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actev_scorer's Introduction

ActEV Scoring Software

Version: 0.5.8

Date: November 4, 2021


Introduction

This software package contains a scoring script for the TRECVID Activities in Extended Video (ActEV) task. The script ActEV_Scorer.py, is a Python 3.7 script that will validate and score a system output file adhering to the JSON format defined in the ActEV evaluation plan. A collection of unit and integration test cases have also been included, see the setup section for more detail.

ActEV_Scorer.py, when run with the '-h' option, will show the script's usage text. The example_run.sh contains an example of typical usage (using the provided test data).

Setup

This package assumes a Unix-like environment. Included Python files are written for Python 3.7

  1. Install Python 3.7+, jq and required dependencies using make install_pip or make install_conda
  2. Run the tests (optional, but strongly recommended) using make check

Option description

SCORING_PROTOCOL - Positional argument, from a fixed set of values (e.g. ActEV18_AD). This required argument controls what system output formats are valid, and what metrics are computed. A description of each supported protocol can be found in the Protocols section of this document.

Common

  • -s SYSTEM_OUTPUT_FILE - Required; path to the system output JSON file to be scored
  • -r REFERENCE_FILE - Required unless running validation only; path to the reference JSON file to score against
  • -a ACTIVITY_INDEX - Required; path to activity index JSON file. This file lists what activities the system output will be evaluated on
  • -f FILE_INDEX - Required; path to file index JSON file. This file lists the files, and temporal ranges within those files, that the system output will be evaluated on ( NOTE currently the temporal ranges specified in FILE_INDEX are not used when considering what portion of the system output and reference to evaluate, and are only used to compute the duration of files. This will be implemented in a future release)
  • -o OUTPUT_DIR - Required unless running validation only; directory for computed scores and figures
  • -d - Optional; by default, the script will produce a Detection Error Tradeoff (DET) curve figure for each activity and a combined figure of all activity curves. If the '-d' option is set, no DET curves will be produced by the script
  • -p SCORING_PARAMETERS_FILE - Optional; path to a scoring parameter JSON file. If provided, overwrites the default parameters used for scoring
  • -v - Optional; if enabled, the script will be more verbose (i.e. provide some scoring progress information)
  • -V - Optional; if enabled, the SYSTEM_OUTPUT_FILE will be validated but not scored. REFERENCE_FILE and OUTPUT_DIR parameters are not required if this option is enabled
  • -F - Optional; if enabled, ignores extraneous "filesProcessed" or "processingReport" if provided and ignores system and reference instance localizations for extraneous files. Note that extraneous files in this sense are those not included in the FILE_INDEX
  • -m - Optional; if enabled, ignore system detection localizations for files not included in the SYSTEM_OUTPUT_FILE
  • -t DET_Point_Resolution - Optional; if enabled, this will change the number of points used for the det curves to be the input integer value rather than the max
  • -P PERCENTAGE - Optional; if set, the system output will be pruned, keeping PERCENTAGE of the original SYSTEM_OUTPUT_FILE
  • -i - Optional; if set, ignore no score regions.
  • -n - Optional; if set, define the number of processes to use for alignments and results computation. Default to 8
  • -c - Optional; if set, specify the path for the plotting parameters JSON file (see test_17_0 for an example)
  • -I - Optional; if set, do not ignore activities that are not in the reference activity instances
  • -S - Optional; if set, skip system output validation step
  • -e - Optinal; if set, compute extra metrics such as mAP
  • --transformations - Optional; if set, converts the json object to the maximum posible bounding box size
  • --rewrite - Optional; if set, rewrites transformed jsons with the given extension

Object detection related options

  • -j - Optional; if set, dump out per-frame object alignment records

Protocols

ActEV18_AD - Scoring protocol for the ActEV 2018 Activity Detection task, the following measures are computed:

  • PMiss at RFA for RFA values of 1, 0.2, 0.15, 0.1, 0.03, and 0.01
  • NMIDE: NOTE currently using a no-score collar size of 0 frames, this will likely change in a future release)
  • NMIDE: at RFA for RFA values of 1, 0.2, 0.15, 0.1, 0.03, and 0.01

ActEV18PC_AD - Scoring protocol for the ActEV18 Prize Challenge Activity Detection task

ActEV18_AD_TFA - Scoring protocol for the ActEV18 Activity Detection task with Temporal False Alarm

ActEV18_AD_1SECOL - Scoring protocol for the ActEV18 Activity Detection task with 1 Second Overlap Kernel Function

ActEV18_AOD - Scoring protocol for the ActEV 2018 Activity and Object Detection task. This protocol computes both the PMiss at RFA and NMIDE measures reported for the ActEV18_AD protocol, but over an activity instance alignment that also considers object detections. The following additional measure are computed:

  • minMODE: The minimum NMODE score for an object detection alignment; reported for each aligned activity instance pair
  • PMiss at RFA for RFA values of 0.5, 0.2, 0.1, 0.033 for object detections (these measures are prefixed with "object-" to differentiate them from PMiss at RFA measures on activity detections)

ActEV18_AODT - Scoring protocol for the ActEV 2018 Activity and Object Detection and Tracking task. This protocol computes both PMiss at RFA, NMIDE, and minMODE measures reported for the ActEV18_AD and ActEV18_AOD protocols, but over an activity instance alignment that also considers object detections. The following additional measures are computed:

  • MOTE: the Multiple Object Tracking Error for an object detection and tracking alignment.

ActEV19_AD - Scoring protocol for the ActEV 2019 Activity Detection task. The difference between ActEV18_AD and ActEV19_AD is correct instances require at least 1 second of overlap with the reference and the use of Time-based False Alarms (TFA).

ActEV19_AD_V2 - Scoring protocol for Version 2 of the ActEV 2019 Activity Detection task. The difference between ActEV19_AD and ActEV19_AD_V2 is correct instances require above a specified percentage of the reference activity must be overlapped by the system activity.

ActEV_SDL_V1 - Scoring protocol for Version 1 of the ActEV Sequestered Data Leaderboard Activity. This version revises the computation of Time-based False Alarm to include false alarm time during reference instances when the system produces detections in excess of the reference instances.

ActEV_SDL_V2 - Scoring protocol for Version 2 of the ActEV Sequestered Data Leaderboard Activity.

SRL_AD_V1 - Scoring protocol for the Self-Reported Leaderboard

SRL_AOD_V1 - Scoring protocol for the Self-Reported Leaderboard with object detection

SRL_AOD_V2 - Scoring protocol for the Self-Reported Leaderboard with object detection V2 - 50% Looser Correctness

SRL_AD_V2 - Scoring protocol for the Self-Reported Leaderboard V2 - 50% Looser Correctness

SRL_AOD_V3 - Scoring protocol for the Self-Reported Leaderboard with object detection V3 - 100% Tighter Correctness

SRL_AD_V3 - Scoring protocol for the Self-Reported Leaderboard V3 - 100% Tighter Correctness

Output

The scoring script writes to several files in the specified OUTPUT_DIR directory (all *.csv files are pipe separated):

  • scores_by_activity.csv - Scores by activity; computed with respect to the selected scoring protocol
  • scores_aggregated.csv - Aggregated scores over all activities
  • scoring_parameters.json - Lists the scoring/protocol parameters used
  • alignment.csv - Lists each of the matched and unmatched system and reference instances, along with the matching kernel components and similarity scores
  • pair_metrics.csv - Metrics computed on the matched system/reference instance pairs
  • figures/DET_<activity>.png - Unless disabled with the '-d' option, the DET curve figure for <activity>
  • figures/[email protected]_<activity>.png - If enabled zith '-e' option, the Precision/Recall curve, with a temporal IoU threshold of 0.5

Option/Protocol dependent:

  • object_alignment.csv - For the "ActEV18_AOD" and "ActEV18_AODT" protocols, enabled with '-j'; Lists the frame-by-frame object alignments for each pair of aligned activity instances

Changelog

Jan 31, 2018 - Version 0.0.1

  • Initial release

Feb 22, 2018 - Version 0.0.2

  • Fixed an issue where system or reference files without instances for an activity listed in the activity index would cause the script to fail
  • Fixed an issue with older versions of the matplotlib package, which would cause the script fail when plotting activities with no false alarms

March 16, 2018 - Version 0.1.0

  • Renamed the ActEV18 protocol to ActEV18_AD, as it's specifically for the Activity Detection task
  • Added scoring protocol for ActEV18_AOD. This protocol includes an option (-j) to dump out the frame-by-frame object alignments for each pair of aligned activity instances
  • Updated schema and code to expect "presenceConf" instead of "decisionScore" for activity detections. The headers of some output files have been updated to reflect this change
  • Changed "config.csv" output to be a serialized JSON, named "scoring_parameters.json"
  • Added a command line option to accept a scoring parameters JSON file to overwrite the protocol's default parameters for the scoring run
  • The script now checks the file index against the "filesProcessed" reported in the system output file for congruence
  • Added N-MIDE measure to aggregate scores, which is computed over the entire alignment (regardless of activity) in addition to the already reported N-MIDE macro-averaged over activities (currently reported as "mean-n-mide")
  • The N-MIDE computation now ignores pairs where the reference instance has been reduced to a duration of zero due to the size of the no-score collar. The number of ignored pairs are reported as "n-mide_num_rejected"

March 27, 2018 - Version 0.1.1

  • Added object detection PMiss@RFA measures for the ActEV18_AOD protocol
  • Fixed object congruence calculation for ActEV18_AOD. Should be calculated as 1 - minMODE, rather than simply minMODE (NOTE this change affects alignment, and as a result your scores may have changed from the previous version)
  • For ActEV18_AOD, updated the default object congruence delta to be 0 instead of 1 to reflect the updated object congruence calculation
  • Fixed an issue where DET curve points with PMiss of 1 or 0 weren't being plotted. Note that these points will not be directly visible within the DET curve figures due to the y-axis scaling, but will connect to other points within the view

April 9, 2018 - Version 0.1.2

  • Fixed an issue where a reference activity instance spanning the entire duration of the source video would cause the N-MIDE computation to fail. These instances are now ignored for N-MIDE, and are included in the "n-mide_num_rejected" count - Added an optimization to the kernel builder function whereby unnecessary filter computations are skipped

April 23, 2018 - Version 0.2.0

  • Now using global range of "presenceConf" scores for detection congruence score component of alignment kernel for both ActEV18_AD and ActEV18_AOD
  • The "ActEV18_AOD" protocol can now accept an "objectTypeMap" for each activity in the provided activity index. Reference and system "objectType" strings are passed through the map (if provided) prior to alignment
  • Added additional columns to the "object_alignment.csv" output file, which specifies both the original "objectType" strings and re-mapped strings for both reference and system instances
  • The ActEV18_AOD protocol will now ignore any objects provided by the reference or system output with an "objectType" not included in the list of "objectTypes" for a given activity in the activity index file. If the "objectTypes" property is provided as an empty list, or is simply omitted, no such filtering takes place

April 25, 2018 - Version 0.2.1

  • Added an option (-V, --validation-only) where the system output file will be validated but not scored. With this option enabled, the reference file (-r), and output directory (-o) parameters are not required
  • Fixed an issue where the global range of "presenceConf" scores for object detections was being computed more often than necessary

April 27, 2018 - Version 0.2.2

  • Fixed a divide by zero issue when computing MODE for the ActEV_AOD object detection congruence kernel component. Specifically when there are no reference objects
  • Added "temporal_fa" and "temporal_miss" to the pair metrics output

May 16, 2018 - Version 0.3.0

  • Renamed existing scoring parameters to be less ambiguous. Added scoring parameters to control error weights for some metrics as well as the target rates of false alarm
  • Refactored portions of the alignment and metric computation code to improve performance

May 24, 2018 - Version 0.3.1

  • Now reporting a PMiss@RFA of 1.0 instead of None when there are no system reported instances
  • For the ActEV_AOD protocol, now reporting a mean object PMiss@RFA of 1.0 instead of None when there are no aligned activity instances

June 13, 2018 - Version 0.3.2

  • Added an option (-F, --ignore-extraneous-files) to ignore "filesProcessed" and reference and system localizations for files not included in the provided FILE_INDEX
  • Miscellaneous improvements

June 25, 2018 - Version 0.3.3

  • Added NMIDE at RFA measures for both ActEV_AD and ActEV_AOD protocols
  • Moved DET curve plot legend to the right of the plot

September 24, 2018 - Version 0.3.4

  • Added AODT Task
  • Added MOTE to output files
  • Added integration test 8_0

June 12, 2019

  • Added AUC at the various tfa and rfa thresholds

June 13, 2019

  • Add nAUC using nAUC@Xtfa = AUC@Xtfa / X

August 30, 2019

  • Added git.commit info to scoring parameters
  • Fixed tfa calculation
  • Added DMRender for graphing

September 5, 2019

  • Added -t option for det point resolution scores to use for processing
  • Added test 14-0

September 18, 2019

  • AUDC now calculated using dm files
  • Added tests 15-0, 15-1, 15-2, 15-3

December 16, 2019

  • Added Actev_SDL_V2 protocol
  • Added tests 11-4, 11-5

May 01, 2020 - Version 0.5.0

  • Updated to Python 3.7
  • Updated tests
    • Ignored output files are now *.png, *.dm & *.log
    • UNIX diff is no longer used during tests. A custom one is used due to the difference of floats precision between Python 2 and 3.
  • Updated README and made it more user-friendly, using MarkDown
  • Added install recipe for Makefile

June 17, 2020 - Version 0.5.1

  • Add parallelization

August 19, 2020 - Version 0.5.2

  • Add pruning option
  • Enhance get_y behavior

September 11, 2020 - Version 0.5.3

September 21, 2020 - Version 0.5.4

  • Added --ignore-no-score-regions option

October 16, 2020 - Version 0.5.5

  • Removed --no-ppf option as it is now the default behavior.
  • Add --plotting-parameters-file option.

November 9, 2020 - Version 0.5.6

  • Add --include-zero-ref-instances option for legacy purposes.

November 16, 2020 - Version 0.5.7

  • Add checks on processingReport

November 27, 2021 - Version 0.5.8

  • Added the suite of SRL_AD_V? and SRL_AOD_V? protocols
  • Update README

Contact

Please send any issues, questions, or comments to [email protected]

Authors

  • David Joy
  • Andrew Delgado
  • Baptiste Chocot
  • Jonathan Fiscus

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actev_scorer's Issues

ActEV_Scorer reports import errors if now protocol is listed

When you run the command below. The program should report a protocol is needed and exit rather than below.

% python ./ActEV_Scorer/ActEV_Scorer.py
Traceback (most recent call last): File "./ActEV_Scorer/ActEV_Scorer.py", line 467, in
args.func(args)
AttributeError: 'Namespace' object has no attribute 'func'

Why not changed the code to activity paralleled?

Hi, after reading the code, I found the scorer is not activity paralleled.

alignment = protocol.compute_alignment(system_activities, reference_activities)

So each activity of prediction needs to be matched with each activity of reference. However, that will bring redundant operation and large memory cost. Since the final nAUDC is activity separated, if you split the input prediction and reference to 37 files (each contains a certain type of action), it brings only 1/37 match operation and 1/37 memory usage.
In fact, according to our test, such operation will shorten the evaluation time from >1h to 5 min and won't influence the final results.

Failed to validate

Hello,
I've test my output format use training data.However,when I submit my result to NIST. I got error like this.

Failed validating u'type' in schema[u'properties'][u'activities'][u'items']:
{u'properties': {u'activity': {u'type': u'string'},
u'activityID': {u'type': u'integer'},
u'alertFrame': {u'type': u'integer'},
u'localization': {u'$ref': u'#/definitions/temporal_localization'},
u'presenceConf': {u'type': u'number'}},
u'required': [u'activity',
u'presenceConf',
u'activityID',
u'localization'],
u'type': u'object'}

Do you have any suggestions for this bug?

Thanks a lot for your time.

object_alignment.csv from AOD protocol does not have the correct format

All records are on the same line, not properly formatted (see test/data/checkfiles/test_4_0/object_alignment.csv)
Expected:

activity|ref_activity|sys_activity|frame|ref_object_type|sys_object_type|mapped_ref_object_type|mapped_sys_object_type|alignment|ref_object|sys_object|sys_presenceconf_score|kernel_similarity|kernel_components
Closing|1|1|3034|person|person|person|person|CD|1|1|0.45|1.0000004952941175|{"spatial_intersection-over-union": 1.0, "presenceconf_congruence": 0.4852941176470588}
Closing|1|1|3034|vehicle|vehicle|vehicle|vehicle|CD|2|2|0.8|1.0000010099999999|{"spatial_intersection-over-union": 1.0, "presenceconf_congruence": 1.0}

General plot options are not correctly checked

They are actually not checked at all. See function validate_options in file lib/ActEV_DMRender.py#151-194 :

try:
        # Handle plot options validation here
        if True:  <---------
            pass
        else:
            raise PlotOptionValidationError("Invalid stuff detected".format(plot_type))

    except PlotOptionValidationError as e:
        logging.error("PlotOptionValidationError: {}".format(e.msg))
        DMRenderExit(logger)

    except KeyError as e:
        logging.error("PlotOptionValidationError: no '{}' provided".format(e.args[0]))
        DMRenderExit(logger)

Failing to Validate for AOD

Hello,

I am on the UMD team. I've been having trouble getting AOD to work. I tried to verify a sample output file using the "-V" option, but I'm getting the following error message:

[Error] {u'5340': {u'boundingBox': {u'y': 124, u'h': 150, u'w': 295, u'x': 176}, u'presenceConf': 0.99}} does not have enough properties

Failed validating u'minProperties' in schema[u'properties'][u'activities'][u'items'][u'properties'][u'objects'][u'items'][u'properties'][u'localization'][u'patternProperties'][u'^[A-Za-z0-9_\\-\\.]+$']:
    {u'additionalProperties': False,
     u'minProperties': 2,
     u'patternProperties': {u'^[0-9]+$': {u'oneOf': [{u'properties': {u'boundingBox': {u'$ref': u'#/definitions/bounding_box'},
                                                                      u'presenceConf': {u'type': u'number'}},
                                                      u'required': [u'presenceConf',
                                                                    u'boundingBox']},
                                                     {u'maxProperties': 0}],
                                          u'type': u'object'}},
     u'type': u'object'}

On instance[u'activities'][0][u'objects'][0][u'localization'][u'VIRAT_S_000007.mp4']:
    {u'5340': {u'boundingBox': {u'h': 150,
                                u'w': 295,
                                u'x': 176,
                                u'y': 124},
               u'presenceConf': 0.99}}

I'm having trouble interpreting exactly what this message means. My object has the "boundingBox" and "presenceConf" fields in the format specified by the PDF.

Are there any suggestions for debugging this?

Thanks for your time,
Steve

Question about "mean-n-mide_num_rejected"

Hello,

I wanted to ask about the metric "n-mide_num_rejected."

I was wondering what this correlates to?

In addition, we ran a test where we fed ground truth data into the system, assigning a confidence of 1.0 to each activity. We observed that this gave us a "mean-n-mide_num_rejected" of 0.08333 and a "n-mide_num_rejected" of 1. Is this intended behaviour?

The ground truth file we used is attached. We can confirm that, except for the addition of the requisite "alertFrame" and "presenceConf" fields, it's identical to the ground truth.

Ground Truth "Output" file: output_gt.json.zip

Ground Truth: gt.json.zip

Thanks,
Steve Schwarcz

Scorer crashes instead of exiting when called without parameter or with -h

$ python3 ActEV_Scorer.py
Traceback (most recent call last):
  File "ActEV_Scorer.py", line 752, in <module>
    parser.parse_args(['-h'])
  File "/Users/bnc8/miniconda3/envs/ActEV_Scorer_py3.8/lib/python3.8/argparse.py", line 1768, in parse_args
    args, argv = self.parse_known_args(args, namespace)
  File "/Users/bnc8/miniconda3/envs/ActEV_Scorer_py3.8/lib/python3.8/argparse.py", line 1800, in parse_known_args
    namespace, args = self._parse_known_args(args, namespace)
  File "/Users/bnc8/miniconda3/envs/ActEV_Scorer_py3.8/lib/python3.8/argparse.py", line 2006, in _parse_known_args
    start_index = consume_optional(start_index)
  File "/Users/bnc8/miniconda3/envs/ActEV_Scorer_py3.8/lib/python3.8/argparse.py", line 1946, in consume_optional
    take_action(action, args, option_string)
  File "/Users/bnc8/miniconda3/envs/ActEV_Scorer_py3.8/lib/python3.8/argparse.py", line 1874, in take_action
    action(self, namespace, argument_values, option_string)
  File "/Users/bnc8/miniconda3/envs/ActEV_Scorer_py3.8/lib/python3.8/argparse.py", line 1044, in __call__
    parser.print_help()
  File "/Users/bnc8/miniconda3/envs/ActEV_Scorer_py3.8/lib/python3.8/argparse.py", line 2494, in print_help
    self._print_message(self.format_help(), file)
  File "/Users/bnc8/miniconda3/envs/ActEV_Scorer_py3.8/lib/python3.8/argparse.py", line 2478, in format_help
    return formatter.format_help()
  File "/Users/bnc8/miniconda3/envs/ActEV_Scorer_py3.8/lib/python3.8/argparse.py", line 282, in format_help
    help = self._root_section.format_help()
  File "/Users/bnc8/miniconda3/envs/ActEV_Scorer_py3.8/lib/python3.8/argparse.py", line 213, in format_help
    item_help = join([func(*args) for func, args in self.items])
  File "/Users/bnc8/miniconda3/envs/ActEV_Scorer_py3.8/lib/python3.8/argparse.py", line 213, in <listcomp>
    item_help = join([func(*args) for func, args in self.items])
  File "/Users/bnc8/miniconda3/envs/ActEV_Scorer_py3.8/lib/python3.8/argparse.py", line 213, in format_help
    item_help = join([func(*args) for func, args in self.items])
  File "/Users/bnc8/miniconda3/envs/ActEV_Scorer_py3.8/lib/python3.8/argparse.py", line 213, in <listcomp>
    item_help = join([func(*args) for func, args in self.items])
  File "/Users/bnc8/miniconda3/envs/ActEV_Scorer_py3.8/lib/python3.8/argparse.py", line 541, in _format_action
    parts.append(self._format_action(subaction))
  File "/Users/bnc8/miniconda3/envs/ActEV_Scorer_py3.8/lib/python3.8/argparse.py", line 529, in _format_action
    help_text = self._expand_help(action)
  File "/Users/bnc8/miniconda3/envs/ActEV_Scorer_py3.8/lib/python3.8/argparse.py", line 621, in _expand_help
    return self._get_help_string(action) % params
TypeError: %o format: an integer is required, not dict

what's the meaning of "metric" in pair_metrics.csv

We are from CMU team and we've successfully run the scoring software offline.
Now we want to debug and analyze the evaluation output.
In the document, it says that pair_metrics.csv contains metrics computed on the matched system/reference instance pairs.
What is the exact meaning of metrics?
It would be preferred if you could associate the explanation with the 4 steps from p14-p17 in evaluation plan pdf.
Thank you very much!

Quiet mode is not 100% quiet when used with option -t

$ python3 ActEV_Scorer.py ActEV_SDL_V2 \
        -o output \
        -s X \
        -r Y \
        -a activity-index.json \
        -f file-index.json \
        --ignore-missing-files -SdF -n 6 -t 1024
[Info] Reducing to 43373 unique  confidence scores for Sweeping to 1024 [0.9993730783462524,1.9568064089980908e-05] unique confidence scores
[Info] Reducing to 43221 unique  confidence scores for Sweeping to 1024 [0.9998784065246582,0.00020951910119038075] unique confidence scores
[Info] Reducing to 43190 unique  confidence scores for Sweeping to 1024 [0.6836076378822327,1.825257459131535e-05] unique confidence scores
[Info] Reducing to 43274 unique  confidence scores for Sweeping to 1024 [0.9995612502098083,0.00030237893224693835] unique confidence scores
[Info] Reducing to 43229 unique  confidence scores for Sweeping to 1024 [0.9969437718391418,7.618912059115246e-05] unique confidence scores
[Info] Reducing to 43244 unique  confidence scores for Sweeping to 1024 [0.9978759288787842,0.00012763180711772293] unique confidence scores
[Info] Reducing to 43255 unique  confidence scores for Sweeping to 1024 [0.9994791150093079,9.485345799475908e-05] unique confidence scores
[Info] Reducing to 43244 unique  confidence scores for Sweeping to 1024 [0.9978759288787842,0.00012763180711772293] unique confidence scores
[Info] Reducing to 43362 unique  confidence scores for Sweeping to 1024 [0.9969587326049805,6.664041575277224e-05] unique confidence scores
[Info] Reducing to 43355 unique  confidence scores for Sweeping to 1024 [0.9961994290351868,0.00010252583888359368] unique confidence scores
[Info] Reducing to 43380 unique  confidence scores for Sweeping to 1024 [0.9994567632675171,2.0765701265190728e-05] unique confidence scores
[Info] Reducing to 43229 unique  confidence scores for Sweeping to 1024 [0.9969437718391418,7.618912059115246e-05] unique confidence scores
[Info] Reducing to 43334 unique  confidence scores for Sweeping to 1024 [0.9992696046829224,1.255160714208614e-05] unique confidence scores
[Info] Reducing to 43287 unique  confidence scores for Sweeping to 1024 [0.9994294047355652,0.000500339490827173] unique confidence scores
[Info] Reducing to 43259 unique  confidence scores for Sweeping to 1024 [0.9999307990074158,4.158047886448912e-05] unique confidence scores
[Info] Reducing to 43276 unique  confidence scores for Sweeping to 1024 [0.995410144329071,5.028772193327313e-06] unique confidence scores
[Info] Reducing to 43270 unique  confidence scores for Sweeping to 1024 [0.9793859720230103,5.959395639365539e-05] unique confidence scores
[Info] Reducing to 42583 unique  confidence scores for Sweeping to 1024 [0.9999810457229614,4.7809175157453865e-05] unique confidence scores
[Info] Reducing to 43306 unique  confidence scores for Sweeping to 1024 [0.9937692880630493,8.089200855465606e-05] unique confidence scores
[Info] Reducing to 43266 unique  confidence scores for Sweeping to 1024 [0.9997636675834656,0.00012850938946940005] unique confidence scores
[Info] Reducing to 43233 unique  confidence scores for Sweeping to 1024 [0.9999585151672363,9.439724090043455e-05] unique confidence scores
[Info] Reducing to 43315 unique  confidence scores for Sweeping to 1024 [0.9972010850906372,5.8350829931441694e-05] unique confidence scores
[Info] Reducing to 43393 unique  confidence scores for Sweeping to 1024 [0.9967694878578186,7.481218926841393e-05] unique confidence scores
[Info] Reducing to 43359 unique  confidence scores for Sweeping to 1024 [0.9998538494110107,1.7420346921426244e-05] unique confidence scores
[Info] Reducing to 43264 unique  confidence scores for Sweeping to 1024 [0.9996740818023682,0.00010489957639947534] unique confidence scores
[Info] Reducing to 43346 unique  confidence scores for Sweeping to 1024 [0.9998893737792969,2.5475015718257055e-05] unique confidence scores
[Info] Reducing to 43380 unique  confidence scores for Sweeping to 1024 [0.9980849027633667,8.414820331381634e-05] unique confidence scores
[Info] Reducing to 43281 unique  confidence scores for Sweeping to 1024 [0.9994276165962219,6.999270408414304e-05] unique confidence scores
[Info] Reducing to 43269 unique  confidence scores for Sweeping to 1024 [0.9978819489479065,1.6150050214491785e-05] unique confidence scores
[Info] Reducing to 43205 unique  confidence scores for Sweeping to 1024 [0.9933363199234009,6.334586214506999e-05] unique confidence scores
[Info] Reducing to 43212 unique  confidence scores for Sweeping to 1024 [0.9998476505279541,0.00011497674131533131] unique confidence scores
[Info] Reducing to 43298 unique  confidence scores for Sweeping to 1024 [0.9978128671646118,3.8905574911041185e-05] unique confidence scores
[Info] Reducing to 43335 unique  confidence scores for Sweeping to 1024 [0.999390721321106,0.00015729911683592945] unique confidence scores
[Info] Reducing to 43258 unique  confidence scores for Sweeping to 1024 [0.9880858659744263,4.090479706064798e-05] unique confidence scores
[Info] Reducing to 43319 unique  confidence scores for Sweeping to 1024 [0.9969897270202637,3.9334820030489936e-05] unique confidence scores
[Info] Reducing to 43284 unique  confidence scores for Sweeping to 1024 [0.999835729598999,0.0001423127978341654] unique confidence scores
[Info] Reducing to 43284 unique  confidence scores for Sweeping to 1024 [0.9998292922973633,1.7123798897955567e-05] unique confidence scores```

multithreading?

The scorer takes hours to run if the test set is large. Is it possible to make the scorer multi-threaded? I presume that it should be possible since each video has it's own score.

Can not create TFA_activity.dm files

when I tried running the example_run.sh it generates this error messages.
[Errno 2] No such file or directory: 'example_run-output/dm/TFA_Closing.dm'
example_run-output/dm/TFA_Closing.dmDNE
[Errno 2] No such file or directory: 'example_run-output/dm/TFA_Closing_Trunk.dm'
example_run-output/dm/TFA_Closing_Trunk.dmDNE
[Errno 2] No such file or directory: 'example_run-output/dm/TFA_Entering.dm'

I am running it on windows.

Difference between mean-n-mide and n-mide?

We are from CMU team and we've successfully run the evaluation software offline.
In the evaluation plan pdf, there's detailed explanation of n-mide metrics.
But in the aggregated output of the software, we get mean-n-mide and n-mide.
What's the difference between these two metrics?
Thank you very much!

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