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ab318c0 d547fdb ab318c0 7d7a621 ab318c0 d547fdb ab318c0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 | from __future__ import annotations
import base64
import csv
import json
from dataclasses import dataclass
from datetime import datetime, timezone
from pathlib import Path
from typing import Iterable
from urllib.parse import urlparse
import httpx
@dataclass(frozen=True)
class EvalConfig:
api: str
images: list[Path]
domain_top_n: int
top_k: int
out_dir: Path
summary: bool
def select_hits(
hits: list[dict],
*,
max_n: int | None = None,
min_score: float | None = None,
) -> list[str]:
out: list[str] = []
for hit in hits:
if min_score is not None:
try:
if float(hit.get("score", 0.0)) < min_score:
continue
except Exception:
continue
out.append(str(hit.get("id")))
if max_n is not None and len(out) >= max_n:
break
return out
def iter_images(paths: Iterable[Path]) -> Iterable[Path]:
exts = {".jpg", ".jpeg", ".png", ".webp"}
for path in paths:
if path.is_dir():
for p in sorted(path.rglob("*")):
if p.is_file() and p.suffix.lower() in exts:
yield p
elif path.is_file() and path.suffix.lower() in exts:
yield path
def upload_label_set(client: httpx.Client, label_set: Path) -> str:
payload = json.loads(label_set.read_text())
r = client.post("/api/v1/label-sets", json=payload)
r.raise_for_status()
return r.json()["label_set_hash"]
def classify_one(
client: httpx.Client,
label_set_hash: str,
image_b64: str,
domain_top_n: int,
top_k: int,
) -> dict:
payload = {
"image_base64": image_b64,
"domain_top_n": domain_top_n,
"top_k": top_k,
}
r = client.post(f"/api/v1/classify?label_set_hash={label_set_hash}", json=payload)
r.raise_for_status()
return r.json()
def encode_image_b64(path: Path) -> str:
return base64.b64encode(path.read_bytes()).decode("utf-8")
def fmt_hit(hit: dict) -> str:
score = hit.get("score")
try:
score_str = f"{float(score):.4f}"
except Exception:
score_str = ""
return f"{hit.get('id')}:{score_str}"
def percentile(values: list[int], q: float) -> int:
if not values:
return 0
values = sorted(values)
idx = int(round((len(values) - 1) * q))
return values[idx]
def timestamp() -> str:
return datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
def api_slug(api: str) -> str:
parsed = urlparse(api)
host = parsed.netloc or parsed.path
host = host.replace("http://", "").replace("https://", "")
host = host.strip("/")
if host in {"localhost:7860", "localhost", "127.0.0.1:7860", "127.0.0.1"}:
return "local"
return "".join(ch if ch.isalnum() or ch in {"-", "."} else "-" for ch in host)
def resolve_out_dir(api: str, out_dir: Path | None) -> Path:
if out_dir is not None:
return out_dir
return Path("data_results") / api_slug(api)
def write_csv(path: Path, rows: list[dict[str, str]], fieldnames: list[str]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("w", newline="", encoding="utf-8") as f:
writer = csv.DictWriter(f, fieldnames=fieldnames)
writer.writeheader()
writer.writerows(rows)
def summarize_latency(rows: list[dict[str, str]]) -> dict[str, str]:
times: list[int] = []
for row in rows:
try:
times.append(int(row["elapsed_ms"]))
except Exception:
continue
return {
"count": str(len(times)),
"avg_elapsed_ms": str(int(sum(times) / max(1, len(times)))),
"p50_elapsed_ms": str(percentile(times, 0.50)),
"p90_elapsed_ms": str(percentile(times, 0.90)),
"p95_elapsed_ms": str(percentile(times, 0.95)),
"p99_elapsed_ms": str(percentile(times, 0.99)),
}
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