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Add Semi-SL for cls Benchmark Test #3647

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Jun 20, 2024
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Add Semi-SL for cls Benchmark Test #3647

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91357b3
Add Semi-SL Benchmark
harimkang Jun 20, 2024
9d49c7d
Fix docstring
harimkang Jun 20, 2024
5ffc780
Add tv models
harimkang Jun 20, 2024
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8 changes: 8 additions & 0 deletions tests/perf/benchmark.py
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Original file line number Diff line number Diff line change
Expand Up @@ -70,6 +70,7 @@ class Dataset:
group: str
num_repeat: int = 1
extra_overrides: dict | None = None
unlabeled_data_path: Path | None = None

@dataclass
class Criterion:
Expand Down Expand Up @@ -192,6 +193,13 @@ def run(
"--engine.device",
self.accelerator,
]

# Add unlabeled data path if exists
if dataset.unlabeled_data_path is not None:
command.extend(
["--data.config.unlabeled_subset.data_root", str(self.data_root / dataset.unlabeled_data_path)],
)

for key, value in dataset.extra_overrides.get("train", {}).items():
command.append(f"--{key}")
command.append(str(value))
Expand Down
121 changes: 121 additions & 0 deletions tests/perf/test_classification.py
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Expand Up @@ -22,6 +22,9 @@ class TestPerfSingleLabelClassification(PerfTestBase):
Benchmark.Model(task="classification/multi_class_cls", name="mobilenet_v3_large", category="accuracy"),
Benchmark.Model(task="classification/multi_class_cls", name="deit_tiny", category="other"),
Benchmark.Model(task="classification/multi_class_cls", name="dino_v2", category="other"),
Benchmark.Model(task="classification/multi_class_cls", name="tv_efficientnet_b3", category="other"),
Benchmark.Model(task="classification/multi_class_cls", name="tv_efficientnet_v2_l", category="other"),
Benchmark.Model(task="classification/multi_class_cls", name="tv_mobilenet_v3_small", category="other"),
]

DATASET_TEST_CASES = [
Expand Down Expand Up @@ -258,3 +261,121 @@ def test_perf(
criteria=self.BENCHMARK_CRITERIA,
resume_from=fxt_resume_from,
)


class TestPerfSemiSLMultiClass(PerfTestBase):
"""Benchmark single-label classification for Semi-SL task."""

MODEL_TEST_CASES = [ # noqa: RUF012
Benchmark.Model(task="classification/multi_class_cls", name="efficientnet_b0_semisl", category="speed"),
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Benchmark.Model(task="classification/multi_class_cls", name="mobilenet_v3_large_semisl", category="speed"),
Benchmark.Model(task="classification/multi_class_cls", name="efficientnet_v2_semisl", category="accuracy"),
Benchmark.Model(task="classification/multi_class_cls", name="deit_tiny_semisl", category="other"),
Benchmark.Model(task="classification/multi_class_cls", name="dino_v2_semisl", category="other"),
Benchmark.Model(task="classification/multi_class_cls", name="tv_efficientnet_b3_semisl", category="other"),
Benchmark.Model(task="classification/multi_class_cls", name="tv_efficientnet_v2_l_semisl", category="other"),
Benchmark.Model(task="classification/multi_class_cls", name="tv_mobilenet_v3_small_semisl", category="other"),
]

DATASET_TEST_CASES = (
[
Benchmark.Dataset(
name=f"cifar10@{num_label}_{idx}",
path=Path(f"multiclass_classification/semi-sl/cifar10@{num_label}_{idx}/supervised"),
group="cifar10",
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num_repeat=1,
unlabeled_data_path=Path(f"multiclass_classification/semi-sl/cifar10@{num_label}_{idx}/unlabel_data"),
extra_overrides={
"train": {
"data.config.train_subset.subset_name": "train_data",
"data.config.val_subset.subset_name": "val_data",
"data.config.test_subset.subset_name": "val_data",
"deterministic": "True",
},
},
)
for idx in (0, 1, 2)
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for num_label in (4, 10, 25)
]
+ [
Benchmark.Dataset(
name=f"svhn@{num_label}_{idx}",
path=Path(f"multiclass_classification/semi-sl/svhn@{num_label}_{idx}/supervised"),
group="svhn",
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num_repeat=1,
unlabeled_data_path=Path(f"multiclass_classification/semi-sl/svhn@{num_label}_{idx}/unlabel_data"),
extra_overrides={
"train": {
"data.config.train_subset.subset_name": "train_data",
"data.config.val_subset.subset_name": "val_data",
"data.config.test_subset.subset_name": "val_data",
"deterministic": "True",
},
},
)
for idx in (0, 1, 2)
for num_label in (4, 10, 25)
]
+ [
Benchmark.Dataset(
name=f"fmnist@{num_label}_{idx}",
path=Path(f"multiclass_classification/semi-sl/fmnist@{num_label}_{idx}/supervised"),
group="fmnist",
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num_repeat=1,
unlabeled_data_path=Path(f"multiclass_classification/semi-sl/fmnist@{num_label}_{idx}/unlabel_data"),
extra_overrides={
"train": {
"data.config.train_subset.subset_name": "train_data",
"data.config.val_subset.subset_name": "val_data",
"data.config.test_subset.subset_name": "val_data",
"deterministic": "True",
},
},
)
for idx in (0, 1, 2)
for num_label in (4, 10, 25)
]
)

BENCHMARK_CRITERIA = [ # noqa: RUF012
Benchmark.Criterion(name="train/epoch", summary="max", compare="<", margin=0.1),
Benchmark.Criterion(name="train/e2e_time", summary="max", compare="<", margin=0.1),
Benchmark.Criterion(name="val/accuracy", summary="max", compare=">", margin=0.1),
Benchmark.Criterion(name="test/accuracy", summary="max", compare=">", margin=0.1),
Benchmark.Criterion(name="export/accuracy", summary="max", compare=">", margin=0.1),
Benchmark.Criterion(name="optimize/accuracy", summary="max", compare=">", margin=0.1),
Benchmark.Criterion(name="train/iter_time", summary="mean", compare="<", margin=0.1),
Benchmark.Criterion(name="test/iter_time", summary="mean", compare="<", margin=0.1),
Benchmark.Criterion(name="export/iter_time", summary="mean", compare="<", margin=0.1),
Benchmark.Criterion(name="optimize/iter_time", summary="mean", compare="<", margin=0.1),
Benchmark.Criterion(name="test(train)/e2e_time", summary="max", compare=">", margin=0.1),
Benchmark.Criterion(name="test(export)/e2e_time", summary="max", compare=">", margin=0.1),
Benchmark.Criterion(name="test(optimize)/e2e_time", summary="max", compare=">", margin=0.1),
]

@pytest.mark.parametrize(
"fxt_model",
MODEL_TEST_CASES,
ids=lambda model: model.name,
indirect=True,
)
@pytest.mark.parametrize(
"fxt_dataset",
DATASET_TEST_CASES,
ids=lambda dataset: dataset.name,
indirect=True,
)
def test_perf(
self,
fxt_model: Benchmark.Model,
fxt_dataset: Benchmark.Dataset,
fxt_benchmark: Benchmark,
fxt_resume_from: Path | None,
):
self._test_perf(
model=fxt_model,
dataset=fxt_dataset,
benchmark=fxt_benchmark,
criteria=self.BENCHMARK_CRITERIA,
resume_from=fxt_resume_from,
)
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