[NO MERGE] Evaluate detections with map_samples

[minhtuev]

What changes are proposed in this pull request?

Changes to evaluate_detections with map_samples implementation and evaluation script

How is this patch tested? If it is not, please explain why.

• Run locally: example: python eval_detection_benchmark_script.py --workers 4,8

=== Performance Summary ===

Sorted by execution time:
configuration shard_method  num_workers   duration
     slice_8w        slice            8  37.121273
        id_8w           id            8  41.387008
     slice_4w        slice            4  45.902943
        id_4w           id            4  78.974790
     baseline         none            0 121.788566

Average duration by shard method:
shard_method
id        60.180899
none     121.788566
slice     41.512108
Name: duration, dtype: float64

Average duration by number of workers:
num_workers
0    121.788566
4     62.438866
8     39.254140
Name: duration, dtype: float64

Results saved to detection_eval_performance.csv

Release Notes

Is this a user-facing change that should be mentioned in the release notes?

• No. You can skip the rest of this section.
• Yes. Give a description of this change to be included in the release
  notes for FiftyOne users.

(Details in 1-2 sentences. You can just refer to another PR with a description
if this PR is part of a larger change.)

What areas of FiftyOne does this PR affect?

• App: FiftyOne application changes
• Build: Build and test infrastructure changes
• Core: Core fiftyone Python library changes
• Documentation: FiftyOne documentation changes
• Other

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[minhtuev]

Full run against bdd100k:

In general we do see an improvement as the number of workers increases, but it does not scale linearly in a noticeable manner

=== Performance Summary ===
Sorted by execution time:
configuration    shard_method    num_workers    duration
slice_16w        slice          16             44.374842
slice_8w         slice           8             45.477002
id_8w            id              8             50.465511
id_4w            id              4             51.208894
slice_4w         slice           4             57.493871
id_16w           id             16             57.690762
slice_2w         slice           2             77.059922
id_2w            id              2             77.097275
baseline         none            0            112.500139
id_1w            id              1            121.470436
slice_1w         slice           1            125.941550
Average duration by shard method:
shard_method    duration
id              71.586576
none           112.500139
slice           70.069437
Average duration by number of workers:
num_workers    duration
0             112.500139
1             123.705993
2              77.078599
4              54.351382
8              47.971256
16             51.032802

[kaixi-wang]

@minhtuev it looks like relevant tests are failing...

[minhtuev]

Thanks @kaixi-wang for spotting it, I missed some small bugs in refactoring evaluate_detections but it should only affect the single thread/process case, not the multiprocessing case :)

[minhtuev]

Updated runtime number, which shows that running without parallel processing is really slow:

=== Performance Summary ===
Sorted by execution time:
configuration shard_method  num_workers    duration
slice_16w    slice        16           119.791122
id_16w       id           16           129.170237
slice_8w     slice        8            212.950201
id_8w        id           8            220.344598
slice_4w     slice        4            335.403332
id_4w        id           4            370.033176
slice_2w     slice        2            604.814190
id_2w        id           2            659.206682
baseline     none         0            1051.305521
slice_1w     slice        1            1105.534975
id_1w        id           1            1227.948978

Average duration by shard method:
shard_method
slice      475.698764
id         521.340734
none       1051.305521

Average duration by number of workers:
num_workers
0  1051.305521
1  1166.741976
2  632.010436
4  352.718254
8  216.647400
16 124.480679