyright 2019 Giorgos Kordopatis-Zilos. All Rights Reserved.
+#
+# 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.
+# ==============================================================================
+"""
+Script that downloads the videos in the FIVR-200K dataset
+"""
+
+from __future__ import division
+from __future__ import print_function
+
+import os
+import argparse
+import yt_dlp
+import numpy as np
+
+from tqdm import tqdm
+from multiprocessing import Pool
+
+
+def download_video(video_id, args):
+    """
+      Download the provided video using the yt-dlp library
+
+      Args:
+        video_id: Youtube ID of the video
+        args: arguments provided by the user
+      Returns:
+        a flag that indicates whether there was an error during the downloading
+    """
+    try:
+        ydl_opts = {
+                'format': 'best[height<={}][ext=mp4]/best[ext=mp4]/best[height<={}]/best'
+                    .format(args.resolution, args.resolution),
+                'outtmpl': '{}/{}.%(ext)s'.format(args.video_dir, video_id),
+                'quiet': True,
+                'no_warnings': True
+            }
+        ydl = yt_dlp.YoutubeDL(ydl_opts)
+        video_url = video_id if 'http' not in video_id else 'https://www.youtube.com/watch?v={}'.format(video_id)
+        ydl.download([video_url])
+        return True
+    except:
+        return False
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-v', '--video_dir',
+                        required=True,
+                        help='Directory where the downloaded videos will be stored')
+    parser.add_argument('-d', '--dataset_ids',
+                        default='dataset/youtube_ids.txt',
+                        help='File that contains the Youtube IDs of the videos in FIVR-200K dataset')
+    parser.add_argument('-c', '--cores',
+                        default=8, type=int,
+                        help='Number of cores that will be used for downloading.')
+    parser.add_argument('-r', '--resolution',
+                        default=480, type=int,
+                        help='Preferred resolution to download videos.')
+    args = parser.parse_args()
+
+    # create root dir
+    if not os.path.exists(args.video_dir):
+        os.makedirs(args.video_dir)
+
+    dataset = np.loadtxt(args.dataset_ids, dtype=str)
+    missing_videos = set(np.loadtxt('missing_videos.txt', dtype=str)) \
+        if os.path.exists('missing_videos.txt') else set()
+    with open('missing_videos.txt', 'a') as f:
+        progress_bar = tqdm(dataset)
+        if args.cores > 1:
+            future = []
+            pool = Pool(args.cores)
+            for video in dataset:
+                future.append(pool.apply_async(download_video, args=[video, args],
+                                               callback=(lambda *a: progress_bar.update())))
+
+            for video, flag in zip(dataset, future):
+                if not flag.get() and video not in missing_videos:
+                    f.write('{}\n'.format(video))
+                    f.flush()
+                    missing_videos.add(video)
+            pool.terminate()
+        else:
+            for video in progress_bar:
+                flag = download_video(video, args)
+                if not flag and video not in missing_videos:
+                    f.write('{}\n'.format(video))
+                    f.flush()
+    print('Downloading completed!')

VidEB/annotations/FIVR-5K/queries/class_map.json

@@ -0,0 +1 @@
+{"0": "GJij-4mt6SA.mp4", "1": "yL1ddCm55y4.mp4", "2": "3ei-VfSpxvA.mp4", "3": "2n30dbPBNKE.mp4", "4": "dQEuwGZulSM.mp4", "5": "RfgJ6VfFR64.mp4", "6": "5MBA_7vDhII.mp4", "7": "cJBA2PjbeJA.mp4", "8": "K8PckJMMpRU.mp4", "9": "PYo1e0cwWws.mp4", "10": "5CVZLZZqvzI.mp4", "11": "6dPo0jmeGZc.mp4", "12": "cDXTcNL1ebY.mp4", "13": "k_NT43aJ_Jw.mp4", "14": "6at-OEesGXc.mp4", "15": "VcDNzL_6U6Y.mp4", "16": "sxYUicqiZvw.mp4", "17": "VCHxOe0Mfvw.mp4", "18": "FvcRE7-bnBM.mp4", "19": "NUaRDrYi1sw.mp4", "20": "D6oBg1vVQZo.mp4", "21": "9aqU9UiDBD4.mp4", "22": "UEqsykDyXus.mp4", "23": "hILt-pP83AU.mp4", "24": "K_ZCc46lJBk.mp4", "25": "OTNDjvvtPF8.mp4", "26": "AOC5YGyoGDw.mp4", "27": "8kBKA7WpdXs.mp4", "28": "eCrhXArKE24.mp4", "29": "e33dD_qoqhg.mp4", "30": "Zskb1cnH63Q.mp4"}

VidEB/annotations/FIVR-5K/queries/test.csv

@@ -0,0 +1,31 @@
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+Zskb1cnH63Q.mp4 30

VidEB/annotations/FIVR-5K/used_videos.txt

@@ -0,0 +1,3446 @@
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VidEB/example.ipynb

@@ -0,0 +1,431 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import av\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "from transformers import SiglipImageProcessor\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "from transformers import SiglipModel\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "import json\n",
+    "\n",
+    "DEVICE= torch.device(\"cuda:1\" if torch.cuda.is_available() else \"cpu\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_video_pyav(container, indices):\n",
+    "    '''\n",
+    "    Decode the video with PyAV decoder.\n",
+    "    Args:\n",
+    "        container (`av.container.input.InputContainer`): PyAV container.\n",
+    "        indices (`List[int]`): List of frame indices to decode.\n",
+    "    Returns:\n",
+    "        result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).\n",
+    "    '''\n",
+    "    frames = []\n",
+    "    container.seek(0)\n",
+    "    start_index = indices[0]\n",
+    "    end_index = indices[-1]\n",
+    "    for i, frame in enumerate(container.decode(video=0)):\n",
+    "        if i > end_index:\n",
+    "            break\n",
+    "        if i >= start_index and i in indices:\n",
+    "            frames.append(frame)\n",
+    "    return np.stack([x.to_ndarray(format=\"rgb24\") for x in frames])\n",
+    "\n",
+    "\n",
+    "def sample_frame_indices(clip_len, frame_sample_rate, seg_len, num_segment, chunk_nb):\n",
+    "    '''\n",
+    "    Sample a given number of frame indices from the video.\n",
+    "    Args:\n",
+    "        clip_len (`int`): Total number of frames to sample.\n",
+    "        frame_sample_rate (`int`): Sample every n-th frame.\n",
+    "        seg_len (`int`): Maximum allowed index of sample's last frame.\n",
+    "    Returns:\n",
+    "        indices (`List[int]`): List of sampled frame indices\n",
+    "    '''\n",
+    "    \n",
+    "    end_idx = seg_len - 1\n",
+    "    start_idx = 0\n",
+    "    indices = np.linspace(start_idx, end_idx, num=num_segment * clip_len)[chunk_nb*clip_len:(chunk_nb+1)*clip_len]\n",
+    "    indices = np.clip(indices, 0, seg_len - 1).astype(np.int64)\n",
+    "    return indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class VideoClsDataset(Dataset):\n",
+    "    def __init__(self, anno_path, prefix, processor, num_segment) -> None:\n",
+    "        super().__init__()\n",
+    "        cleaned = pd.read_csv(anno_path, header=None, delimiter=' ')\n",
+    "        self.samples = list(cleaned.values[:, 0])\n",
+    "        self.labels = list(cleaned.values[:, 1])\n",
+    "        self.num_segment = num_segment\n",
+    "        \n",
+    "        self.label_array = list()\n",
+    "        self.dataset_samples = list()\n",
+    "        for i in range(self.num_segment):\n",
+    "            for sample, label in zip(self.samples, self.labels):\n",
+    "                self.label_array.append((i, label))\n",
+    "                self.dataset_samples.append(sample)\n",
+    "            \n",
+    "        self.prefix = prefix\n",
+    "        self.processor = processor\n",
+    "    def __len__(self):\n",
+    "        return len(self.label_array)\n",
+    "    def __getitem__(self, index):\n",
+    "        video_file = os.path.join(self.prefix, self.dataset_samples[index])\n",
+    "        chunk_nb, label = self.label_array[index]\n",
+    "        container = av.open(video_file)\n",
+    "        indices = sample_frame_indices(clip_len=16, frame_sample_rate=1, seg_len=container.streams.video[0].frames, num_segment = self.num_segment, chunk_nb=chunk_nb)\n",
+    "        video = read_video_pyav(container, indices)\n",
+    "        inputs = self.processor.preprocess(list(video), return_tensors='pt')\n",
+    "        inputs.update(labels=label)\n",
+    "        return inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = SiglipModel.from_pretrained(\"google/siglip-so400m-patch14-384\") #google/siglip-so400m-patch14-384\n",
+    "model.to(DEVICE)\n",
+    "preprocess = SiglipImageProcessor.from_pretrained(\"google/siglip-so400m-patch14-384\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@torch.no_grad()\n",
+    "def extract_feature(data_loader, model):\n",
+    "    model.eval()\n",
+    "    final_result = dict()\n",
+    "    for batch in tqdm(data_loader):\n",
+    "        for k in batch.keys():\n",
+    "            batch[k] = batch[k].to(DEVICE, non_blocking=True)\n",
+    "        B, T, C, H, W = batch['pixel_values'].shape\n",
+    "        batch['pixel_values'] = batch['pixel_values'].flatten(0,1)\n",
+    "        target = batch.pop('labels')\n",
+    "        output =  model.get_image_features(**batch)\n",
+    "        output =  output.reshape(B, T, output.shape[-1])\n",
+    "        for id, feat in zip(target, output):\n",
+    "            id = id.item()\n",
+    "            feat = feat.cpu().numpy()\n",
+    "            if final_result.get(id, None) is None:\n",
+    "                final_result[id] = []\n",
+    "            final_result[id].append(feat)\n",
+    "        break\n",
+    "    for k, v in final_result.items():\n",
+    "        final_result[k] = np.concatenate(final_result[k], axis=0)\n",
+    "    #np.save(file + \"_embedding\", np.array(list(final_result.values())))\n",
+    "    #np.save(file + \"_labels\", np.array(list(final_result.keys())))\n",
+    "    return np.array(list(final_result.values())), np.array(list(final_result.keys()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# FOR DVSC23"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATASET_NAME = \"DVSC23\"\n",
+    "DATA_PATH_QUERY=f'annotations/{DATASET_NAME}/queries/test.csv'\n",
+    "DATA_PATH_DATABASE=f'annotations/{DATASET_NAME}/database/test.csv'\n",
+    "\n",
+    "QUERY_PREFIX=f'/path/to/prefix_path_of_query'\n",
+    "DATABASE_PREFIX=f'/path/to/prefix_path_of_database'\n",
+    "\n",
+    "QUERY_MAP_PATH = f\"annotations/{DATASET_NAME}/queries/class_map.json\"\n",
+    "DATABASE_MAP_PATH = f\"annotations/{DATASET_NAME}/database/class_map.json\"\n",
+    "\n",
+    "OUTPUT_DIR_QUERY=f'{DATASET_NAME}/queries'\n",
+    "OUTPUT_DIR_DATABASE=f'{DATASET_NAME}/database'\n",
+    "os.makedirs(OUTPUT_DIR_QUERY, exist_ok=True)\n",
+    "os.makedirs(OUTPUT_DIR_DATABASE, exist_ok=True)\n",
+    "\n",
+    "NUM_SEGMENTS = 5\n",
+    "BATCH_SIZE = 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def normalize(embeddings):\n",
+    "    #embeddings = (embeddings - embeddings.mean(0, keepdims=True)) / (embeddings.std(0, keepdims=True) + 1e-6)\n",
+    "    embeddings /= np.linalg.norm(embeddings, axis=-1, keepdims=True) + 1e-6\n",
+    "    embeddings = (embeddings - embeddings.mean(0, keepdims=True)) / (embeddings.std(0, keepdims=True) + 1e-6)\n",
+    "    return embeddings\n",
+    "\n",
+    "def save_features(embeddings, ids, save_path, video_id_map):\n",
+    "    \n",
+    "    def flatten(data, ids):\n",
+    "        T = data.shape[1]\n",
+    "        return data.reshape(-1, data.shape[-1]), np.tile(ids[:, None],(1, T)).flatten()\n",
+    "    embeddings, ids = flatten(embeddings, ids)\n",
+    "\n",
+    "\n",
+    "    video_ids = [video_id_map[str(id)] for id in ids]  # Can also be str: \"Q20000\", ...\n",
+    "    sort_ids = np.argsort(video_ids).tolist()\n",
+    "    np.savez(\n",
+    "        save_path,\n",
+    "        video_ids=np.sort(video_ids).tolist(),\n",
+    "        timestamps=[[0, 1]]*len(video_ids),\n",
+    "        features=embeddings[sort_ids].astype(np.float32),\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#extract feature for query in DVSC23\n",
+    "query_dataset = VideoClsDataset(DATA_PATH_QUERY, QUERY_PREFIX, preprocess, NUM_SEGMENTS)\n",
+    "dataloader = DataLoader(query_dataset, num_workers=4, batch_size=BATCH_SIZE)\n",
+    "query_embeddings, query_ids = extract_feature(dataloader, model)\n",
+    "query_embeddings = normalize(query_embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(QUERY_MAP_PATH, 'r') as file:\n",
+    "    query_map = json.load(file)\n",
+    "save_features(query_embeddings, query_ids, f'{OUTPUT_DIR_QUERY}/descriptors.npz', query_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#extract feature for reference in DVSC23\n",
+    "ref_dataset = VideoClsDataset(DATA_PATH_DATABASE, DATABASE_PREFIX, preprocess, NUM_SEGMENTS)\n",
+    "dataloader = DataLoader(ref_dataset, num_workers=4, batch_size=BATCH_SIZE)\n",
+    "ref_embeddings, ref_ids = extract_feature(dataloader, model)\n",
+    "ref_embeddings = normalize(ref_embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(DATABASE_MAP_PATH, 'r') as file:\n",
+    "    database_map = json.load(file)\n",
+    "save_features(ref_embeddings, ref_ids, f'{OUTPUT_DIR_DATABASE}/descriptors.npz', database_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%cd DVSC23\n",
+    "!python descriptor_eval.py  \\\n",
+    "    --query_features '{OUTPUT_DIR_QUERY}/descriptors.npz' \\\n",
+    "    --ref_features '{OUTPUT_DIR_DATABASE}/descriptors.npz' \\\n",
+    "    --ground_truth ../annotations/DVSC23/test_ground_truth.csv\n",
+    "%cd .."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# FOR FIVR-5K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_PATH_QUERY='annotations/FIVR-5K/queries/test.csv'\n",
+    "DATA_PATH_DATABASE='annotations/FIVR-5K/database/test.csv'\n",
+    "\n",
+    "QUERY_PREFIX=f'/path/to/prefix_path_of_query'\n",
+    "DATABASE_PREFIX=f'/path/to/prefix_path_of_database'\n",
+    "\n",
+    "\n",
+    "QUERY_MAP_PATH = \"annotations/FIVR-5K/queries/class_map.json\"\n",
+    "DATABASE_MAP_PATH = \"annotations/FIVR-5K/database/class_map.json\"\n",
+    "\n",
+    "\n",
+    "OUTPUT_DIR='FIVR-5K'\n",
+    "os.makedirs(OUTPUT_DIR, exist_ok=True)\n",
+    "\n",
+    "NUM_SEGMENTS = 1\n",
+    "BATCH_SIZE = 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_similarity(test_feature, train_feature):\n",
+    "    test_feature /= np.linalg.norm(test_feature, axis=-1, keepdims=True) + 1e-6\n",
+    "    train_feature /= np.linalg.norm(train_feature, axis=-1, keepdims=True) + 1e-6\n",
+    "    return test_feature @ (train_feature.T)\n",
+    "\n",
+    "\n",
+    "def save_predictions(logits, save_path, query_map, database_map):\n",
+    "    prediction = dict()\n",
+    "    for query_id, query_prob in enumerate(logits):\n",
+    "        prob_per_query = dict()\n",
+    "        for ref_id, prob in enumerate(query_prob):\n",
+    "            if prob.item() > 0 :\n",
+    "                prob_per_query[str(database_map[str(ref_id)]).split('.')[0]] = prob.item()\n",
+    "        prediction[query_map[str(query_id)].split('.')[0]] = prob_per_query\n",
+    "    with open(save_path, 'w') as f:\n",
+    "        json.dump(prediction, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#extract feature for query in FIVR-5K\n",
+    "query_dataset = VideoClsDataset(DATA_PATH_QUERY, QUERY_PREFIX, preprocess, NUM_SEGMENTS)\n",
+    "dataloader = DataLoader(query_dataset, num_workers=4, batch_size=BATCH_SIZE)\n",
+    "query_embeddings, query_ids = extract_feature(dataloader, model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#extract feature for reference in FIVR-5K\n",
+    "ref_dataset = VideoClsDataset(DATA_PATH_DATABASE, DATABASE_PREFIX, preprocess, NUM_SEGMENTS)\n",
+    "dataloader = DataLoader(ref_dataset, num_workers=4, batch_size=BATCH_SIZE)\n",
+    "ref_embeddings, ref_ids = extract_feature(dataloader, model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(QUERY_MAP_PATH, 'r') as file:\n",
+    "    query_map = json.load(file)\n",
+    "\n",
+    "with open(DATABASE_MAP_PATH, 'r') as file:\n",
+    "    database_map = json.load(file)\n",
+    "\n",
+    "similarity = calculate_similarity(query_embeddings.mean(1), ref_embeddings.mean(1))\n",
+    "save_predictions(similarity, f\"{OUTPUT_DIR}/prediction.json\", query_map, database_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%cd FIVR-200K\n",
+    "!python evaluation.py --result_file \"../{OUTPUT_DIR}/prediction.json\" \\\n",
+    "                      --relevant_labels ND,DS \\\n",
+    "                      --dataset_ids ../annotations/FIVR-5K/used_videos.txt \\\n",
+    "                      --annotations_file ../annotations/FIVR-5K/annotation.json\n",
+    "%cd .."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%cd FIVR-200K\n",
+    "!python evaluation.py --result_file \"../{OUTPUT_DIR}/prediction.json\" \\\n",
+    "                      --relevant_labels ND,DS,CS \\\n",
+    "                      --dataset_ids ../annotations/FIVR-5K/used_videos.txt \\\n",
+    "                      --annotations_file ../annotations/FIVR-5K/annotation.json\n",
+    "%cd .."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%cd FIVR-200K\n",
+    "!python evaluation.py --result_file \"../{OUTPUT_DIR}/prediction.json\" \\\n",
+    "                      --relevant_labels ND,DS,CS,IS \\\n",
+    "                      --dataset_ids ../annotations/FIVR-5K/used_videos.txt \\\n",
+    "                      --annotations_file ../annotations/FIVR-5K/annotation.json\n",
+    "%cd .."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "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": "3.8.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}