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https://github.com/SikongJueluo/Mini-Nav.git
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SikongJueluo
9eb52f8cef
- Evaluate multiple datasets in a single run (CIFAR10 + CIFAR100) - Report Recall@K for a list of K values from one underlying search - Save results to disk: summary.md, metrics.csv, per-dataset predictions.csv, confusion_matrix.csv - Richer evaluation output: query_labels, topk_ids, topk_labels for downstream analysis - Add --dataset and --top-k CLI overrides for benchmark command - Update config schema: dataset→datasets, top_k→top_k_list
456 lines
15 KiB
Python
456 lines
15 KiB
Python
"""Benchmark runner for executing evaluations."""
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import csv
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import json
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Callable, cast
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import lancedb
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from benchmarks.datasets import HuggingFaceDataset, LocalDataset
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from benchmarks.tasks import get_task
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from configs.models import BenchmarkConfig, DatasetSourceConfig, ModelConfig
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from rich.console import Console
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from rich.table import Table
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console = Console()
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def _create_task(config: BenchmarkConfig, model_config: ModelConfig | None) -> Any:
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"""Create benchmark task with task-specific model settings.
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Args:
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config: Benchmark configuration.
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model_config: Optional model configuration for task-owned loading.
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Returns:
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Benchmark task instance.
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"""
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task_kwargs: dict[str, Any] = {"top_k_list": config.task.top_k_list}
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if config.task.type == "retrieval" and model_config is not None:
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task_kwargs.update(
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{
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"dino_model": model_config.dino_model,
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"compression_dim": model_config.compression_dim,
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"compressor_path": model_config.compressor_path,
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}
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)
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return get_task(config.task.type, **task_kwargs)
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def create_dataset(config: DatasetSourceConfig) -> Any:
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"""Create a dataset instance from configuration.
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Args:
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config: Dataset source configuration.
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Returns:
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Dataset instance.
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Raises:
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ValueError: If source_type is not supported.
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"""
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if config.source_type == "huggingface":
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return HuggingFaceDataset(
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hf_id=config.path,
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img_column=config.img_column,
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label_column=config.label_column,
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)
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elif config.source_type == "local":
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return LocalDataset(
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local_path=config.path,
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img_column=config.img_column,
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label_column=config.label_column,
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)
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else:
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raise ValueError(
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f"Unsupported source_type: {config.source_type}. "
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f"Supported types: 'huggingface', 'local'"
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)
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def _get_table_name(
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config: BenchmarkConfig,
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model_name: str,
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dataset_path: str,
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) -> str:
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"""Generate database table name from config, model, and dataset.
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Args:
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config: Benchmark configuration.
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model_name: Model name for table naming.
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dataset_path: Dataset identifier or path (e.g. "uoft-cs/cifar100").
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Returns:
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Formatted table name.
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"""
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prefix = config.model_table_prefix
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dataset_name = Path(dataset_path).name.lower().replace("-", "_")
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return f"{prefix}_{dataset_name}_{model_name}"
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def _ensure_table(
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config: BenchmarkConfig,
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model_name: str,
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vector_dim: int,
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dataset_path: str,
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) -> lancedb.table.Table:
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"""Ensure the LanceDB table exists with correct schema.
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Args:
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config: Benchmark configuration.
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model_name: Model name for table naming.
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vector_dim: Feature vector dimension.
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dataset_path: Dataset identifier or path.
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Returns:
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LanceDB table instance.
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"""
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import pyarrow as pa
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from database import db_manager
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table_name = _get_table_name(config, model_name, dataset_path)
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# Build expected schema
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schema = pa.schema(
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[
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pa.field("id", pa.int32()),
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pa.field("label", pa.int32()),
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pa.field("vector", pa.list_(pa.float32(), vector_dim)),
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]
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)
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db = db_manager.db
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existing_tables = db.list_tables().tables
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# Check if table exists and has correct schema
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if table_name in existing_tables:
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table = db.open_table(table_name)
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if table.schema != schema:
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console.print(
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f"[yellow]Table '{table_name}' schema mismatch, rebuilding.[/yellow]"
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)
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db.drop_table(table_name)
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table = db.create_table(table_name, schema=schema)
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else:
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table = db.create_table(table_name, schema=schema)
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return table
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def _print_benchmark_info(
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config: BenchmarkConfig,
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vector_dim: int,
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table_name: str,
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table_count: int,
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dataset_path: str = "",
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) -> None:
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"""Print benchmark configuration info using Rich table.
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Args:
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config: Benchmark configuration.
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vector_dim: Feature vector dimension.
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table_name: Database table name.
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table_count: Number of entries in the table.
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dataset_path: Current dataset identifier or path.
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"""
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table = Table(title="Benchmark Configuration", show_header=False)
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table.add_column("Key", style="cyan", no_wrap=True)
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table.add_column("Value", style="magenta")
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table.add_row("Dataset", dataset_path)
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table.add_row("Model Output Dimension", str(vector_dim))
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table.add_row("Table Name", table_name)
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table.add_row("Table Entries", str(table_count))
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console.print(table)
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def _print_benchmark_results(results: dict[str, Any]) -> None:
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"""Print benchmark results using Rich table.
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Args:
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results: Final benchmark metrics for a single dataset.
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"""
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table = Table(title="Benchmark Results", show_header=False)
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table.add_column("Metric", style="cyan", no_wrap=True)
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table.add_column("Value", style="green")
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# Print recalls first
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recalls = results.get("recalls", {})
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for k_str, v in recalls.items():
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if isinstance(v, float):
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table.add_row(k_str, f"{v:.4f}")
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for key, value in results.items():
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if key == "recalls":
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continue
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if isinstance(value, (list, dict)):
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continue
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if isinstance(value, float):
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table.add_row(key, f"{value:.4f}")
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continue
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table.add_row(key, str(value))
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console.print(table)
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def _save_benchmark_outputs(
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all_results: dict[str, dict[str, Any]],
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output_root: Path,
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model_name: str,
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) -> Path:
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"""Save multi-dataset benchmark results to disk.
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Writes to ``output_root/{run_id}/``:
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- summary.md — overall summary with per-dataset recall table
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- metrics.csv — one row per (dataset, K) combination
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- {dataset_name}/predictions.csv — per-sample predictions
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- {dataset_name}/confusion_matrix.csv — row-normalized (Top-1)
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Args:
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all_results: Dict mapping dataset name to evaluate() result dict.
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output_root: Parent directory (e.g. ``outputs/benchmark``).
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model_name: Model identifier for the run_id.
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Returns:
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Path to the created run directory.
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"""
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run_id = f"{datetime.now().strftime('%Y-%m-%d-%H%M%S')}-{model_name}"
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out_dir = output_root / run_id
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out_dir.mkdir(parents=True, exist_ok=True)
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# --- metrics.csv: one row per (dataset, k) ---
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csv_keys = ["model", "run_id", "dataset", "k", "recall@k", "total", "num_classes"]
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all_csv_rows: list[dict[str, Any]] = []
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# --- summary.md lines ---
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md_lines = [
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"# Benchmark Results",
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"",
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f"- **run_id**: `{run_id}`",
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f"- **model**: `{model_name}`",
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"",
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"## Per-Dataset Recall",
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"",
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]
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for dataset_name, results in all_results.items():
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recalls = results.get("recalls", {})
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total = results.get("total", 0)
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num_classes = results.get("num_classes", 0)
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md_lines.append(f"### {dataset_name}")
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md_lines.append("")
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md_lines.append(f"- total queries: `{total}`")
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md_lines.append(f"- num_classes: `{num_classes}`")
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md_lines.append("")
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md_lines.append("| K | Recall@K |")
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md_lines.append("|---:|---:|")
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for k_str, recall_val in sorted(recalls.items()):
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k = int(k_str.replace("recall@", ""))
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md_lines.append(f"| {k} | {recall_val:.4f} |")
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all_csv_rows.append({
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"model": model_name,
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"run_id": run_id,
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"dataset": dataset_name,
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"k": k,
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"recall@k": recall_val,
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"total": total,
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"num_classes": num_classes,
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})
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md_lines.append("")
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# Per-dataset subdirectory
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dataset_dir = out_dir / dataset_name
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dataset_dir.mkdir(parents=True, exist_ok=True)
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# --- predictions.csv (per-sample) ---
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query_labels = results.get("query_labels", [])
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topk_labels = results.get("topk_labels", [])
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if query_labels and topk_labels:
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max_k = max(len(preds) for preds in topk_labels) if topk_labels else 1
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fieldnames = ["idx", "true_label"] + [
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f"top{i+1}_label" for i in range(max_k)
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]
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with (dataset_dir / "predictions.csv").open(
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"w", newline="", encoding="utf-8"
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) as f:
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writer = csv.DictWriter(f, fieldnames=fieldnames)
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writer.writeheader()
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for idx, (true_label, preds) in enumerate(
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zip(query_labels, topk_labels)
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):
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row_data: dict[str, Any] = {
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"idx": idx,
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"true_label": true_label,
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}
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for i in range(max_k):
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row_data[f"top{i+1}_label"] = (
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preds[i] if i < len(preds) else ""
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)
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writer.writerow(row_data)
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# --- confusion_matrix.csv ---
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cm = results.get("confusion_matrix")
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if cm is not None:
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with (dataset_dir / "confusion_matrix.csv").open(
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"w", newline="", encoding="utf-8"
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) as fh:
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writer = csv.writer(fh)
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for row in cm:
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writer.writerow([f"{v:.6f}" for v in row])
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# Write metrics.csv
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if all_csv_rows:
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with (out_dir / "metrics.csv").open("w", newline="", encoding="utf-8") as f:
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writer = csv.DictWriter(f, fieldnames=csv_keys)
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writer.writeheader()
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for row in all_csv_rows:
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writer.writerow(row)
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# Write summary.md
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(out_dir / "summary.md").write_text("\n".join(md_lines) + "\n", encoding="utf-8")
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console.print(f"[green]Results saved to:[/green] {out_dir}")
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return out_dir
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def run_benchmark(
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model: Any,
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processor: Any,
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config: BenchmarkConfig,
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model_config: ModelConfig | None = None,
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model_name: str = "model",
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output_root: Path | None = None,
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) -> dict[str, Any]:
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"""Run benchmark evaluation across one or more datasets.
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Workflow:
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1. Load model / processor once
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2. For each dataset in config.datasets:
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a. Create dataset, get splits
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b. Build evaluation database
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c. Evaluate with all K values in top_k_list
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3. Print per-dataset results
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4. Save all results to disk (if ``output_root`` provided)
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Args:
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model: Feature extraction model.
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processor: Image preprocessor.
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config: Benchmark configuration.
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model_config: Optional model configuration for task-owned loading.
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model_name: Model name for table naming.
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output_root: Optional root directory for saving results.
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Returns:
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Dict mapping dataset name to evaluation result dict.
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Raises:
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ValueError: If benchmark is not enabled in config.
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"""
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# ── Resolve model / processor once ──────────────────────────
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task = _create_task(config, model_config)
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resolver = getattr(task, "prepare_benchmark", None)
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if callable(resolver):
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prepare_benchmark = cast(
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Callable[[Any, Any, str], tuple[Any, Any, str]],
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resolver,
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)
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model, processor, model_name = prepare_benchmark(model, processor, model_name)
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if model is None or processor is None:
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raise ValueError("Benchmark task did not provide a valid model and processor")
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# ── Infer vector dimension from first dataset ─────────────────
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first_cfg = config.datasets[0]
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first_dataset = create_dataset(first_cfg)
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first_train = first_dataset.get_train_split()
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sample = first_train[0]
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sample_image = sample["img"]
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from utils.feature_extractor import infer_vector_dim
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vector_dim = infer_vector_dim(processor, model, sample_image)
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console.print(f"[cyan]Model output dimension:[/cyan] {vector_dim}")
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# ── Evaluate each dataset ────────────────────────────────────
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all_results: dict[str, dict[str, Any]] = {}
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for dataset_cfg in config.datasets:
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console.print(
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f"\n[bold cyan]Dataset:[/bold cyan] "
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f"{dataset_cfg.source_type} - {dataset_cfg.path}"
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)
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dataset = create_dataset(dataset_cfg)
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train_dataset = dataset.get_train_split()
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test_dataset = dataset.get_test_split()
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if train_dataset is None or test_dataset is None:
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raise ValueError(
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f"Dataset {dataset_cfg.path} does not have train/test splits"
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)
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dataset_name = Path(dataset_cfg.path).name.lower()
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table = _ensure_table(config, model_name, vector_dim, dataset_cfg.path)
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table_name = _get_table_name(config, model_name, dataset_cfg.path)
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# Build database (skip if cached)
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table_count = table.count_rows()
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expected_count = len(train_dataset)
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if table_count == expected_count:
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console.print(
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f"[yellow]Table '{table_name}' already has {table_count} entries, "
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f"skipping database build.[/yellow]"
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)
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else:
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if table_count > 0:
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console.print(
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f"[yellow]Table '{table_name}' has {table_count} entries "
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f"(expected {expected_count}), rebuilding.[/yellow]"
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)
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# Rebuild: drop and recreate
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from database import db_manager
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db_manager.db.drop_table(table_name)
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table = _ensure_table(config, model_name, vector_dim, dataset_cfg.path)
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console.print(
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f"[cyan]Building database[/cyan] with {expected_count} training samples..."
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)
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task.build_database(
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model, processor, train_dataset, table, config.batch_size,
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label_column=dataset_cfg.label_column,
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)
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table_count = table.count_rows()
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_print_benchmark_info(
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config, vector_dim, table_name, table_count,
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dataset_path=dataset_cfg.path,
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)
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# Evaluate
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console.print(
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f"[cyan]Evaluating[/cyan] on {len(test_dataset)} test samples..."
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)
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results = task.evaluate(
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model, processor, test_dataset, table, config.batch_size,
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label_column=dataset_cfg.label_column,
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)
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all_results[dataset_name] = results
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# Print per-dataset results
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_print_benchmark_results(results)
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# ── Save outputs ─────────────────────────────────────────────
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if output_root is not None:
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_save_benchmark_outputs(all_results, output_root, model_name)
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return all_results
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