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Chặn tất cả các file .docx bằng gitignore
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fix(notebook): robust resume for streaming -- retry + per-shard done markers
convitom +1: Claude Opus 4.7Streaming downloads of ~880 MB shards hit transient ChunkedEncodingError / IncompleteRead on flaky cloud networks. Add: - _download_retry: 6 attempts with linear backoff; hf_hub_download resumes the partial download so each retry continues rather than restarts. - HF_HUB_DOWNLOAD_TIMEOUT=60 + commented hf_transfer toggle. - Per-shard '.done' markers under WORK_DIR (outside DST_TREE so never packed): a resumed run skips finished shards WITHOUT re-downloading them. - Final manifest recounts images/non-image from the actual tree so totals are correct even across resumed runs. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>2bace46
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feat(notebook): stream-process sharded HF source per-shard (low disk peak)
convitom +1: Claude Opus 4.7The source on hieu3636/cxr-vlm-data is also tar-sharded (under MIMIC-CXR_processed/shards/*.tar), not a raw image tree. The old notebook assumed a raw tree and would've failed snapshot_download. Rework into a streaming pipeline: list source shards on HF, then for each one download -> extract to scratch -> resize+copy into cumulative resized tree -> delete tar + scratch. Peak disk stays ~10 GB instead of ~200 GB. Implementation: - Split resize_tree into _walk_and_process (returns counts, no manifest) and a thin wrapper. Streaming orchestrator calls _walk_and_process per shard and writes ONE final manifest summarising all shards. - Autodetect arcname root on first shard (handles both 'files/...' and 'MIMIC-CXR_processed/files/...' layouts). - Output uploaded to MIMIC-CXR_resized/shards/ mirroring source convention. - Consumption snippet updated to the new shards/*.tar layout. - Removed now-redundant "## 4 Resize" + "## 5 Free source" cells (work is done in the streaming cell). …bb6ccbf -
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feat(eval): add METEOR + optional LLM-as-judge for VQA scoring
convitom +1: Claude Opus 4.7METEOR (WordNet synonym/stem-aware) supplements BLEU/ROUGE for radiology phrasing variance. LLM-as-judge is opt-in via --llm_judge (defaults to gpt-4o-mini, OpenAI-compatible base_url overridable for Gemini/Claude), with --llm_judge_max_samples for cost control. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>8f6cf28 -
feat(model): match frozen encoder dtype to LLM compute precision
convitom +1: Claude Opus 4.7Image encoder weights were loaded at FP32 (~340MB) while compute ran in BF16 under autocast, forcing a cast every forward and wasting VRAM. Now BioViLTEncoder accepts a `dtype` arg and CXRVisionLanguageModel passes `llm_dtype` (resolved once, shared with the LLM) so encoder weights live at BF16/FP16 directly. Also adds 3 GPU profile presets (L4/3090 default, T4, A100) in the Colab notebook so switching hardware is a comment swap. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>2cd8b5a -
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fix(scripts): carry non-image files through resize + tar shards
convitom +1: Claude Opus 4.7The IMG_EXTS filter silently dropped reports (.txt), CheXpert labels (.csv), and any other metadata interleaved in the source tree, so the shipped MIMIC-CXR_resized would lose them. Add a parallel _copy_one worker that copies non-image files verbatim, preserving the exact tree. Manifest now records both counts. Applies to both build_resized_dataset.py and resize_and_shard.ipynb (cell c06). This makes the resized release a faithful mirror of MIMIC-CXR_processed regardless of what's mixed in -- needed if the instruct JSON builder is ever run on the training box (auto-build path reads .txt + chexpert.csv). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>0a99045
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chore: untrack __pycache__ bytecode (already in .gitignore)
convitom +1: Claude Opus 4.7The 5 *.pyc files were tracked before .gitignore got the __pycache__/ rule, so git kept following them. Untrack (keep local copies); the existing ignore rule prevents them coming back. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>8b5bef1 -
chore(data): WIP EDA notebooks + labeler comparison tooling
convitom +1: Claude Opus 4.7Pre-existing in-progress work (not part of the resize tooling): expanded EDA notebooks, eval_labelers tweak, new img_stat.py and labeler_comparison.csv. Committed to clean the working tree. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>21fa652 -
feat(scripts): offline dataset resize + tar-shard tooling for cloud training
convitom +1: Claude Opus 4.7Add build_resized_dataset.py and resize_and_shard.ipynb to pre-shrink the MIMIC-CXR tree to RAD-DINO's 518px working resolution and pack it into tar shards. Avoids re-decoding ~100GB of full-res JPGs every epoch (encoder is only ~1-2% of compute; the win is removing decode + transfer cost, not a feature cache which would be larger and inflexible). Shorter-edge>=518 keying matches the processor exactly so features are unchanged; --square optionally bakes the center-crop. Notebook reads/writes the HF dataset repo (parallel + resumable), no change to dataset.py/cxr_vlm.py. Also fixes leftover rad_dino.py docstring header from the earlier rename. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>47a30f7 -
chore(notebook): wire colab notebook to resolver-built MIMIC JSON
convitom +1: Claude Sonnet 4.6- MIMIC inline parse/build/VQA/filter/save cells → no-ops (the JSON is now auto-built by data.mimic_cxr_builder with PNU CheXpert + VQA). - cell-cfg: auto-discover *chexpert*.csv → mimic_chexpert_csv, set mimic_vqa_root from VQA_ROOT, mimic_auto_build=True; instruct_json is the base path the resolver suffixes. - Refresh section-3/4 markdown to match the new flow. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>c3bc6f2