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7330a0e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | from __future__ import annotations
import json
from bridgelink_asl.clip_dataset import load_clip_dataset
from bridgelink_asl.asl_types import GestureWindow, SentenceEvent
from bridgelink_asl.wrapper import LocalQwen25VlmInterpreter, run_wrapper
def test_compare_mode_logs_cnn_and_vlm_predictions(tmp_path) -> None:
manifest_path = tmp_path / "clips.jsonl"
output_path = tmp_path / "comparison-results.jsonl"
manifest_path.write_text(
json.dumps(
{
"clip_id": "team_hello_want_drink_001",
"split": "test",
"source": "team",
"gloss": ["HELLO", "WANT", "DRINK"],
"english": "Hello, I want a drink.",
}
)
+ "\n",
encoding="utf-8",
)
summary = run_wrapper(manifest_path, mode="compare", output_path=output_path)
rows = [json.loads(line) for line in output_path.read_text(encoding="utf-8").splitlines() if line.strip()]
assert summary.records_processed == 1
assert summary.failures == 0
assert rows[0]["clip_id"] == "team_hello_want_drink_001"
assert rows[0]["cnn_prediction"]["model_mode"] == "cnn"
assert rows[0]["vlm_prediction"]["model_mode"] == "vlm"
assert "want" in rows[0]["cnn_prediction"]["sentence"].lower()
assert rows[0]["vlm_prediction"]["sentence"] == "Hello, I want a drink."
assert isinstance(rows[0]["cnn_latency_ms"], float)
assert isinstance(rows[0]["vlm_latency_ms"], float)
def test_vlm_mode_falls_back_to_gloss_when_confidence_is_low(tmp_path) -> None:
manifest_path = tmp_path / "clips.jsonl"
output_path = tmp_path / "comparison-results.jsonl"
manifest_path.write_text(
json.dumps(
{
"clip_id": "team_no_stop_001",
"split": "test",
"source": "team",
"gloss": ["NO", "STOP"],
"english": "No, stop.",
}
)
+ "\n",
encoding="utf-8",
)
class LowConfidenceInterpreter:
def interpret(self, window):
return SentenceEvent(
gloss=tuple(token.label for token in window.token_trace),
sentence="I am not sure.",
confidence=0.2,
model_mode="vlm",
)
summary = run_wrapper(
manifest_path,
mode="vlm",
output_path=output_path,
interpreter=LowConfidenceInterpreter(),
vlm_confidence_floor=0.6,
)
row = json.loads(output_path.read_text(encoding="utf-8").strip())
assert summary.failures == 1
assert row["cnn_prediction"] is None
assert row["vlm_prediction"]["sentence"] == "No stop."
assert row["vlm_prediction"]["needs_clarification"] is True
assert any("gloss fallback" in note for note in row["failure_notes"])
def test_cnn_mode_only_writes_cnn_fields(tmp_path) -> None:
manifest_path = tmp_path / "clips.jsonl"
output_path = tmp_path / "comparison-results.jsonl"
manifest_path.write_text(
json.dumps(
{
"clip_id": "team_please_help_001",
"split": "test",
"source": "team",
"gloss": ["PLEASE", "HELP"],
"english": "Please help.",
}
)
+ "\n",
encoding="utf-8",
)
run_wrapper(manifest_path, mode="cnn", output_path=output_path)
row = json.loads(output_path.read_text(encoding="utf-8").strip())
assert row["cnn_prediction"] is not None
assert row["vlm_prediction"] is None
assert isinstance(row["token_trace"], list)
assert row["failure_notes"] == []
def test_load_clip_dataset_accepts_hybrid_eval_rows_and_resolves_local_clip(tmp_path) -> None:
clips_dir = tmp_path / "clips"
clips_dir.mkdir()
local_clip = clips_dir / "12320_computer.mp4"
local_clip.write_bytes(b"fake")
manifest_path = tmp_path / "hybrid.jsonl"
manifest_path.write_text(
json.dumps(
{
"candidate_model": "landmark_cnn",
"video_id": "12320",
"true_label": "computer",
"video_path": "/content/drive/MyDrive/BridgeLink-ASL/vlm_eval_wlasl25_cnn/clips/12320_computer.mp4",
"cnn_top1": "computer",
"cnn_top5": [
{"label": "computer", "confidence": 0.19},
{"label": "snow", "confidence": 0.08},
],
"vlm_prompt": "Choose the best label from the list only.",
}
)
+ "\n",
encoding="utf-8",
)
records = load_clip_dataset(manifest_path)
assert len(records) == 1
assert records[0].clip_id == "12320"
assert records[0].split == "test"
assert records[0].gloss == ("COMPUTER",)
assert records[0].candidate_labels == ("COMPUTER", "SNOW")
assert records[0].video_path == local_clip.resolve()
def test_local_qwen_interpreter_parses_json_response_without_real_model() -> None:
class FakeInterpreter(LocalQwen25VlmInterpreter):
def _generate_response_text(self, messages):
return json.dumps(
{
"gloss": ["computer"],
"sentence": "Computer.",
"confidence": 0.88,
"needs_clarification": False,
}
)
interpreter = FakeInterpreter(model_id="Qwen/Qwen2.5-VL-7B-Instruct")
event = interpreter.interpret(
GestureWindow(
clip_id="demo",
sampled_frames=(),
token_trace=(),
video_path=None,
candidate_labels=("COMPUTER", "SNOW"),
)
)
assert event.gloss == ("COMPUTER",)
assert event.sentence == "Computer."
assert event.model_mode == "vlm"
assert event.confidence == 0.88
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