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Add perf benchmark test cases for action and visual prompting v1 #3292

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merged 7 commits into from
Apr 11, 2024
Merged

Add perf benchmark test cases for action and visual prompting v1 #3292

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5001f2a
Add action benchmark dummy
goodsong81 Mar 27, 2024
d69e0ff
Run command w/ subprocess.run() for better stability
goodsong81 Apr 1, 2024
e082669
Collect raw data to get seed info
goodsong81 Apr 1, 2024
1c3b16c
Fix model-category default to all
goodsong81 Apr 4, 2024
aec7d53
Add action perf test cases
goodsong81 Apr 5, 2024
7be3a63
Add visual prompting perf test cases
goodsong81 Apr 5, 2024
383e8ea
Fix pre-commit
goodsong81 Apr 5, 2024
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4 changes: 3 additions & 1 deletion .github/workflows/perf_benchmark.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@ on:
options:
- default # speed, balance, accuracy models only
- all # default + other models
default: default
default: all
data-group:
type: choice
description: Data group to run benchmark
Expand Down Expand Up @@ -98,6 +98,8 @@ jobs:
fail-fast: false
matrix:
include:
- task-short: "act"
task: "action"
- task-short: "ano"
task: "anomaly"
- task-short: "cls"
Expand Down
8 changes: 6 additions & 2 deletions tests/perf/benchmark.py
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Original file line number Diff line number Diff line change
Expand Up @@ -121,7 +121,7 @@ def load_result(result_path: str) -> pd.DataFrame | None:
"""
# Search csv files
if os.path.isdir(result_path):
csv_file_paths = glob.glob(f"{result_path}/**/exp_summary.csv", recursive=True)
csv_file_paths = glob.glob(f"{result_path}/**/all_exp_result.csv", recursive=True)
else:
csv_file_paths = [result_path]
results = []
Expand All @@ -142,7 +142,9 @@ def load_result(result_path: str) -> pd.DataFrame | None:

# Merge experiments
data = pd.concat(results, ignore_index=True)
data["train_e2e_time"] = pd.to_timedelta(data["train_e2e_time"]).dt.total_seconds() # H:M:S str -> seconds
if "train_e2e_time" in data:
data["train_e2e_time"] = pd.to_timedelta(data["train_e2e_time"]).dt.total_seconds() # H:M:S str -> seconds
data = data.rename(columns={"repeat": "seed"})
return data.set_index(["task", "model", "data_group", "data"])

@staticmethod
Expand Down Expand Up @@ -231,6 +233,8 @@ def _set_num_epoch(model_id: str, train_params: dict, num_epoch: int):
return # No configurable parameter for num_epoch
elif "stfpm" in model_id:
train_params["learning_parameters.max_epochs"] = num_epoch
elif "SAM" in model_id:
train_params["learning_parameters.trainer.max_epochs"] = num_epoch
else:
train_params["learning_parameters.num_iters"] = num_epoch

Expand Down
203 changes: 203 additions & 0 deletions tests/perf/test_action.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,203 @@
"""OTX Action perfomance tests."""

# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0


import pytest

from otx.cli.registry import Registry
from typing import Callable
from .benchmark import Benchmark


class TestPerfActionClassification:
"""Benchmark action classification."""

MODEL_TEMPLATES = Registry(f"src/otx/algorithms").filter(task_type="ACTION_CLASSIFICATION").templates
MODEL_IDS = [template.model_template_id for template in MODEL_TEMPLATES]

BENCHMARK_CONFIGS = {
"small": {
"tags": {
"task": "action_classification",
},
"datasets": [
"action/action_classification/ucf_cvat_5percent",
],
"num_repeat": 5,
"num_epoch": 10,
},
"medium": {
"tags": {
"task": "action_classification",
},
"datasets": [
"action/action_classification/ucf_cvat_30percent",
],
"num_repeat": 5,
"num_epoch": 10,
},
"large": {
"tags": {
"task": "action_classification",
},
"datasets": [
"action/action_classification/ucf_cvat",
],
"num_repeat": 5,
"num_epoch": 3,
},
}

@pytest.mark.parametrize("fxt_model_id", MODEL_TEMPLATES, ids=MODEL_IDS, indirect=True)
@pytest.mark.parametrize("fxt_benchmark", BENCHMARK_CONFIGS.items(), ids=BENCHMARK_CONFIGS.keys(), indirect=True)
def test_perf(self, fxt_model_id: str, fxt_benchmark: Benchmark):
"""Benchmark performance metrics."""
result = fxt_benchmark.run(model_id=fxt_model_id)
fxt_benchmark.check(
result,
criteria=[
{
"name": "Accuracy(train)",
"op": ">",
"margin": 0.1,
},
{
"name": "Accuracy(export)",
"op": ">",
"margin": 0.1,
},
{
"name": "Accuracy(optimize)",
"op": ">",
"margin": 0.1,
},
{
"name": "epoch",
"op": "<",
"margin": 0.1,
},
{
"name": "train_e2e_time",
"op": "<",
"margin": 0.1,
},
{
"name": "avg_data_time",
"op": "<",
"margin": 0.1,
},
{
"name": "avg_iter_time",
"op": "<",
"margin": 0.1,
},
{
"name": "avg_time_per_image(export)",
"op": "<",
"margin": 0.1,
},
{
"name": "avg_time_per_image(optimize)",
"op": "<",
"margin": 0.1,
},
],
)


class TestPerfActionDetection:
"""Benchmark action detection."""

MODEL_TEMPLATES = Registry(f"src/otx/algorithms").filter(task_type="ACTION_DETECTION").templates
MODEL_IDS = [template.model_template_id for template in MODEL_TEMPLATES]

BENCHMARK_CONFIGS = {
"small": {
"tags": {
"task": "action_detection",
},
"datasets": [
"action/action_detection/UCF101_cvat_5percent",
],
"num_repeat": 5,
"num_epoch": 3,
},
"medium": {
"tags": {
"task": "action_detection",
},
"datasets": [
"action/action_detection/UCF101_cvat_30percent",
],
"num_repeat": 5,
"num_epoch": 3,
},
"large": {
"tags": {
"task": "action_detection",
},
"datasets": [
"action/action_detection/UCF101_cvat",
],
"num_repeat": 5,
"num_epoch": 1,
},
}

@pytest.mark.parametrize("fxt_model_id", MODEL_TEMPLATES, ids=MODEL_IDS, indirect=True)
@pytest.mark.parametrize("fxt_benchmark", BENCHMARK_CONFIGS.items(), ids=BENCHMARK_CONFIGS.keys(), indirect=True)
def test_perf(self, fxt_model_id: str, fxt_benchmark: Benchmark):
"""Benchmark performance metrics."""
result = fxt_benchmark.run(model_id=fxt_model_id)
fxt_benchmark.check(
result,
criteria=[
{
"name": "f-measure(train)",
"op": ">",
"margin": 0.1,
},
{
"name": "epoch",
"op": "<",
"margin": 0.1,
},
{
"name": "f-measure(export)",
"op": ">",
"margin": 0.1,
},
{
"name": "f-measure(optimize)",
"op": ">",
"margin": 0.1,
},
{
"name": "train_e2e_time",
"op": "<",
"margin": 0.1,
},
{
"name": "avg_data_time",
"op": "<",
"margin": 0.1,
},
{
"name": "avg_iter_time",
"op": "<",
"margin": 0.1,
},
{
"name": "avg_time_per_image(export)",
"op": "<",
"margin": 0.1,
},
{
"name": "avg_time_per_image(optimize)",
"op": "<",
"margin": 0.1,
},
],
)
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