File size: 2,790 Bytes
4949db9 | 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 | import os
from pathlib import Path
BASE_DIR = Path(__file__).resolve().parent
N_ANNOTATORS_PER_VIDEO = 3
BATCH_SIZE_PER_USER = 4 # default fallback
# Per-cohort batch size (number of comparison pages per user)
COHORT_BATCH_SIZE: dict[str, int] = {
"cohort_a": 4,
"cohort_b": 11,
"cohort_c": 4,
"cohort_d": 4,
"test": 2, # Test cohort — excluded from dashboard/stats
"others": 100, # Default for unspecified cohort
}
# Expected headcount per cohort (for dashboard display)
COHORT_EXPECTED: dict[str, int] = {
"cohort_a": 285,
"cohort_b": 35,
"cohort_d": 80,
}
# Fixed completion codes per cohort (not randomly generated)
COHORT_COMPLETION_CODE: dict[str, str] = {
"cohort_c": "COMPLETION",
"cohort_d": "COMPLETION",
}
# Cohorts to exclude from dashboard statistics and exports
TEST_COHORTS = {"test"}
VALID_COHORTS = set(COHORT_BATCH_SIZE.keys())
def get_batch_size(cohort: str | None) -> int:
"""Return batch size for a given cohort, falling back to default."""
return COHORT_BATCH_SIZE.get(cohort or "others", BATCH_SIZE_PER_USER)
ASSIGNMENT_TTL_HOURS = 24
VIDEO_DATA_DIR = BASE_DIR / "../../data/videos"
DB_PATH = BASE_DIR / "human_eval.db"
TEST_DB_PATH = BASE_DIR / "human_eval_test.db"
DISAGREEMENT_TOP_K = 20
SECRET_KEY = os.environ.get("HUMAN_EVAL_SECRET_KEY", "human-eval-local-dev-key")
# Assignment status constants
STATUS_ASSIGNED = "assigned"
STATUS_COMPLETED = "completed"
STATUS_PARTIAL = "partial"
STATUS_SKIPPED = "skipped"
# ---------------------------------------------------------------------------
# Comparison mode: multi-model side-by-side evaluation
# ---------------------------------------------------------------------------
# Model prefixes — must match the prefix part of dataset directory names
# e.g. "ltx-2-19b-distilled-fp8" matches "ltx-2-19b-distilled-fp8-openvid", etc.
COMPARISON_MODELS = [
"wan2.2-ti2v-5b",
"ltx-2-19b-dev",
"cosmos-predict2.5-2b",
"cosmos-predict2.5-14b",
"veo-3.1",
"wan2.2-i2v-a14b",
"omniweaving",
"ltx-2.3-22b-dev",
]
# How many models to show per comparison group (randomly sampled from COMPARISON_MODELS)
MODELS_PER_GROUP = 3
SOURCE_DATASETS = {"wmb", "openvid", "physics_iq", "video_phy_2"}
# Redirect URL shown after user completes all assigned groups
COMPLETION_SURVEY_URL = "https://example.com/survey"
def extract_model(dataset_name: str) -> str:
"""Extract model prefix from a dataset directory name like 'ltx-2-19b-distilled-fp8-openvid' → 'ltx-2-19b-distilled-fp8'."""
for src in sorted(SOURCE_DATASETS, key=len, reverse=True):
suffix = f"-{src}"
if dataset_name.endswith(suffix):
return dataset_name[: -len(suffix)]
return dataset_name
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