Give wordless pictures a way to be found
A screenshot with no OCR-able words used to be a memory nothing could reach. It now has a vector, and a question can be asked with it. - picture space: Xenova/chinese-clip-vit-base-patch16 fp16 ONNX, 361MB, sha256 0fad978faa89b85f…, CPU-runnable on the onnxruntime the repo already ships. Text 76ms, image 29ms/image at batch 8. The ONNX contract was measured, not assumed: it is the fused contrastive graph, so the unused tower is fed a placeholder row, and the tokenizer must be BertTokenizer — a CLIP tokenizer emits ids past the embedding table. - visual_index.py keeps picture vectors in their own family beside the text ones, keyed by atom id, and refuses to answer when the map and the array disagree. retrieval.py stays under 500 lines. - a search can be asked with a picture, and when the words come up thin the picture ranking joins the fusion — but only then, so a text query never pays for the encoder. - visual tokens are counted at 32x32 patches (migration v7): 21,325 image atoms went from 5,331 to 34,618 tokens, and the library from 29.9M to 84.2M. The 100M capsule fits this 50,278-file corpus and nothing much larger — that is what the claim actually means. - the vision-language describer is written and gated: with no snapshot an atom records "deferred: no vision-language model snapshot"; a broken model becomes a note on the atom instead of a dead ingest. - forget now voids the picture index and the copy this process holds, so a forgotten picture stops answering from RAM. Measured, six arms, ScreenSpot-v2 (554 icon crops, 408 whole screenshots), bench/reports/VISUAL.md: icon pool picture 0.208 words 0.175 chance 0.018 screenshot pool words 0.384 picture 0.215 screenshot to icon 0.108 — CLIP is a whole-image model, not a grounding model: it cannot point at one small icon inside a screen. within one screen MRR 0.908 (recall@10 is 1.000 there only because chance is 1.000 too, so MRR is the only honest number in that row). ScreenSpot-v2 instructions are all English, so this run does not test the bilingual claim and the report says so. Gates: 199 tests, ruff clean on 17 files, mypy clean on 16 (fresh cache), the repo's own pyright 0 errors on the 12 files touched.
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# VISUAL: can a library of wordless pictures be searched? (commit 7e1fe649f)
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- generated 2026-10-08T00:37:50.278445+00:00
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- corpus: OS-Copilot/ScreenSpot-v2 (icon instructions), 554 icon crops and 408 whole screenshots stored as separate memories, 427 crops with no OCR text
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- encoder: Xenova/chinese-clip-vit-base-patch16 sha256 0fad978faa89b85f
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- machine: 32 cpus, load [46.81, 44.0, 33.68]
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- instructions containing Chinese: 0 (ScreenSpot-v2 is English, so this run does not test the bilingual claim)
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| ask | pool | recall@1 | recall@5 | recall@10 | chance@10 | MRR | hit@10 lower 95% | p50 s | p95 s |
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| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| words → icon crops | crops | 0.090 | 0.152 | 0.175 | 0.0181 | 0.118 | 0.146 | 0.1716 | 0.2096 |
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| a sentence → icon crops (picture space) | crops | 0.081 | 0.155 | 0.208 | 0.0181 | 0.116 | 0.176 | 0.1399 | 0.1998 |
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| the whole screenshot → icon crops | crops | 0.038 | 0.083 | 0.108 | 0.0181 | 0.058 | 0.085 | 0.1599 | 0.22 |
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| words → whole screenshots | shots | 0.121 | 0.296 | 0.384 | 0.0245 | 0.196 | 0.345 | 0.1362 | 0.2038 |
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| a sentence → whole screenshots (picture space) | shots | 0.049 | 0.146 | 0.215 | 0.0245 | 0.091 | 0.183 | 0.136 | 0.204 |
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| a sentence → the icons of that one screen | own_screen | 0.827 | 1.000 | 1.000 | 1.0000 | 0.908 | 0.993 | 0.1358 | 0.1959 |
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The words rows are what the product could do before any picture vector: if OCR found no words, there is nothing to match. The picture rows ask the same sentence, or the whole screenshot, of the CLIP space. The pools change the question: among every icon in the library, among whole screenshots (the granularity the store keeps), and among one screen's icons, which is the shape ScreenSpot itself poses. A number is only comparable inside its own pool, so chance is printed per row.
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@@ -0,0 +1,166 @@
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{
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"generated_at": "2026-10-08T00:37:50.278445+00:00",
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"commit": "7e1fe649f68bcb25108e2781d912c8a80e3bbfc8",
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"dataset": "OS-Copilot/ScreenSpot-v2 (icon instructions)",
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"model": {
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"repo": "Xenova/chinese-clip-vit-base-patch16",
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"sha256": "0fad978faa89b85fda53a83a4c202116a9eb87c8f9723fb4d85e4483efbce4b2"
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},
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"targets": 554,
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"screenshots": 408,
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"crops_without_ocr_text": 427,
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"instruction_languages": {
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"cjk_instructions": 0
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},
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"machine": {
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"system": "Linux",
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"release": "6.18.40.1-microsoft-standard-WSL2",
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"cpus": 32,
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"threads_per_query": 4,
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"load_average_1_5_15": [
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46.81,
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44.0,
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33.68
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]
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},
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"arms": [
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{
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"label": "words → icon crops",
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"key": "words->crops",
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"n_answerable": 554,
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"n_unanswerable": 0,
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"recall_at_1": 0.09025270758122744,
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"recall_at_5": 0.15162454873646208,
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"recall_at_10": 0.17509025270758122,
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"ndcg_at_10": 0.13173348831922826,
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"mrr": 0.11802403873703514,
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"hit_at_10": 0.17509025270758122,
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"hit_at_10_wilson_lower": 0.1457109350541202,
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"abstention_rate_answerable": 0.04512635379061372,
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"abstention_rate_unanswerable": 0.0,
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"top_hit_from_holder_unanswerable": 0.0,
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"latency": {
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"queries": 554,
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"p50": 0.1716,
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"p95": 0.2096
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},
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"chance_recall_at_10": 0.0181,
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"pool": "crops"
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},
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{
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"label": "a sentence → icon crops (picture space)",
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"key": "picture_words->crops",
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"n_answerable": 554,
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"n_unanswerable": 0,
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"recall_at_1": 0.0812274368231047,
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"recall_at_5": 0.1552346570397112,
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"recall_at_10": 0.2075812274368231,
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"ndcg_at_10": 0.13734530518050886,
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"mrr": 0.1158894905736061,
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"hit_at_10": 0.2075812274368231,
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"hit_at_10_wilson_lower": 0.1758785967641179,
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"abstention_rate_answerable": 0.0,
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"abstention_rate_unanswerable": 0.0,
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"top_hit_from_holder_unanswerable": 0.0,
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"latency": {
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"queries": 554,
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"p50": 0.1399,
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"p95": 0.1998
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},
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"chance_recall_at_10": 0.0181,
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"pool": "crops"
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},
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{
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"label": "the whole screenshot → icon crops",
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"key": "picture->crops",
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"n_answerable": 554,
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"n_unanswerable": 0,
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"recall_at_1": 0.03790613718411552,
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"recall_at_5": 0.08303249097472924,
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"recall_at_10": 0.10830324909747292,
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"ndcg_at_10": 0.069893811531563,
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"mrr": 0.058046100509999435,
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"hit_at_10": 0.10830324909747292,
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"hit_at_10_wilson_lower": 0.08507164644767994,
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"abstention_rate_answerable": 0.0,
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"abstention_rate_unanswerable": 0.0,
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"top_hit_from_holder_unanswerable": 0.0,
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"latency": {
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"queries": 554,
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"p50": 0.1599,
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"p95": 0.22
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},
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"chance_recall_at_10": 0.0181,
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"pool": "crops"
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},
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{
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"label": "words → whole screenshots",
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"key": "words->shots",
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"n_answerable": 554,
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"n_unanswerable": 0,
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"recall_at_1": 0.12093862815884476,
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"recall_at_5": 0.296028880866426,
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"recall_at_10": 0.3844765342960289,
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"ndcg_at_10": 0.24046684872963128,
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"mrr": 0.1959028135923443,
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"hit_at_10": 0.3844765342960289,
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"hit_at_10_wilson_lower": 0.34489504523476483,
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"abstention_rate_answerable": 0.0,
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"abstention_rate_unanswerable": 0.0,
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"top_hit_from_holder_unanswerable": 0.0,
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"latency": {
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"queries": 554,
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"p50": 0.1362,
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"p95": 0.2038
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},
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"chance_recall_at_10": 0.0245,
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"pool": "shots"
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},
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{
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"label": "a sentence → whole screenshots (picture space)",
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"key": "picture_words->shots",
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"n_answerable": 554,
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"n_unanswerable": 0,
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"recall_at_1": 0.048736462093862815,
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"recall_at_5": 0.14620938628158844,
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"recall_at_10": 0.2148014440433213,
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"ndcg_at_10": 0.11948802140007041,
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"mrr": 0.09050126067274082,
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"hit_at_10": 0.2148014440433213,
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"hit_at_10_wilson_lower": 0.18262874778266808,
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"abstention_rate_answerable": 0.0,
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"abstention_rate_unanswerable": 0.0,
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"top_hit_from_holder_unanswerable": 0.0,
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"latency": {
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"queries": 554,
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"p50": 0.136,
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"p95": 0.204
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},
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"chance_recall_at_10": 0.0245,
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"pool": "shots"
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},
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{
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"label": "a sentence → the icons of that one screen",
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"key": "picture_words->own_screen",
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"n_answerable": 554,
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"n_unanswerable": 0,
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"recall_at_1": 0.8267148014440433,
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"recall_at_5": 1.0,
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"recall_at_10": 1.0,
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"ndcg_at_10": 0.9318097223990938,
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"mrr": 0.9080024067388689,
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"hit_at_10": 1.0,
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"hit_at_10_wilson_lower": 0.9931137086353976,
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"abstention_rate_answerable": 0.0,
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"abstention_rate_unanswerable": 0.0,
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"top_hit_from_holder_unanswerable": 0.0,
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"latency": {
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"queries": 554,
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"p50": 0.1358,
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"p95": 0.1959
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},
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"chance_recall_at_10": 1.0,
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"pool": "own_screen"
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}
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]
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}
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"""Can a library of wordless pictures be searched?
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ScreenSpot-v2 is the only corpus in this suite whose gold answer is a *region of a screen*, and
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its icon targets carry almost no text. That makes it the honest test of the picture index: a
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sentence about an icon has to reach the icon itself, not a transcription of it.
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Six arms, three questions:
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words BM25 over whatever OCR found — what the product did before any picture vector
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picture a sentence, or a whole screenshot, asked of the CLIP space
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pool the same question at three granularities: among all icon crops, among whole
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screenshots, and among the icons of one screen (the shape ScreenSpot itself poses)
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"""
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from __future__ import annotations
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import hashlib
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import json
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import re
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import statistics
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import subprocess
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import sys
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import time
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from dataclasses import dataclass
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Any
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sys.path.insert(0, str(Path(__file__).resolve().parent))
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from ely_eye.config import Settings
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from ely_eye.db import Database
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from ely_eye.ocr import read_text_from_image
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from ely_eye.retrieval import RetrievalService
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from ely_eye.schemas import (
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EvidenceAtom,
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Modality,
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SourceRecord,
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TrustScores,
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)
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from ely_eye.storage import (
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ObjectStore,
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token_equivalent,
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visual_token_equivalent,
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)
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from ely_eye.visual_index import VisualIndex
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from metrics import aggregate, chance_recall_at_k, score_query
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from systems import machine_facts
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ROOT = Path(__file__).resolve().parent
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CORPUS = ROOT / "corpus" / "OS-Copilot" / "ScreenSpot-v2"
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IMAGES = CORPUS / "images"
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IMAGE_ZIP = CORPUS / "screenspotv2_image.zip"
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CROPS = ROOT / "runs" / "visual" / "crops"
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HOME = ROOT / "runs" / "visual"
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MODEL_DIR = ROOT / "models" / "chinese-clip-vit-base-patch16"
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REPORTS = ROOT / "reports"
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TOP_K = 10
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@dataclass(frozen=True)
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class Arm:
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"""One way to ask, one pool to answer from, one kind of gold."""
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label: str
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signal: str # words | picture_words | picture
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pool: str # crops | shots | own_screen
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gold: str # crop | screen
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@property
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def key(self) -> str:
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return f"{self.signal}->{self.pool}"
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ARMS = (
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Arm("words → icon crops", "words", "crops", "crop"),
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Arm("a sentence → icon crops (picture space)", "picture_words", "crops", "crop"),
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Arm("the whole screenshot → icon crops", "picture", "crops", "crop"),
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Arm("words → whole screenshots", "words", "shots", "screen"),
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Arm("a sentence → whole screenshots (picture space)", "picture_words", "shots", "screen"),
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Arm("a sentence → the icons of that one screen", "picture_words", "own_screen", "crop"),
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)
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def git_commit() -> str:
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return subprocess.run(
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["git", "rev-parse", "HEAD"], cwd=ROOT.parent, check=True, capture_output=True, text=True
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).stdout.strip()
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def model_sha256() -> str:
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graph = MODEL_DIR / "onnx" / "model_fp16.onnx"
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if not graph.exists():
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raise SystemExit(f"{graph} is missing; fetch the Chinese-CLIP fp16 export first")
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digest = hashlib.sha256()
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with graph.open("rb") as handle:
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for block in iter(lambda: handle.read(1024 * 1024), b""):
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digest.update(block)
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return digest.hexdigest()
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def ensure_images() -> None:
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"""The dataset ships its screenshots inside one zip; unpack them once, flat."""
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if IMAGES.exists() and any(IMAGES.iterdir()):
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return
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if not IMAGE_ZIP.exists():
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raise SystemExit(f"{IMAGE_ZIP} is missing; download the ScreenSpot-v2 corpus first")
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import zipfile
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started = time.monotonic()
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with zipfile.ZipFile(IMAGE_ZIP) as archive:
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names = [name for name in archive.namelist() if name.lower().endswith((".png", ".jpg", ".jpeg"))]
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IMAGES.mkdir(parents=True, exist_ok=True)
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for name in names:
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target = IMAGES / Path(name).name
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if not target.exists():
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target.write_bytes(archive.read(name))
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print(f"unpacked {len(names)} screenshots into {IMAGES} in {time.monotonic() - started:.1f}s", flush=True)
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def icon_targets() -> list[dict[str, Any]]:
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"""Every icon instruction, with its crop and the screenshot it came from."""
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ensure_images()
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targets: list[dict[str, Any]] = []
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for name in ("mobile", "desktop", "web"):
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rows = _read_rows(CORPUS / f"screenspot_{name}_v2.json")
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for row in rows:
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if row.get("data_type") != "icon":
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continue
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picture = CORPUS / "images" / row["img_filename"]
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if not picture.exists():
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raise SystemExit(f"{picture}: ScreenSpot names a screenshot that is not on disk")
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stem = Path(row["img_filename"]).stem
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crop_path = CROPS / f"{stem}-{len(targets)}.png"
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screenshot_path = CROPS / f"{stem}-full.png"
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CROPS.mkdir(parents=True, exist_ok=True)
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from PIL import Image
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with Image.open(picture) as screen:
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left, top, width, height = [int(value) for value in row["bbox"]]
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if not crop_path.exists():
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screen.crop((left, top, left + width, top + height)).convert("RGB").save(crop_path)
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if not screenshot_path.exists():
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screen.convert("RGB").save(screenshot_path)
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targets.append(
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{
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"instruction": row["instruction"],
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"source_id": f"{stem}-icon-{len(targets)}",
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"screen_id": f"screen-{stem}",
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"crop": crop_path,
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"screenshot": screenshot_path,
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"size": (width, height),
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"holder": row.get("data_source"),
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}
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)
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if not targets:
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raise SystemExit(f"{CORPUS}: no icon targets found; is the ScreenSpot-v2 corpus downloaded?")
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return targets
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def screenshot_targets(targets: list[dict[str, Any]]) -> list[dict[str, Any]]:
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"""One memory per distinct screenshot: the granularity the product actually stores."""
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screens: dict[str, dict[str, Any]] = {}
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for target in targets:
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screen = screens.setdefault(
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target["screen_id"],
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{"source_id": target["screen_id"], "screenshot": target["screenshot"], "crops": []},
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)
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screen["crops"].append(target["source_id"])
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return list(screens.values())
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def _read_rows(path: Path) -> list[dict[str, Any]]:
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try:
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return json.loads(path.read_text(encoding="utf-8"))
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except (OSError, json.JSONDecodeError) as error:
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raise ValueError(f"{path}: the ScreenSpot instruction file is unreadable ({error})") from error
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def _atom(settings: Settings, store: ObjectStore, path: Path, source_id: str, modality: Modality) -> EvidenceAtom:
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ocr = read_text_from_image(path)
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width, height = _size(path)
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return EvidenceAtom(
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atom_id=f"{source_id}:image",
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modality=modality,
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source_id=source_id,
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source=str(path),
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text=ocr.text,
|
||||
image_ref=store.put_file(path, "images"),
|
||||
trust=TrustScores(parser=1.0, ocr=ocr.confidence, model=0.0),
|
||||
token_equivalent=token_equivalent(ocr.text) + visual_token_equivalent(width, height),
|
||||
scenario="ui-referencing",
|
||||
)
|
||||
|
||||
|
||||
def ingest(settings: Settings, targets: list[dict[str, Any]], screens: list[dict[str, Any]]) -> None:
|
||||
"""Store each crop and each screenshot as one memory, the way ingestion would."""
|
||||
database = Database(settings)
|
||||
store = ObjectStore(settings)
|
||||
atoms: list[EvidenceAtom] = []
|
||||
sources: list[SourceRecord] = []
|
||||
for position, target in enumerate(targets):
|
||||
path = Path(target["crop"])
|
||||
atoms.append(_atom(settings, store, path, str(target["source_id"]), Modality.image))
|
||||
sources.append(_source(str(target["source_id"]), path))
|
||||
if (position + 1) % 100 == 0:
|
||||
print(f"ingested {position + 1}/{len(targets)} crops", flush=True)
|
||||
for position, screen in enumerate(screens):
|
||||
path = Path(screen["screenshot"])
|
||||
atoms.append(_atom(settings, store, path, str(screen["source_id"]), Modality.ui_screenshot))
|
||||
sources.append(_source(str(screen["source_id"]), path))
|
||||
if (position + 1) % 100 == 0:
|
||||
print(f"ingested {position + 1}/{len(screens)} screenshots", flush=True)
|
||||
database.upsert_sources(sources)
|
||||
database.upsert_atoms(atoms)
|
||||
|
||||
|
||||
def _source(source_id: str, path: Path) -> SourceRecord:
|
||||
return SourceRecord(
|
||||
source_id=source_id,
|
||||
path=str(path),
|
||||
kind="image",
|
||||
mime="image/png",
|
||||
sha256=hashlib.sha256(path.read_bytes()).hexdigest(),
|
||||
size_bytes=path.stat().st_size,
|
||||
)
|
||||
|
||||
|
||||
def _size(path: Path) -> tuple[int, int]:
|
||||
from PIL import Image
|
||||
|
||||
with Image.open(path) as picture:
|
||||
return picture.size
|
||||
|
||||
|
||||
def run_arm(
|
||||
arm: Arm,
|
||||
targets: list[dict[str, Any]],
|
||||
pools: dict[str, Any],
|
||||
index: VisualIndex,
|
||||
retrieval: RetrievalService,
|
||||
source_of_atom: dict[str, str],
|
||||
) -> tuple[list[list[str]], list[float]]:
|
||||
"""Ask every instruction one way, and keep the answers in corpus ids."""
|
||||
ranked: list[list[str]] = []
|
||||
seconds: list[float] = []
|
||||
for position, target in enumerate(targets):
|
||||
pool = _pool(arm, target, pools)
|
||||
started = time.monotonic()
|
||||
if arm.signal == "words":
|
||||
hits = [hit.atom.atom_id for hit in retrieval.search(target["instruction"], TOP_K * 4).hits]
|
||||
answer = [hit for hit in hits if pool is None or hit in pool][:TOP_K]
|
||||
elif arm.signal == "picture_words":
|
||||
answer = [atom_id for atom_id, _ in index.search_text(target["instruction"], TOP_K, pool)]
|
||||
else:
|
||||
answer = [atom_id for atom_id, _ in index.search_image(target["screenshot"], TOP_K, pool)]
|
||||
seconds.append(time.monotonic() - started)
|
||||
ranked.append([source_of_atom[atom_id] for atom_id in answer if atom_id in source_of_atom])
|
||||
if (position + 1) % 100 == 0:
|
||||
print(f"{arm.label}: {position + 1}/{len(targets)}", flush=True)
|
||||
return ranked, seconds
|
||||
|
||||
|
||||
def _pool(arm: Arm, target: dict[str, Any], pools: dict[str, Any]) -> set[str] | None:
|
||||
if arm.pool == "own_screen":
|
||||
return {
|
||||
atom_id
|
||||
for atom_id, source_id in pools["atom_of_target"].items()
|
||||
if source_id in set(pools["screen_crops"][target["screen_id"]])
|
||||
}
|
||||
return pools.get(arm.pool)
|
||||
|
||||
|
||||
def score(arm: Arm, targets: list[dict[str, Any]], ranked: list[list[str]], seconds: list[float]) -> dict[str, Any]:
|
||||
scores = [
|
||||
score_query(
|
||||
f"{target['source_id']}, {target['instruction'][:40]}",
|
||||
hits,
|
||||
{target["source_id"] if arm.gold == "crop" else target["screen_id"]},
|
||||
)
|
||||
for target, hits in zip(targets, ranked, strict=True)
|
||||
]
|
||||
ordered = sorted(seconds)
|
||||
return {
|
||||
"label": arm.label,
|
||||
"key": arm.key,
|
||||
**aggregate(scores),
|
||||
"latency": {
|
||||
"queries": len(scores),
|
||||
"p50": round(statistics.median(ordered), 4),
|
||||
"p95": round(ordered[min(len(ordered) - 1, int(len(ordered) * 0.95))], 4),
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def main() -> None:
|
||||
settings = Settings(home=HOME, clip_model_dir=MODEL_DIR)
|
||||
targets = icon_targets()
|
||||
screens = screenshot_targets(targets)
|
||||
database = Database(settings)
|
||||
if database.atom_count() != len(targets) + len(screens):
|
||||
started = time.monotonic()
|
||||
ingest(settings, targets, screens)
|
||||
print(f"ingested {len(targets)} crops + {len(screens)} screenshots in {time.monotonic() - started:.1f}s", flush=True)
|
||||
source_of_atom = {atom.atom_id: atom.source_id for atom in database.list_atoms()}
|
||||
crops = {atom.atom_id for atom in database.list_atoms() if atom.modality is Modality.image}
|
||||
shots = {atom.atom_id for atom in database.list_atoms() if atom.modality is Modality.ui_screenshot}
|
||||
wordless = sum(
|
||||
1 for atom in database.list_atoms() if atom.atom_id in crops and not atom.text.strip()
|
||||
)
|
||||
|
||||
index = VisualIndex(settings, database)
|
||||
if not index.is_current():
|
||||
started = time.monotonic()
|
||||
built = index.build()
|
||||
print(f"encoded {built} in {time.monotonic() - started:.1f}s", flush=True)
|
||||
retrieval = RetrievalService(settings, database)
|
||||
|
||||
pools = {
|
||||
"crops": crops,
|
||||
"shots": shots,
|
||||
"atom_of_target": source_of_atom,
|
||||
"screen_crops": {screen["source_id"]: screen["crops"] for screen in screens},
|
||||
}
|
||||
per_screen = [len(screen["crops"]) for screen in screens]
|
||||
rows = []
|
||||
for arm in ARMS:
|
||||
ranked, seconds = run_arm(arm, targets, pools, index, retrieval, source_of_atom)
|
||||
row = score(arm, targets, ranked, seconds)
|
||||
row["chance_recall_at_10"] = round(
|
||||
chance_recall_at_k(
|
||||
1,
|
||||
len(crops) if arm.pool == "crops" else len(shots) if arm.pool == "shots" else int(statistics.median(per_screen)),
|
||||
TOP_K,
|
||||
),
|
||||
4,
|
||||
)
|
||||
row["pool"] = arm.pool
|
||||
rows.append(row)
|
||||
|
||||
report = {
|
||||
"generated_at": datetime.now(timezone.utc).isoformat(),
|
||||
"commit": git_commit(),
|
||||
"dataset": "OS-Copilot/ScreenSpot-v2 (icon instructions)",
|
||||
"model": {"repo": "Xenova/chinese-clip-vit-base-patch16", "sha256": model_sha256()},
|
||||
"targets": len(targets),
|
||||
"screenshots": len(screens),
|
||||
"crops_without_ocr_text": wordless,
|
||||
"instruction_languages": {
|
||||
"cjk_instructions": sum(1 for t in targets if re.search(r"[一-鿿]", t["instruction"]))
|
||||
},
|
||||
"machine": machine_facts(),
|
||||
"arms": rows,
|
||||
}
|
||||
REPORTS.mkdir(parents=True, exist_ok=True)
|
||||
(REPORTS / "visual.json").write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
|
||||
(REPORTS / "VISUAL.md").write_text(markdown(report), encoding="utf-8")
|
||||
for row in rows:
|
||||
print(
|
||||
f"{row['label']}: recall@10 {row['recall_at_10']:.3f} (chance {row['chance_recall_at_10']:.3f}) "
|
||||
f"mrr {row['mrr']:.3f} p50 {row['latency']['p50']}s"
|
||||
)
|
||||
print(f"wrote {REPORTS / 'VISUAL.md'}")
|
||||
|
||||
|
||||
def markdown(report: dict[str, Any]) -> str:
|
||||
lines = [
|
||||
f"# VISUAL: can a library of wordless pictures be searched? (commit {report['commit'][:9]})",
|
||||
"",
|
||||
f"- generated {report['generated_at']}",
|
||||
(
|
||||
f"- corpus: {report['dataset']}, {report['targets']} icon crops and {report['screenshots']} whole "
|
||||
f"screenshots stored as separate memories, {report['crops_without_ocr_text']} crops with no OCR text"
|
||||
),
|
||||
f"- encoder: {report['model']['repo']} sha256 {report['model']['sha256'][:16]}",
|
||||
f"- machine: {report['machine']['cpus']} cpus, load {report['machine']['load_average_1_5_15']}",
|
||||
(
|
||||
f"- instructions containing Chinese: {report['instruction_languages']['cjk_instructions']} "
|
||||
"(ScreenSpot-v2 is English, so this run does not test the bilingual claim)"
|
||||
),
|
||||
"",
|
||||
"| ask | pool | recall@1 | recall@5 | recall@10 | chance@10 | MRR | hit@10 lower 95% | p50 s | p95 s |",
|
||||
"| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |",
|
||||
]
|
||||
for row in report["arms"]:
|
||||
lines.append(
|
||||
f"| {row['label']} | {row['pool']} | {row['recall_at_1']:.3f} | {row['recall_at_5']:.3f} "
|
||||
f"| {row['recall_at_10']:.3f} | {row['chance_recall_at_10']:.4f} | {row['mrr']:.3f} "
|
||||
f"| {row['hit_at_10_wilson_lower']:.3f} | {row['latency']['p50']} | {row['latency']['p95']} |"
|
||||
)
|
||||
lines += [
|
||||
"",
|
||||
(
|
||||
"The words rows are what the product could do before any picture vector: if OCR found no words, "
|
||||
"there is nothing to match. The picture rows ask the same sentence, or the whole screenshot, of the "
|
||||
"CLIP space. The pools change the question: among every icon in the library, among whole screenshots "
|
||||
"(the granularity the store keeps), and among one screen's icons, which is the shape ScreenSpot "
|
||||
"itself poses. A number is only comparable inside its own pool, so chance is printed per row."
|
||||
),
|
||||
"",
|
||||
]
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in new issue
Block a user