File size: 5,816 Bytes
e8055cf | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | """Tests for harness/C/run.py -- result-record shape and result-file writing."""
import json
from harness import C
from harness.C import run as harness_run
_FAKE_ANSWER = {
"prompt_text": "<rendered chat template>",
"answer_text": "4",
"answer_raw": "<|im_start|>assistant\n4<|im_end|>",
"input_token_count": 22205,
"vision_input_shapes": {"pixel_values": [76800, 1536], "image_grid_thw": [64, 3]},
"output_token_ids": [19, 151645],
"output_token_count": 2,
"hit_token_limit": False,
"eos_token_ids": [151645],
"generation_seconds": 5.6,
"device": "cuda",
"dtype": "bfloat16",
"library_versions": {"transformers": "5.14.1", "torch": "2.13.0+cu130"},
"generation_config": {
"max_new_tokens": 16,
"do_sample": False,
"temperature": 0.0,
"top_p": None,
"top_k": None,
"enable_thinking": False,
},
}
_FAKE_ROW = {
"id": 7,
"scene_name": "scene0001_00",
"dataset": "scannet",
"question_type": "object_counting",
"question": "How many chairs?",
"options": None,
"ground_truth": "4",
}
_FAKE_SOURCE_INFO = {
"protocol": "thinking",
"spatial_code_format": "explicit",
"input_selection": "selective",
"frame_count": 64,
"depth": "metric",
"tracking": "tracking",
"spatial_code_path": "/workspace/data/spatial codes/.../scene0001_00.json",
"video_path": "/root/data/VSI-Bench/scannet/scene0001_00.mp4",
"frame_indices": [0, 30, 60],
"frame_timestamps": [0.0, 1.0, 2.0],
}
def test_results_dir_for_matches_established_dimension_nesting():
root = harness_run.results_dir_for(
"qwen3.5-4b", "thinking", "explicit", "metric", "tracking", "uniform", 32
)
assert root == (
C.RESULTS_DIR
/ "qwen3.5-4b"
/ "explicit"
/ "metric"
/ "tracking"
/ "uniform"
/ "32"
)
def test_results_dir_for_honors_explicit_override(tmp_path):
root = harness_run.results_dir_for(
"qwen3.5-4b",
"base",
"explicit",
"relative",
"no tracking",
"selective",
16,
tmp_path,
)
assert root == tmp_path
def test_build_record_carries_both_frame_and_spatial_code_provenance():
record = harness_run._build_record(
_FAKE_ROW,
"full prompt text",
_FAKE_ANSWER,
"MRA:.5:.95:.05",
1.0,
"qwen3.5-4b",
"/root/models/qwen3.5-4b",
_FAKE_SOURCE_INFO,
)
# Spatial-code provenance (shared with harness.B).
assert record["spatial_code_format"] == "explicit"
assert record["input_selection"] == "selective"
assert record["frame_count"] == 64
assert record["depth"] == "metric"
assert record["tracking"] == "tracking"
assert record["spatial_code_path"] == _FAKE_SOURCE_INFO["spatial_code_path"]
# Frame provenance (shared with harness.A).
assert record["video_path"] == _FAKE_SOURCE_INFO["video_path"]
assert record["frame_indices"] == [0, 30, 60]
assert record["frame_timestamps_seconds"] == [0.0, 1.0, 2.0]
# Question/answer fields, same shape as A and B.
assert record["question"] == "How many chairs?"
assert record["answer_given"] == "4"
assert record["vision_input_shapes"] == _FAKE_ANSWER["vision_input_shapes"]
assert record["condition"] == "extended:explicit:metric:tracking:selective:64"
assert record["protocol"] == "thinking"
assert record["score"] == 1.0
def test_write_question_result_writes_one_json_file_per_question(tmp_path):
path, record = harness_run.write_question_result(
_FAKE_ROW,
"full prompt text",
_FAKE_ANSWER,
"MRA:.5:.95:.05",
1.0,
"qwen3.5-4b",
"/root/models/qwen3.5-4b",
_FAKE_SOURCE_INFO,
results_dir=tmp_path,
)
assert path == tmp_path / "scene0001_00" / "7.json"
on_disk = json.loads(path.read_text())
assert on_disk == record
def test_build_record_carries_reasoning_fields_when_forced():
extended_answer = {
**_FAKE_ANSWER,
"reasoning_text": "long reasoning about the frames and spatial code",
"reasoning_raw": "long reasoning about the frames and spatial code<|im_end|>",
"reasoning_token_ids": list(range(50)),
"reasoning_token_count": 50,
"reasoning_hit_limit": True,
"forced": True,
"forced_input_token_count": 22300,
}
record = harness_run._build_record(
_FAKE_ROW,
"full prompt text",
extended_answer,
"MRA:.5:.95:.05",
1.0,
"qwen3.5-4b",
"/root/models/qwen3.5-4b",
_FAKE_SOURCE_INFO,
)
assert (
record["reasoning_text"] == "long reasoning about the frames and spatial code"
)
assert record["reasoning_raw"] == "long reasoning about the frames and spatial code<|im_end|>"
assert record["reasoning_token_ids"] == list(range(50))
assert record["reasoning_token_count"] == 50
assert record["reasoning_hit_limit"] is True
assert record["forced"] is True
assert record["forced_input_token_count"] == 22300
def test_video_results_use_video_branch():
assert (
harness_run.results_dir_for(
"qwen3.5-4b", "thinking", "explicit", "metric", "tracking", "video", None
)
== C.RESULTS_DIR / "qwen3.5-4b" / "explicit" / "metric" / "tracking" / "video"
)
def test_video_record_has_no_frame_count_in_condition():
info = dict(_FAKE_SOURCE_INFO, input_selection="video", frame_count=None)
record = harness_run._build_record(
_FAKE_ROW, "prompt", _FAKE_ANSWER, "metric", 1.0, "qwen3.5-4b", "/model", info
)
assert record["condition"] == "thinking:explicit:metric:tracking:video"
assert record["frame_count"] is None
|