diff --git a/.gitignore b/.gitignore index d3aec26..b69af95 100644 --- a/.gitignore +++ b/.gitignore @@ -123,3 +123,5 @@ data/* !data/README.md code_snippets.py .venv +docs/* +*.pt diff --git a/README.md b/README.md index 578d233..7f6669f 100644 --- a/README.md +++ b/README.md @@ -1,21 +1,328 @@ # ExcelTableCNN -This repository is for ExcelTableCNN project - open source automatic table detection on Excel sheets with computer vision. - -The project's goal is to implement OpenSource library for easy and convenient detection of multiple Table Header/Data coordinates by the use of algorithms combined with Deep Learning/Computer Vision approaches. - -## The model design -The first version of the ExcelTableCNN would try to recreate the [TableSense model](https://arxiv.org/abs/2106.13500). So the model structure would look like this: -1) DataLoader with minibatch=1 -2) FCN backbone (this is where the number of channels would be normalized to be 3) -3) RPN - generationg of RoI -4) For each RoI RoIAlign would be implemented and for each RoIAlign segment would be done - - Table classification - change of table presence in the RoI - - BBR + TableSense smart RoIAlign for each bounding box prediction - - Table segmentation - binary mask for table present in each cell of RoI -5) For each RoI bounding boxes would be ranked and filtered with NMS - -## Performance Metrics -TODO - -## Training Data -For this project the data that's going to be used is based on VEnron2 dataset with markup from [TableSense project](https://github.com/microsoft/TableSense/blob/main/dataset/Table%20range%20annotations.txt). + +Open-source table detection on Excel sheets with computer vision: a spreadsheet +is featurized into an image-like tensor (one channel per cell feature) and a +Faster R-CNN detector predicts the bounding ranges of every table on the sheet. + +This is an **independent open-source reimplementation inspired by the +TableSense paper** — Dong et al., *TableSense: Spreadsheet Table Detection with +Convolutional Neural Networks*, AAAI 2019 +([arXiv:2106.13500](https://arxiv.org/abs/2106.13500)). It is not affiliated +with or endorsed by Microsoft; no code from any Microsoft repository is used. + +```python +from excel_table_cnn import detect_tables + +detect_tables("report.xlsx", weights="checkpoints/final.pt") +# [{"sheet": "Q3", "range": "B2:H45", "score": 0.97}, +# {"sheet": "Q3", "range": "J2:M10", "score": 0.91}] +``` + +**Status (alpha):** the pipeline is functional and tested end-to-end (see +[Testing](#testing)); pre-trained weights are not published yet, so to get +useful detections you currently need to train first — see +[Training](#training). + +## How it works + +1. **Featurization** (`excel_table_cnn/data/features.py`): every cell of a + sheet becomes a 30-dimensional feature vector following the paper's + scheme — emptiness, string content and statistics (length, digit/letter + ratios, `%`/decimal presence), number-format template classification + (numeric/date/time), merge membership and direction, bold/italic font, + the four borders, fill and non-default fill/font colors, alignment, + wrapped text, indentation, and formula presence. A sheet becomes an + `H×W×30` tensor at cell resolution. Trailing all-default rows/columns are + trimmed and the used range is capped (default `2048×512`) to survive + sheets with stray formatting. +2. **Detection** (`excel_table_cnn/model/`): a stride-1 fully convolutional + backbone (no pooling — cell-level resolution is preserved, as in the + paper) feeds a torchvision Faster R-CNN with anchors sized in cell units + — the anchor lattice is tuned on the annotated corpus's box-shape census + (spreadsheet tables are tall: median height/width ratio 2.5, p95 = 26) — + and a transform that skips image-style resizing/normalization. +3. **Grid-context backbone** (`excel_table_cnn/model/grid_context.py`) — + *this project's own addition over TableSense*: table boundaries are + global row/column events, so the backbone (a) receives row/column + fill-density and coordinate priors as derived channels, (b) uses dilated + convolutions to span typical table heights, and (c) runs an axial + strip-pooling block that gives every cell a learned summary of its entire + row and column. +4. **PBR boundary snapping** (`excel_table_cnn/model/pbr.py`): the paper's + precise-bounding-box-regression idea, discretized — for each detected + edge, a head reads a ±7-cell feature band around it and *classifies* the + integer offset to the true boundary, directly optimizing exact-boundary + (EoB-0) accuracy. Trained by recovering jittered ground-truth boxes. +5. **Boxes ↔ ranges** (`excel_table_cnn/training/dataset.py`): boxes use a + half-open cell convention — `"A1:C3"` ↔ `[0, 0, 3, 3]` — so even a + single-cell table has positive area. `box_to_range()` converts predictions + back to Excel ranges. +6. **Evaluation** (`excel_table_cnn/evaluation/`): the paper's + Error-of-Boundary metric. EoB of a detection is the maximum absolute + boundary deviation in cells; a detection counts as correct at EoB-0 + (exact) or EoB-2 (≤ 2 cells off). This is far stricter than IoU and is + the metric that matters for downstream extraction. + +## Repository structure + +``` +excel_table_cnn/ + __init__.py # public API (detect_tables, build_model, train_model, ...) + device.py # device resolution: auto = CUDA -> CPU; MPS opt-in + data/ + loader.py # corpus download (VEnron2 etc. from figshare) + markup.py # TableSense table-range annotations (O-UDA licensed) + workbook.py # format dispatch: .xlsx/.xlsm (openpyxl), .xls (xlrd) + features.py # cell featurization -> (H, W, 30) tensors (openpyxl) + features_xls.py # same channels for legacy .xls via xlrd (no LibreOffice) + converter.py # optional .xlsb/.xls -> .xlsx via headless LibreOffice + census.py # GT box-shape stats + anchor-lattice coverage + pipeline.py # end-to-end dataset build with on-disk tensor caching + model/ + backbone.py # stride-1 FCN backbone (GroupNorm; batch size is 1) + grid_context.py # NOVEL: row/col priors + axial strip pooling + pbr.py # PBR boundary snapping (per-edge offset classification) + rcnn.py # customized torchvision Faster R-CNN, corpus-tuned anchors + detector.py # TableDetectionModel + build_model() + training/ + dataset.py # box convention, SpreadsheetDataset, validation + train.py # trainer (per-loss logging, warmup, AMP, checkpoints) + CLI + evaluation/ + eob.py # EoB metric, matching, precision/recall + evaluate.py # evaluation harness + report formatting + inference.py # detect_tables() / load_model() +tests/ # unit tests + the M0 overfit smoke test (see below) +notebooks/ + train_kaggle_colab.ipynb # training runbook for Kaggle / Colab +archive/ # unmaintained legacy code, kept for reference only +``` + +## Installation + +Requires Python ≥ 3.10. The project is managed with [uv](https://docs.astral.sh/uv/) +(standard `pyproject.toml` + committed `uv.lock` — no `requirements.txt`): + +```bash +git clone https://github.com/Flagro/ExcelTableCNN.git +cd ExcelTableCNN +uv sync --extra dev # creates .venv with locked, reproducible deps +uv run pytest -m "not slow" +``` + +Plain pip works too (Kaggle/Colab, or if you don't use uv): + +```bash +pip install -e . # library +pip install -e ".[dev]" # library + test tooling +``` + +On CPU-only Linux machines you can save gigabytes by installing the CPU torch +wheels first: + +```bash +pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu +pip install -e . +``` + +**LibreOffice is optional.** `.xlsx`/`.xlsm` are read with openpyxl and legacy +`.xls` natively with xlrd (including formatting — the one caveat: the +`formula` feature channel is always 0 for `.xls`, since xlrd only exposes +cached formula results). Only `.xlsb` files require converting via +LibreOffice (`--use-libreoffice` / `use_libreoffice=True`). + +## Quickstart: inference + +```python +from excel_table_cnn import detect_tables, load_model + +model = load_model("checkpoints/final.pt") # a trainer checkpoint +tables = detect_tables("report.xlsx", model=model) # all sheets +tables = detect_tables("report.xlsx", sheet_name="Q3", model=model, + score_threshold=0.7) # one sheet, stricter +``` + +Each detection is `{"sheet": str, "range": "B2:H45", "score": float}`. + +## Training + +Training data: the [VEnron2 corpus](https://figshare.com/articles/dataset/Venron/5714233) +(spreadsheets extracted from the Enron email dump) with table-range +annotations from the +[Microsoft TableSense repository](https://github.com/microsoft/TableSense) +(licensed under the Open Use of Data Agreement v1.0). Both are downloaded on +demand — nothing is redistributed in this repository. + +### Locally + +The console script drives the whole pipeline — download, conversion, cached +featurization, training, evaluation: + +```bash +excel-table-cnn-train --data-dir ./data --train-size 50 --test-size 20 \ + --epochs 20 --checkpoint-dir ./checkpoints +``` + +- `--train-size/--test-size` subsample sheets for quick runs; drop them to + train on everything. +- No LibreOffice needed: `.xls` files are read natively. Add + `--use-libreoffice` only if you want the conversion path (e.g. for `.xlsb`). +- Devices: the default `--device auto` picks CUDA when available, otherwise + CPU; mixed precision turns on automatically with CUDA (`--no-amp` to + disable). See [Apple Silicon](#apple-silicon-macs) below for MPS. +- Checkpoints: `last.pt` (every epoch) and `final.pt`; reload with + `excel_table_cnn.load_checkpoint(path)`. +- Feature tensors are cached under `/feature_cache/` keyed by file + hash — the expensive featurization step runs once per sheet, ever. + +Two companion commands work with the artifacts the trainer produces: + +```bash +# EoB report for a checkpoint on the annotated test split (+ worst offenders): +excel-table-cnn-eval --weights checkpoints/final.pt --data-dir ./data --worst 5 + +# Detect tables in any spreadsheet from the command line: +excel-table-cnn-detect report.xls --weights checkpoints/final.pt +# Sheet1!B2:H45 score=0.973 +``` + +The same is available as a library via `get_train_test()`, +`SpreadsheetDataset`, `build_model()`, `train_model()` — see the notebook for +the exact sequence. + +If figshare refuses the automatic corpus download (it sometimes answers +non-browser clients with an empty `202`), download the archive in a browser +and drop it at `/VEnron2.7z` — the loader verifies its MD5 against +the figshare-published checksum and unpacks it. + +### On Kaggle + +1. Create a notebook, enable a GPU accelerator, internet on. +2. Upload/import [`notebooks/train_kaggle_colab.ipynb`](notebooks/train_kaggle_colab.ipynb) + and run it top to bottom. It pip-installs this repo, downloads the corpus + into `/kaggle/working/data`, trains (CUDA + AMP picked up automatically), + and prints an EoB report. + +Shell equivalent inside any Kaggle cell: + +```bash +%pip install -q git+https://github.com/Flagro/ExcelTableCNN.git +!excel-table-cnn-train --data-dir /kaggle/working/data --epochs 20 +``` + +### On Colab + +Same notebook works. One Colab-specific note: mount Google Drive and point +`--data-dir`/`--checkpoint-dir` at it if you want the feature cache and +checkpoints to survive the session. + +### Apple Silicon Macs + +Everything runs locally out of the box — `pip`/`uv` install the MPS-capable +torch wheels on macOS, and `.xls` reading needs no LibreOffice. Two notes: + +- The default `--device auto` uses the **CPU** on Macs, deliberately: with + the current small backbone and batch size 1, measured training throughput + on MPS is 2–5× *slower* than on the M-series performance cores (detection + heads launch many tiny GPU kernels; overhead dominates). +- MPS is still fully supported and covered by a test + (`tests/test_mps_smoke.py`) — pass `--device mps` (or `device="mps"`) to + use it. Worth re-benchmarking when the backbone grows (see roadmap). + +### Diagnosing a run + +The trainer logs all four detection losses separately every `log_every` +steps: `loss_objectness` and `loss_rpn_box_reg` (region proposal network), +`loss_classifier` and `loss_box_reg` (detection head). A healthy run has all +four non-zero and trending down. If `loss_objectness`/`loss_classifier` sit +near zero from the first steps while detections are garbage, the model is +classifying everything as background — a data/label problem, not a tuning +problem (this exact failure mode is why the logging exists). + +## Evaluation + +```python +from excel_table_cnn import SpreadsheetDataset, evaluate_model, format_report + +report = evaluate_model(model, SpreadsheetDataset(test_samples), device="cuda") +print(format_report(report)) +# Evaluated 20 sheets, 34 tables: +# EoB-0: precision=0.61 recall=0.55 (tp=..., fp=..., fn=...) +# EoB-2: precision=0.78 recall=0.71 (...) +``` + +`report["per_sheet"]` carries per-sheet predictions, ground truth, and the +best EoB achieved per table — the starting point for error analysis. For +reference, the TableSense paper reports EoB-2 recall 91.3% / precision 86.5%, +trained on 10,220 hand-labeled sheets; this project trains on the much +smaller VEnron2 annotation set, so expect substantially lower numbers. + +Measured results (2026-07, identical config for all rows: 239 training +sheets, 40 epochs, seed 42, `--max-rows 512 --max-cols 128`, CPU; evaluated +on the same held-out 30-sheet / 67-table VEnron2 split at threshold 0.5): + +| Configuration | EoB-0 recall / precision | EoB-2 recall / precision | +|---|---|---| +| v1 baseline (17 binary channels) | 0 / 0 | 23.9% / 41.0% | +| \+ paper levers (30ch, tuned anchors, 14×14) | 0 / 0 | 19.4% / 24.1% | +| \+ PBR boundary snapping | 10.4% / 14.3% | 29.9% / 40.8% | +| **\+ grid-context backbone (full model)** | **16.4% / 25.6%** | **40.3% / 62.8%** | + +Three honest takeaways: the paper's capacity levers alone *regressed* at +this tiny data scale (they were designed against 10,220 training sheets); +PBR both unlocked exact boundaries and repaired the regression; and the +grid-context backbone — this project's own component — delivered the +largest single jump, halving false positives (29 → 16). Expect all rows to +rise substantially with a full-corpus GPU run; the paper's reference is +EoB-2 91.3% / 86.5% on 25× more training data. + +## Testing + +```bash +uv run pytest -m "not slow" # unit tests, a few seconds +uv run pytest # + slow tests: M0 overfit gate (~1 min CPU) and, + # on Macs, the MPS compatibility smoke test +``` + +The suite covers featurization for both backends (every channel asserted +against crafted .xlsx *and* .xls workbooks, plus a channel-parity test +between them), the box convention (including the degenerate +single-cell/column regressions), dataset validation, model construction and +loss components, the PBR head (including a module-level "learns to snap +edges" test), the grid-context blocks, the box census, device resolution, +the EoB metric, the evaluation harness, the feature cache, the training +loop, and inference output. The slow overfit test is the project's core +gate: the model must overfit a single synthetic sheet and reproduce its +table at **EoB-0 (cell-exact)** — proving the whole pipeline, PBR snapping +included, can learn. + +## Roadmap + +- **Done:** correct data pipeline (30-channel featurization, box convention, + caching), torchvision-based detector with corpus-tuned anchors, PBR + boundary snapping, the grid-context backbone, per-component training + diagnostics, EoB evaluation, test suite with the EoB-0 overfit gate. +- **Next — accuracy:** train on the full annotation set on GPU, + augmentation, categorical color encoding, iterative PBR refinement, + segmentation branch. +- **Then — release:** published pre-trained weights + model card, PyPI + package. + +## Licensing & attribution + +- Code: [MIT](LICENSE), © Anton Potapov. +- The TableSense **method** is reimplemented from the published paper; please + cite the paper if you build on this: + + ```bibtex + @inproceedings{dong2019tablesense, + title={TableSense: Spreadsheet Table Detection with Convolutional Neural Networks}, + author={Dong, Haoyu and Liu, Shijie and Han, Shi and Fu, Zhouyu and Zhang, Dongmei}, + booktitle={AAAI}, + year={2019} + } + ``` + +- Table-range **annotations** come from the Microsoft TableSense repository + under the Open Use of Data Agreement v1.0; the **VEnron2 corpus** is + third-party research data fetched from figshare. Neither is redistributed + here. diff --git a/archive/README.md b/archive/README.md new file mode 100644 index 0000000..ce3af94 --- /dev/null +++ b/archive/README.md @@ -0,0 +1,12 @@ +# Archive + +Unmaintained code kept for reference only. Nothing in this folder is part of +the `excel_table_cnn` package, is covered by tests, or is expected to run. + +| Folder / file | What it was | Why it's here | +|---|---|---| +| `experimental_model/` | A from-scratch Faster-R-CNN-style detector (hand-written anchor generator, RPN heads, RoIAlign, NMS, detection head with a PBR output). | Superseded by the torchvision-based model in `excel_table_cnn/model/`. It could never learn as written — it computes no RPN loss, so proposals stay random and the loss collapses to ~0. Kept because its PBR head structure is the starting point for the planned paper-faithful PBR module (see the project roadmap). | +| `ml_classification/`, `algorithms/`, `table_detector.py` | A pre-CNN prototype: sklearn per-cell header classifier plus heuristic table-body growing. | Different approach and scope; depends on a private library (`dkslib`) and broken import paths. Kept for historical reference. | +| `excel-cnn-testing.ipynb` | The old Kaggle driver notebook. | Its imports point at module paths that no longer exist. Replaced by `notebooks/train_kaggle_colab.ipynb`. | +| `dl_tensors.ipynb` | Early tensor-building exploration for the header classifier. | Obsolete. | +| `code_snippets.py` | Scratchpad of torchvision API experiments. | Never runnable; reference only. Gitignored (local copy only). | diff --git a/excel_table_cnn/algorithms/train_test_composer.py b/archive/algorithms/train_test_composer.py similarity index 100% rename from excel_table_cnn/algorithms/train_test_composer.py rename to archive/algorithms/train_test_composer.py diff --git a/dl_tensors.ipynb b/archive/dl_tensors.ipynb similarity index 100% rename from dl_tensors.ipynb rename to archive/dl_tensors.ipynb diff --git a/excel-cnn-testing.ipynb b/archive/excel-cnn-testing.ipynb similarity index 100% rename from excel-cnn-testing.ipynb rename to archive/excel-cnn-testing.ipynb diff --git a/excel_table_cnn/dl_classification/__init__.py b/archive/experimental_model/__init__.py similarity index 100% rename from excel_table_cnn/dl_classification/__init__.py rename to archive/experimental_model/__init__.py diff --git a/excel_table_cnn/dl_classification/experimental_model/anchor_generator.py b/archive/experimental_model/anchor_generator.py similarity index 100% rename from excel_table_cnn/dl_classification/experimental_model/anchor_generator.py rename to archive/experimental_model/anchor_generator.py diff --git a/excel_table_cnn/dl_classification/experimental_model/cell_featurizer.py b/archive/experimental_model/cell_featurizer.py similarity index 100% rename from excel_table_cnn/dl_classification/experimental_model/cell_featurizer.py rename to archive/experimental_model/cell_featurizer.py diff --git a/excel_table_cnn/dl_classification/experimental_model/detection_head.py b/archive/experimental_model/detection_head.py similarity index 100% rename from excel_table_cnn/dl_classification/experimental_model/detection_head.py rename to archive/experimental_model/detection_head.py diff --git a/excel_table_cnn/dl_classification/experimental_model/fcn_backbone.py b/archive/experimental_model/fcn_backbone.py similarity index 100% rename from excel_table_cnn/dl_classification/experimental_model/fcn_backbone.py rename to archive/experimental_model/fcn_backbone.py diff --git a/excel_table_cnn/dl_classification/experimental_model/model.py b/archive/experimental_model/model.py similarity index 100% rename from excel_table_cnn/dl_classification/experimental_model/model.py rename to archive/experimental_model/model.py diff --git a/excel_table_cnn/dl_classification/experimental_model/nms.py b/archive/experimental_model/nms.py similarity index 100% rename from excel_table_cnn/dl_classification/experimental_model/nms.py rename to archive/experimental_model/nms.py diff --git a/excel_table_cnn/dl_classification/experimental_model/roi_align.py b/archive/experimental_model/roi_align.py similarity index 100% rename from excel_table_cnn/dl_classification/experimental_model/roi_align.py rename to archive/experimental_model/roi_align.py diff --git a/excel_table_cnn/dl_classification/experimental_model/rpn.py b/archive/experimental_model/rpn.py similarity index 100% rename from excel_table_cnn/dl_classification/experimental_model/rpn.py rename to archive/experimental_model/rpn.py diff --git a/excel_table_cnn/dl_classification/experimental_model/train_eval.py b/archive/experimental_model/train_eval.py similarity index 100% rename from excel_table_cnn/dl_classification/experimental_model/train_eval.py rename to archive/experimental_model/train_eval.py diff --git a/excel_table_cnn/dl_classification/experimental_model/__init__.py b/archive/ml_classification/__init__.py similarity index 100% rename from excel_table_cnn/dl_classification/experimental_model/__init__.py rename to archive/ml_classification/__init__.py diff --git a/excel_table_cnn/ml_classification/ml_classifier.py b/archive/ml_classification/ml_classifier.py similarity index 100% rename from excel_table_cnn/ml_classification/ml_classifier.py rename to archive/ml_classification/ml_classifier.py diff --git a/excel_table_cnn/ml_classification/utils.py b/archive/ml_classification/utils.py similarity index 100% rename from excel_table_cnn/ml_classification/utils.py rename to archive/ml_classification/utils.py diff --git a/excel_table_cnn/table_detector.py b/archive/table_detector.py similarity index 100% rename from excel_table_cnn/table_detector.py rename to archive/table_detector.py diff --git a/excel_table_cnn/__init__.py b/excel_table_cnn/__init__.py index e69de29..6ace560 100644 --- a/excel_table_cnn/__init__.py +++ b/excel_table_cnn/__init__.py @@ -0,0 +1,56 @@ +"""ExcelTableCNN: spreadsheet table detection with CNNs. + +An independent open-source reimplementation inspired by the TableSense paper +(Dong et al., AAAI 2019, arXiv:2106.13500). +""" + +__version__ = "0.3.0" + +from .data.census import box_census, lattice_coverage +from .data.features import FEATURE_NAMES, NUM_FEATURES, featurize_sheet +from .data.pipeline import build_samples, build_sheet_sample, get_train_test +from .data.workbook import UnsupportedFormatError, WorkbookReader, load_sheet_array +from .device import resolve_device +from .evaluation.eob import eob, eob_precision_recall +from .evaluation.evaluate import evaluate_model, format_report +from .inference import detect_tables, load_model +from .model.detector import TableDetectionModel, build_model +from .training.dataset import ( + SpreadsheetDataset, + box_to_range, + collate_fn, + parse_table_range, +) +from .training.train import TrainConfig, load_checkpoint, save_checkpoint, train_model + +__all__ = [ + "__version__", + "box_census", + "lattice_coverage", + "FEATURE_NAMES", + "NUM_FEATURES", + "featurize_sheet", + "build_samples", + "build_sheet_sample", + "get_train_test", + "UnsupportedFormatError", + "WorkbookReader", + "load_sheet_array", + "resolve_device", + "eob", + "eob_precision_recall", + "evaluate_model", + "format_report", + "detect_tables", + "load_model", + "TableDetectionModel", + "build_model", + "SpreadsheetDataset", + "box_to_range", + "collate_fn", + "parse_table_range", + "TrainConfig", + "load_checkpoint", + "save_checkpoint", + "train_model", +] diff --git a/excel_table_cnn/ml_classification/__init__.py b/excel_table_cnn/data/__init__.py similarity index 100% rename from excel_table_cnn/ml_classification/__init__.py rename to excel_table_cnn/data/__init__.py diff --git a/excel_table_cnn/data/census.py b/excel_table_cnn/data/census.py new file mode 100644 index 0000000..718a043 --- /dev/null +++ b/excel_table_cnn/data/census.py @@ -0,0 +1,77 @@ +"""Ground-truth box census: measure table shapes and anchor-lattice coverage. + +Used to derive the anchor defaults in ``model/rcnn.py`` from the actual +annotation corpus instead of guessing. Rerun on a new corpus with:: + + from excel_table_cnn.data.census import box_census, lattice_coverage + from excel_table_cnn.data.markup import MarkupLoader + stats = box_census(MarkupLoader().get_markup("tablesense")) +""" + +import math +from typing import Dict, List, Sequence, Tuple + +from ..training.dataset import parse_table_range + + +def collect_box_dims(markup_df) -> List[Tuple[float, float]]: + """(width, height) in cells for every parseable annotated table range.""" + dims: List[Tuple[float, float]] = [] + for ranges in markup_df["table_range"]: + for rng in ranges: + rng = rng.strip() + if not rng: + continue + try: + x1, y1, x2, y2 = parse_table_range(rng) + except ValueError: + continue + dims.append((x2 - x1, y2 - y1)) + return dims + + +def _percentile(sorted_values: Sequence[float], p: float) -> float: + return sorted_values[round(p / 100 * (len(sorted_values) - 1))] + + +def box_census(markup_df) -> Dict[str, Dict[str, float]]: + """Percentile summary of widths, heights, sqrt-areas and h/w ratios.""" + dims = collect_box_dims(markup_df) + series = { + "width": sorted(w for w, _ in dims), + "height": sorted(h for _, h in dims), + "sqrt_area": sorted(math.sqrt(w * h) for w, h in dims), + "hw_ratio": sorted(h / w for w, h in dims), + } + return { + name: {f"p{p}": _percentile(values, p) for p in (1, 5, 25, 50, 75, 95, 99)} + for name, values in series.items() + } + + +def _anchor_dims(size: float, ratio: float) -> Tuple[float, float]: + """torchvision convention: h = size*sqrt(ratio), w = size/sqrt(ratio).""" + return size / math.sqrt(ratio), size * math.sqrt(ratio) + + +def lattice_coverage( + dims: Sequence[Tuple[float, float]], + sizes: Sequence[float], + ratios: Sequence[float], + iou_thresholds: Sequence[float] = (0.5, 0.7), +) -> Dict[float, float]: + """Fraction of GT boxes whose best (centered) anchor reaches each IoU.""" + + def best_iou(width: float, height: float) -> float: + best = 0.0 + for size in sizes: + for ratio in ratios: + aw, ah = _anchor_dims(size, ratio) + inter = min(width, aw) * min(height, ah) + union = width * height + aw * ah - inter + best = max(best, inter / union) + return best + + coverage = [best_iou(w, h) for w, h in dims] + n = max(len(coverage), 1) + return {t: sum(c >= t for c in coverage) / n for t in iou_thresholds} diff --git a/excel_table_cnn/data/converter.py b/excel_table_cnn/data/converter.py new file mode 100644 index 0000000..5278bc6 --- /dev/null +++ b/excel_table_cnn/data/converter.py @@ -0,0 +1,50 @@ +"""Convert legacy spreadsheet formats to .xlsx via headless LibreOffice. + +Openpyxl only reads .xlsx; the VEnron2 corpus ships as .xls. Conversion is +lossy for some formatting details, which is a known limitation — see the +README's dataset section. +""" + +import logging +import os +import subprocess + +from tqdm import tqdm + +logger = logging.getLogger(__name__) + +CONVERTIBLE_EXTENSIONS = (".xls", ".xlsb") + + +def convert_file(file_path: str, output_dir: str) -> bool: + """Convert one file to .xlsx next to it. Returns True if the .xlsx exists.""" + output_file_path = os.path.splitext(file_path)[0] + ".xlsx" + if os.path.exists(output_file_path): + return True + try: + subprocess.run( + ["libreoffice", "--headless", "--convert-to", "xlsx", + file_path, "--outdir", output_dir], + check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, + ) + except (subprocess.CalledProcessError, FileNotFoundError) as exc: + logger.error("Error converting %s: %s", file_path, exc) + return os.path.exists(output_file_path) + + +def convert_files(files_df, data_folder_path: str): + """Convert every .xls/.xlsb row of a files dataframe; returns an updated + copy where converted rows point at the .xlsx file name.""" + updated_files_df = files_df.copy() + + for index, row in tqdm(files_df.iterrows(), total=files_df.shape[0], + desc="Converting files"): + file_name, file_ext = os.path.splitext(row["file_name"]) + if file_ext.lower() not in CONVERTIBLE_EXTENSIONS: + continue + file_path = os.path.join(data_folder_path, row["parent_path"], row["file_name"]) + output_directory = os.path.join(data_folder_path, row["parent_path"]) + if convert_file(file_path, output_directory): + updated_files_df.at[index, "file_name"] = file_name + ".xlsx" + + return updated_files_df diff --git a/excel_table_cnn/data/features.py b/excel_table_cnn/data/features.py new file mode 100644 index 0000000..97d4566 --- /dev/null +++ b/excel_table_cnn/data/features.py @@ -0,0 +1,259 @@ +"""Cell featurization: turn an openpyxl worksheet into an (H, W, C) float array. + +v2 scheme, 30 channels per cell (``FEATURE_NAMES`` gives the order): the +17 original binary channels plus value-string, data-format and cell-format +groups (string statistics, number-format template classification, merge +direction, non-default colors). Non-binary channels are normalized to [0, 1]. + +The array always starts at A1 so that annotation coordinates remain valid +without offset bookkeeping; trailing all-default rows/columns are trimmed +(plus a small margin), and the used range is capped to protect against +sheets with huge stray formatting. +""" + +import logging +import math +import re +from typing import List, Optional, Tuple + +import numpy as np +from openpyxl.worksheet.worksheet import Worksheet + +logger = logging.getLogger(__name__) + +# Bump when FEATURE_NAMES or their semantics change: invalidates cached tensors. +FEATURES_VERSION = "v2" + +FEATURE_NAMES: List[str] = [ + # --- v1 binary channels (order preserved) --- + "is_empty", + "is_string", + "is_merged", + "is_bold", + "is_italic", + "left_border", + "right_border", + "top_border", + "bottom_border", + "is_filled", + "horizontal_alignment", + "left_horizontal_alignment", + "right_horizontal_alignment", + "center_horizontal_alignment", + "wrapped_text", + "indent", + "formula", + # --- v2: value-string statistics (normalized) --- + "string_length", + "digit_ratio", + "letter_ratio", + "has_percent_symbol", + "has_decimal_point", + # --- v2: number-format template classification --- + "numeric_format", + "date_format", + "time_format", + "format_length", + # --- v2: cell-format extensions --- + "merged_horizontal", + "merged_vertical", + "non_default_fill_color", + "non_default_font_color", +] + +NUM_FEATURES = len(FEATURE_NAMES) + +_IS_EMPTY_IDX = FEATURE_NAMES.index("is_empty") + +# Hard caps on the featurized used range. Real tables larger than this exist +# but are rare in the training corpus; stray formatting thousands of rows below +# the data is common and would otherwise explode memory. +DEFAULT_MAX_ROWS = 2048 +DEFAULT_MAX_COLS = 512 + +# Rows/columns kept beyond the last non-default cell, so tables whose +# annotation extends slightly into empty cells aren't clipped. +TRIM_MARGIN = 2 + +_STRING_LENGTH_CAP = 1024.0 +_FORMAT_LENGTH_CAP = 64.0 + + +def default_cell_vector() -> np.ndarray: + """Feature vector of an untouched cell: empty, everything else off.""" + vec = np.zeros(NUM_FEATURES, dtype=np.float32) + vec[_IS_EMPTY_IDX] = 1.0 + return vec + + +def value_text(value) -> str: + """Canonical text of a cell value, identical across backends. + + xlrd reports every number as float (30 -> 30.0) while openpyxl keeps + ints; canonicalize so string statistics agree. + """ + if value is None: + return "" + if isinstance(value, float) and value.is_integer(): + return str(int(value)) + return str(value) + + +def text_stats(text: str) -> List[float]: + """string_length, digit_ratio, letter_ratio, has %, has '.' — all [0,1].""" + if not text: + return [0.0, 0.0, 0.0, 0.0, 0.0] + n = len(text) + digits = sum(ch.isdigit() for ch in text) + letters = sum(ch.isalpha() for ch in text) + return [ + min(math.log1p(n) / math.log1p(_STRING_LENGTH_CAP), 1.0), + digits / n, + letters / n, + float("%" in text), + float("." in text), + ] + + +def format_features(number_format: Optional[str]) -> List[float]: + """numeric_format, date_format, time_format, format_length — from the + cell's number-format template string (e.g. ``#,##0.00`` or ``yyyy-mm-dd``). + + Color sections (``[Red]``) and quoted literals are stripped before token + matching so e.g. the ``d`` in ``[Red]`` doesn't read as a date token. + """ + if not number_format or number_format.lower() == "general": + return [0.0, 0.0, 0.0, 0.0] + fmt = re.sub(r"\[[^\]]*\]", "", number_format.lower()) + fmt = re.sub(r'"[^"]*"', "", fmt) + is_time = any(token in fmt for token in ("h", "s", "am/pm")) + is_date = "y" in fmt or "d" in fmt or ("m" in fmt and not is_time) + is_numeric = "0" in fmt or "#" in fmt + return [ + float(is_numeric), + float(is_date), + float(is_time), + min(len(number_format) / _FORMAT_LENGTH_CAP, 1.0), + ] + + +def _openpyxl_color_is_nondefault(color) -> bool: + # Dispatch on Color.type: accessing a mismatched attribute (e.g. .rgb on + # a theme color) returns the Typed-descriptor's error *string*, not None. + if color is None: + return False + color_type = getattr(color, "type", None) + if color_type == "rgb": + return color.rgb.upper() not in ("00000000", "FF000000", "FFFFFFFF") + if color_type == "indexed": + return color.indexed not in (0, 1, 64, 65) # black/white/auto + if color_type == "theme": + return color.theme not in (0, 1) # background-1 / text-1 + return False # auto or unknown + + +def cell_feature_vector(cell, merged_extent: Optional[Tuple[bool, bool]]) -> List[float]: + """Features of a single openpyxl cell, ordered as FEATURE_NAMES. + + ``merged_extent`` is None for unmerged cells, else (spans_multiple_cols, + spans_multiple_rows) of the merged range the cell belongs to. + """ + alignment = cell.alignment + border = cell.border + text = value_text(cell.value) + merged_h = bool(merged_extent and merged_extent[0]) + merged_v = bool(merged_extent and merged_extent[1]) + return [ + float(cell.value is None), + float(cell.data_type in ("s", "str")), + float(merged_extent is not None), + float(bool(cell.font.b)), + float(bool(cell.font.i)), + # ``border.left`` is always a Side object in openpyxl; only its + # ``style`` says whether a border is actually drawn. + float(border.left.style is not None), + float(border.right.style is not None), + float(border.top.style is not None), + float(border.bottom.style is not None), + float(cell.fill.patternType is not None), + float(alignment.horizontal is not None), + float(alignment.horizontal == "left"), + float(alignment.horizontal == "right"), + float(alignment.horizontal == "center"), + float(bool(alignment.wrapText)), + float(bool(alignment.indent)), + float(cell.data_type == "f"), + *text_stats(text), + *format_features(cell.number_format), + float(merged_h), + float(merged_v), + float( + cell.fill.patternType is not None + and _openpyxl_color_is_nondefault(cell.fill.fgColor) + ), + float(_openpyxl_color_is_nondefault(cell.font.color)), + ] + + +def featurize_sheet( + ws: Worksheet, + max_rows: int = DEFAULT_MAX_ROWS, + max_cols: int = DEFAULT_MAX_COLS, +) -> np.ndarray: + """Featurize a worksheet into an (H, W, NUM_FEATURES) float32 array.""" + used_rows = ws.max_row or 1 + used_cols = ws.max_column or 1 + if used_rows > max_rows or used_cols > max_cols: + logger.warning( + "Sheet %r used range %dx%d exceeds cap %dx%d; clipping", + ws.title, used_rows, used_cols, max_rows, max_cols, + ) + n_rows = min(used_rows, max_rows) + n_cols = min(used_cols, max_cols) + + arr = np.tile(default_cell_vector(), (n_rows, n_cols, 1)) + + merged_extents = {} + for merged_range in ws.merged_cells.ranges: + extent = ( + merged_range.max_col > merged_range.min_col, + merged_range.max_row > merged_range.min_row, + ) + for r in range(merged_range.min_row, merged_range.max_row + 1): + for c in range(merged_range.min_col, merged_range.max_col + 1): + merged_extents[(r, c)] = extent + + for row in ws.iter_rows(min_row=1, max_row=n_rows, min_col=1, max_col=n_cols): + for cell in row: + arr[cell.row - 1, cell.column - 1] = cell_feature_vector( + cell, merged_extents.get((cell.row, cell.column)) + ) + + return trim_trailing_default(arr) + + +def trim_trailing_default(arr: np.ndarray) -> np.ndarray: + """Trim trailing rows/columns whose cells all equal the default vector.""" + non_default = (arr != default_cell_vector()).any(axis=2) + if not non_default.any(): + return arr[:1, :1] + last_row = int(np.nonzero(non_default.any(axis=1))[0][-1]) + 1 + last_col = int(np.nonzero(non_default.any(axis=0))[0][-1]) + 1 + return arr[: min(last_row + TRIM_MARGIN, arr.shape[0]), + : min(last_col + TRIM_MARGIN, arr.shape[1])] + + +def load_sheet_features( + file_path: str, + sheet_name: str, + max_rows: int = DEFAULT_MAX_ROWS, + max_cols: int = DEFAULT_MAX_COLS, +) -> np.ndarray: + """Convenience: open a workbook and featurize one sheet.""" + import openpyxl + + wb = openpyxl.load_workbook(file_path) + try: + return featurize_sheet(wb[sheet_name], max_rows=max_rows, max_cols=max_cols) + finally: + wb.close() diff --git a/excel_table_cnn/data/features_xls.py b/excel_table_cnn/data/features_xls.py new file mode 100644 index 0000000..c304921 --- /dev/null +++ b/excel_table_cnn/data/features_xls.py @@ -0,0 +1,128 @@ +"""Cell featurization for legacy .xls workbooks via xlrd — no LibreOffice. + +xlrd (with ``formatting_info=True``) exposes fonts, borders, fills, alignment, +number formats and merged ranges for the BIFF .xls format, so the channels +match the openpyxl featurizer. Known deviations, both documented and covered +by the parity test's exclusions: + +- the ``formula`` channel is always 0 — xlrd returns cached formula results + without flagging them (value-string statistics therefore describe the + cached *result* for formula cells, not the formula text); +- date/time cells surface as floats, so their value-string statistics differ + from openpyxl's datetime rendering (format channels still agree). +""" + +import logging + +import numpy as np + +from .features import ( + DEFAULT_MAX_COLS, + DEFAULT_MAX_ROWS, + FEATURE_NAMES, + default_cell_vector, + format_features, + text_stats, + trim_trailing_default, + value_text, +) + +logger = logging.getLogger(__name__) + +# xlrd cell types +_XL_CELL_EMPTY = 0 +_XL_CELL_TEXT = 1 +_XL_CELL_BLANK = 6 + +# xlrd XFAlignment.hor_align values +_HALIGN_GENERAL = 0 +_HALIGN_LEFT = 1 +_HALIGN_CENTER = 2 +_HALIGN_RIGHT = 3 + +# BIFF color indexes treated as "default": automatic (32767), black (0, 8), +# white (1, 9), and the auto foreground/background pattern indexes (64, 65). +_DEFAULT_FONT_COLOR_INDEXES = frozenset({32767, 0, 8}) +_DEFAULT_FILL_COLOR_INDEXES = frozenset({64, 65, 0, 1, 8, 9}) + + +def _xls_cell_vector(cell_type, value, xf, font, number_format, merged_extent): + horizontal = xf.alignment.hor_align + text = value_text(value) + filled = xf.background.fill_pattern != 0 + return [ + float(cell_type in (_XL_CELL_EMPTY, _XL_CELL_BLANK)), + float(cell_type == _XL_CELL_TEXT), + float(merged_extent is not None), + float(bool(font.bold)), + float(bool(font.italic)), + float(xf.border.left_line_style != 0), + float(xf.border.right_line_style != 0), + float(xf.border.top_line_style != 0), + float(xf.border.bottom_line_style != 0), + float(filled), + float(horizontal != _HALIGN_GENERAL), + float(horizontal == _HALIGN_LEFT), + float(horizontal == _HALIGN_RIGHT), + float(horizontal == _HALIGN_CENTER), + float(bool(xf.alignment.text_wrapped)), + float(bool(xf.alignment.indent_level)), + 0.0, # formula: not detectable via xlrd (cached results only) + *text_stats("" if cell_type in (_XL_CELL_EMPTY, _XL_CELL_BLANK) else text), + *format_features(number_format), + float(bool(merged_extent and merged_extent[0])), + float(bool(merged_extent and merged_extent[1])), + float( + filled + and xf.background.pattern_colour_index not in _DEFAULT_FILL_COLOR_INDEXES + ), + float(font.colour_index not in _DEFAULT_FONT_COLOR_INDEXES), + ] + + +def featurize_xls_sheet( + book, + sheet, + max_rows: int = DEFAULT_MAX_ROWS, + max_cols: int = DEFAULT_MAX_COLS, +) -> np.ndarray: + """Featurize an xlrd sheet into an (H, W, NUM_FEATURES) float32 array. + + ``book`` must have been opened with ``formatting_info=True``. + """ + used_rows = max(sheet.nrows, 1) + used_cols = max(sheet.ncols, 1) + if used_rows > max_rows or used_cols > max_cols: + logger.warning( + "Sheet %r used range %dx%d exceeds cap %dx%d; clipping", + sheet.name, used_rows, used_cols, max_rows, max_cols, + ) + n_rows = min(used_rows, max_rows) + n_cols = min(used_cols, max_cols) + + arr = np.tile(default_cell_vector(), (n_rows, n_cols, 1)) + + merged_extents = {} + for row_lo, row_hi, col_lo, col_hi in sheet.merged_cells: # half-open + extent = (col_hi - col_lo > 1, row_hi - row_lo > 1) + for r in range(row_lo, row_hi): + for c in range(col_lo, col_hi): + merged_extents[(r, c)] = extent + + # Iterate only rows that physically exist (sheet.nrows can be 0 for an + # empty sheet while the array keeps its 1x1 default floor). + for r in range(min(n_rows, sheet.nrows)): + row_len = sheet.row_len(r) + for c in range(min(n_cols, row_len)): + cell_type = sheet.cell_type(r, c) + xf = book.xf_list[sheet.cell_xf_index(r, c)] + font = book.font_list[xf.font_index] + format_record = book.format_map.get(xf.format_key) + number_format = format_record.format_str if format_record else None + arr[r, c] = _xls_cell_vector( + cell_type, sheet.cell_value(r, c), xf, font, number_format, + merged_extents.get((r, c)), + ) + + assert arr.shape[2] == len(FEATURE_NAMES) + return trim_trailing_default(arr) diff --git a/excel_table_cnn/data/loader.py b/excel_table_cnn/data/loader.py new file mode 100644 index 0000000..5ee2ff9 --- /dev/null +++ b/excel_table_cnn/data/loader.py @@ -0,0 +1,149 @@ +"""Corpus download from figshare, with integrity verification. + +All URLs and checksums below were verified (2026-07-07) against the figshare +API for project 20116 "Versioned Spreadsheet Corpora" (author Wensheng Dou, +all articles CC0) — the upstream source of the annotated corpora. + +Note: figshare's downloader sometimes answers non-browser clients with an +empty ``202 Accepted`` response. If the automatic download fails, download +the archive in a browser and drop it at ``/.7z`` — the +loader picks it up, verifies the MD5, and unpacks it. +""" + +import hashlib +import logging +import os +import shutil + +import pandas as pd +import requests +from py7zr import unpack_7zarchive + +logger = logging.getLogger(__name__) + +USER_AGENT = "Mozilla/5.0 (compatible; ExcelTableCNN dataset loader)" + + +class DatasetLoader: + def __init__(self, save_path): + # Where the datasets will be saved + self.save_path = save_path + + # figshare download URLs (stable file IDs) + self.datasets = { + "VEnron2": "https://figshare.com/ndownloader/files/8639470", + "VFUSE": "https://figshare.com/ndownloader/files/7889911", + "VEUSES": "https://figshare.com/ndownloader/files/7889902", + "VEnron1.1": "https://figshare.com/ndownloader/files/7889866", + "VEnron1.0": "https://figshare.com/ndownloader/files/7889947", + } + # figshare-published MD5s (from api.figshare.com article metadata) + self.known_md5 = { + "VEnron2": "9a724c5f667f7fa371619774a1b19c4b", + "VFUSE": "e822971e2a76e033516b6e6adeb605e7", + "VEUSES": "46f5b8b4233473b2e2b7d388c56a0ea0", + "VEnron1.1": "b83818285dea1ea0161ab5aa1b803c81", + "VEnron1.0": "15a3430526b01a3ace679225a450cc1e", + } + + def _archive_path(self, dataset_name): + return os.path.join(self.save_path, f"{dataset_name}.7z") + + @staticmethod + def _md5(path): + hasher = hashlib.md5() + with open(path, "rb") as f: + for chunk in iter(lambda: f.read(1 << 20), b""): + hasher.update(chunk) + return hasher.hexdigest() + + def verify_archive(self, dataset_name): + """Check the downloaded archive against the figshare-published MD5.""" + archive = self._archive_path(dataset_name) + expected = self.known_md5.get(dataset_name) + if expected is None: + logger.warning("No known checksum for %s; skipping verification", dataset_name) + return True + actual = self._md5(archive) + if actual != expected: + raise RuntimeError( + f"Checksum mismatch for {archive}: got {actual}, expected {expected}. " + "The download is corrupt or tampered with — delete it and retry." + ) + logger.info("Checksum OK for %s (%s)", dataset_name, expected) + return True + + def download_dataset(self, dataset_name): + if dataset_name not in self.datasets: + raise ValueError(f"Dataset {dataset_name} not found.") + + url = self.datasets[dataset_name] + archive = self._archive_path(dataset_name) + os.makedirs(self.save_path, exist_ok=True) + + response = requests.get( + url, stream=True, headers={"User-Agent": USER_AGENT}, timeout=60 + ) + response.raise_for_status() + with open(archive, "wb") as file: + for chunk in response.iter_content(chunk_size=1 << 20): + if chunk: + file.write(chunk) + + if os.path.getsize(archive) == 0: + os.remove(archive) + raise RuntimeError( + f"figshare returned an empty response for {url} (it sometimes " + "answers non-browser clients with 202/empty). Download the file " + f"in a browser and save it as {archive}, then rerun — the loader " + "will verify and unpack it." + ) + self.verify_archive(dataset_name) + logger.info("Dataset %s downloaded successfully.", dataset_name) + + def unpack_dataset(self, dataset_name, keep_archive=False): + archive = self._archive_path(dataset_name) + extract_path = os.path.join(self.save_path, dataset_name) + + if not os.path.exists(archive): + raise FileNotFoundError(f"Dataset archive not found at {archive}.") + + # Register the .7z format with shutil, only if it's not already registered + if "7zip" not in shutil._UNPACK_FORMATS: + shutil.register_unpack_format("7zip", [".7z"], unpack_7zarchive) + + shutil.unpack_archive(archive, extract_path, "7zip") + logger.info("Dataset %s unpacked successfully.", dataset_name) + + if not keep_archive: + os.remove(archive) + logger.info("%s.7z removed after unpacking.", dataset_name) + + def get_dataset(self, dataset_name, check_exists=True): + dataset_dir = os.path.join(self.save_path, dataset_name) + + if check_exists and os.path.exists(dataset_dir): + logger.info("Dataset %s already exists at %s.", dataset_name, dataset_dir) + return + + # A manually-downloaded archive (e.g. via browser) is picked up here. + if os.path.exists(self._archive_path(dataset_name)): + self.verify_archive(dataset_name) + else: + self.download_dataset(dataset_name) + self.unpack_dataset(dataset_name) + + def get_files(self, dataset_name): + dataset_dir = os.path.join(self.save_path, dataset_name) + + if not os.path.exists(dataset_dir): + logger.error("Directory %s does not exist.", dataset_dir) + return pd.DataFrame(columns=["file_name", "parent_path"]) + + result = [] + for root, _dirs, files in os.walk(dataset_dir): + for file in files: + relative_path = os.path.relpath(root, self.save_path) + result.append({"file_name": file, "parent_path": relative_path}) + + return pd.DataFrame(result) diff --git a/excel_table_cnn/train_test_helpers/markup_loader.py b/excel_table_cnn/data/markup.py similarity index 100% rename from excel_table_cnn/train_test_helpers/markup_loader.py rename to excel_table_cnn/data/markup.py diff --git a/excel_table_cnn/data/pipeline.py b/excel_table_cnn/data/pipeline.py new file mode 100644 index 0000000..90dda5f --- /dev/null +++ b/excel_table_cnn/data/pipeline.py @@ -0,0 +1,232 @@ +"""End-to-end dataset preparation: download -> annotations -> featurized, +cached samples ready for ``SpreadsheetDataset``. + +Workbooks are read natively (.xlsx/.xlsm via openpyxl, .xls via xlrd) — no +LibreOffice required. Pass ``use_libreoffice=True`` to convert legacy files +to .xlsx first instead (needed only for .xlsb, or to compare featurization +fidelity between the two paths). + +Featurizing a sheet is the expensive step, so every (file, sheet) tensor is +cached on disk keyed by the file content hash, the sheet name and the +featurization version — reruns and notebook restarts hit the cache. +""" + +import hashlib +import logging +import os +from typing import Dict, List, Optional, Tuple + +import numpy as np +import torch + +from .converter import convert_files +from .features import ( + DEFAULT_MAX_COLS, + DEFAULT_MAX_ROWS, + FEATURE_NAMES, + FEATURES_VERSION, +) +from .loader import DatasetLoader +from .markup import MarkupLoader +from .workbook import NATIVE_EXTENSIONS, WorkbookReader +from ..training.dataset import parse_table_range + +logger = logging.getLogger(__name__) + + +def _cache_key(file_path: str, sheet_name: str) -> str: + hasher = hashlib.sha1() + with open(file_path, "rb") as f: + for chunk in iter(lambda: f.read(1 << 20), b""): + hasher.update(chunk) + hasher.update(sheet_name.encode("utf-8")) + hasher.update(FEATURES_VERSION.encode("utf-8")) + return hasher.hexdigest() + + +def _make_sample( + array: np.ndarray, table_ranges: List[str], file_path: str, sheet_name: str +) -> Dict: + return { + "tensor": torch.from_numpy(array).permute(2, 0, 1).contiguous(), + "boxes": torch.tensor( + [parse_table_range(tr) for tr in table_ranges], dtype=torch.float32 + ).reshape(-1, 4), + "file_path": file_path, + "sheet_name": sheet_name, + "feature_names": list(FEATURE_NAMES), + } + + +def build_sheet_sample( + file_path: str, + sheet_name: str, + table_ranges: List[str], + cache_dir: Optional[str] = None, + array: Optional[np.ndarray] = None, + max_rows: int = DEFAULT_MAX_ROWS, + max_cols: int = DEFAULT_MAX_COLS, +) -> Dict: + """Featurize one sheet (or load it from cache) into a sample dict. + + ``array`` may carry a pre-featurized sheet to avoid reopening the file. + """ + cache_path = None + if cache_dir is not None: + os.makedirs(cache_dir, exist_ok=True) + cache_path = os.path.join(cache_dir, _cache_key(file_path, sheet_name) + ".pt") + if os.path.exists(cache_path): + return torch.load(cache_path, weights_only=True) + + if array is None: + with WorkbookReader(file_path) as reader: + array = reader.sheet_array(sheet_name, max_rows=max_rows, max_cols=max_cols) + + sample = _make_sample(array, table_ranges, file_path, sheet_name) + if cache_path is not None: + torch.save(sample, cache_path) + return sample + + +def build_samples( + files_df, + data_folder_path: str, + cache_dir: Optional[str] = None, + max_rows: int = DEFAULT_MAX_ROWS, + max_cols: int = DEFAULT_MAX_COLS, +) -> List[Dict]: + """Build samples for every (file, sheet) row of a files dataframe. + + Expects columns: parent_path, file_name, sheet_name, table_range (list of + Excel range strings). Sheets that fail to load are logged and skipped. + """ + from tqdm import tqdm + + samples: List[Dict] = [] + if files_df.empty: + return samples + errors = 0 + grouped = files_df.groupby( + files_df.apply(lambda r: os.path.join(r["parent_path"], r["file_name"]), axis=1) + ) + for rel_path, group in tqdm(grouped, desc="Featurizing sheets"): + file_path = os.path.join(data_folder_path, rel_path) + reader = None + try: + for _, row in group.iterrows(): + sheet_name = row["sheet_name"] + cached = None + if cache_dir is not None and os.path.exists(file_path): + cache_path = os.path.join( + cache_dir, _cache_key(file_path, sheet_name) + ".pt" + ) + if os.path.exists(cache_path): + cached = torch.load(cache_path, weights_only=True) + if cached is not None: + samples.append(cached) + continue + if reader is None: + reader = WorkbookReader(file_path).__enter__() + samples.append( + build_sheet_sample( + file_path, sheet_name, list(row["table_range"]), + cache_dir=cache_dir, + array=reader.sheet_array(sheet_name, max_rows=max_rows, + max_cols=max_cols), + max_rows=max_rows, max_cols=max_cols, + ) + ) + except Exception as exc: # noqa: BLE001 - corpus files are wild + errors += 1 + logger.error("Skipping %s: %s", file_path, exc) + finally: + if reader is not None: + reader.close() + if errors: + logger.warning("Skipped %d file(s) due to errors", errors) + return samples + + +def get_train_test( + data_folder_path: str = "./data", + dataset_name: str = "VEnron2", + markup_name: str = "tablesense", + train_size: Optional[int] = None, + testing_size: Optional[int] = None, + cache_dir: Optional[str] = None, + seed: int = 42, + max_rows: int = DEFAULT_MAX_ROWS, + max_cols: int = DEFAULT_MAX_COLS, + use_libreoffice: bool = False, +) -> Tuple[List[Dict], List[Dict]]: + """Download the corpus + annotations and return (train, test) sample lists. + + ``train_size``/``testing_size`` subsample sheets (None = use all). + ``cache_dir`` defaults to ``/feature_cache``. + ``use_libreoffice=True`` converts legacy files to .xlsx before + featurization (requires LibreOffice on PATH); the default reads .xls + natively via xlrd. + """ + if cache_dir is None: + cache_dir = os.path.join(data_folder_path, "feature_cache") + + logger.info("Ensuring dataset %s is available...", dataset_name) + dataset_loader = DatasetLoader(save_path=data_folder_path) + dataset_loader.get_dataset(dataset_name) + dataset_files = dataset_loader.get_files(dataset_name) + + logger.info("Loading markup %s...", markup_name) + markup_files = MarkupLoader().get_markup(markup_name) + + # Annotations may reference .xls while the corpus (after conversion) + # holds .xlsx — match on extension-less names. + dataset_files["file_name_no_ext"] = dataset_files["file_name"].apply( + lambda x: os.path.splitext(x)[0] + ) + markup_files["file_name_no_ext"] = markup_files["file_name"].apply( + lambda x: os.path.splitext(x)[0] + ) + files_df = markup_files.merge( + dataset_files, how="inner", on=["file_name_no_ext", "parent_path"] + ) + files_df = files_df.drop(columns=["file_name_x", "file_name_no_ext"]) + files_df = files_df.rename(columns={"file_name_y": "file_name"}) + + train_df = files_df[files_df["set_type"] == "training_set"] + test_df = files_df[files_df["set_type"] == "testing_set"] + if train_size is not None: + train_df = train_df.sample(min(train_size, len(train_df)), random_state=seed) + if train_df.empty: + train_df = train_df.reindex(columns=files_df.columns) + if testing_size is not None: + test_df = test_df.sample(min(testing_size, len(test_df)), random_state=seed) + + if use_libreoffice: + train_df = convert_files(train_df, data_folder_path) + test_df = convert_files(test_df, data_folder_path) + else: + for name, df in (("train", train_df), ("test", test_df)): + unsupported = ~df["file_name"].str.lower().str.endswith(NATIVE_EXTENSIONS) + if unsupported.any(): + logger.warning( + "Dropping %d %s file(s) with no native reader (e.g. .xlsb); " + "rerun with use_libreoffice=True to include them", + int(unsupported.sum()), name, + ) + train_df = train_df[ + train_df["file_name"].str.lower().str.endswith(NATIVE_EXTENSIONS) + ] + test_df = test_df[ + test_df["file_name"].str.lower().str.endswith(NATIVE_EXTENSIONS) + ] + + train_samples = build_samples( + train_df, data_folder_path, cache_dir=cache_dir, + max_rows=max_rows, max_cols=max_cols, + ) + test_samples = build_samples( + test_df, data_folder_path, cache_dir=cache_dir, + max_rows=max_rows, max_cols=max_cols, + ) + logger.info("Built %d train / %d test samples", len(train_samples), len(test_samples)) + return train_samples, test_samples diff --git a/excel_table_cnn/data/workbook.py b/excel_table_cnn/data/workbook.py new file mode 100644 index 0000000..539251d --- /dev/null +++ b/excel_table_cnn/data/workbook.py @@ -0,0 +1,109 @@ +"""Format-dispatching workbook reader: .xlsx/.xlsm via openpyxl, .xls via xlrd. + +This is what makes LibreOffice optional: legacy .xls files (the bulk of the +VEnron2 corpus) are featurized natively. Only .xlsb has no native reader — +``UnsupportedFormatError`` points at the LibreOffice conversion path. +""" + +import logging +import os + +import numpy as np + +from .features import DEFAULT_MAX_COLS, DEFAULT_MAX_ROWS, featurize_sheet +from .features_xls import featurize_xls_sheet + +logger = logging.getLogger(__name__) + +OPENPYXL_EXTENSIONS = (".xlsx", ".xlsm") +XLRD_EXTENSIONS = (".xls",) +NATIVE_EXTENSIONS = OPENPYXL_EXTENSIONS + XLRD_EXTENSIONS + + +class UnsupportedFormatError(ValueError): + pass + + +class WorkbookReader: + """Uniform lazy reader over supported workbook formats. + + Usage:: + + with WorkbookReader(path) as reader: + for name in reader.sheet_names: + array = reader.sheet_array(name) + """ + + def __init__(self, file_path: str): + self.file_path = file_path + ext = os.path.splitext(file_path)[1].lower() + if ext in OPENPYXL_EXTENSIONS: + self._backend = "openpyxl" + elif ext in XLRD_EXTENSIONS: + self._backend = "xlrd" + else: + raise UnsupportedFormatError( + f"No native reader for {ext!r} files ({file_path!r}). " + "Convert to .xlsx first — e.g. with LibreOffice: " + "libreoffice --headless --convert-to xlsx " + "(see excel_table_cnn.data.converter)." + ) + self._book = None + + def __enter__(self): + if self._backend == "openpyxl": + import openpyxl + + self._book = openpyxl.load_workbook(self.file_path) + else: + import xlrd + + self._book = xlrd.open_workbook(self.file_path, formatting_info=True) + return self + + def __exit__(self, *exc_info): + self.close() + return False + + def close(self): + if self._book is not None: + if self._backend == "openpyxl": + self._book.close() + else: + self._book.release_resources() + self._book = None + + @property + def sheet_names(self): + if self._backend == "openpyxl": + return list(self._book.sheetnames) + return list(self._book.sheet_names()) + + def sheet_array( + self, + sheet_name: str, + max_rows: int = DEFAULT_MAX_ROWS, + max_cols: int = DEFAULT_MAX_COLS, + ) -> np.ndarray: + """Featurize one sheet into an (H, W, NUM_FEATURES) float32 array.""" + if self._backend == "openpyxl": + return featurize_sheet( + self._book[sheet_name], max_rows=max_rows, max_cols=max_cols + ) + return featurize_xls_sheet( + self._book, + self._book.sheet_by_name(sheet_name), + max_rows=max_rows, + max_cols=max_cols, + ) + + +def load_sheet_array( + file_path: str, + sheet_name: str, + max_rows: int = DEFAULT_MAX_ROWS, + max_cols: int = DEFAULT_MAX_COLS, +) -> np.ndarray: + """One-shot convenience for a single sheet.""" + with WorkbookReader(file_path) as reader: + return reader.sheet_array(sheet_name, max_rows=max_rows, max_cols=max_cols) diff --git a/excel_table_cnn/device.py b/excel_table_cnn/device.py new file mode 100644 index 0000000..511aaca --- /dev/null +++ b/excel_table_cnn/device.py @@ -0,0 +1,32 @@ +"""Device selection: run anywhere, use accelerators when they actually help. + +``"auto"`` picks CUDA when available, otherwise CPU. Apple Silicon (MPS) is +fully supported and covered by tests, but it is deliberately **not** part of +auto-selection: with the current small backbone and batch size 1, measured +steady-state training is 2-5x slower on MPS than on the M-series CPU cores +(detection heads launch many tiny kernels; GPU overhead dominates). Pass +``device="mps"`` explicitly to use it — worth re-measuring once the backbone +grows. +""" + +from typing import Optional, Union + +import torch + +DeviceLike = Union[str, torch.device, None] + + +def resolve_device(device: DeviceLike = "auto") -> torch.device: + if device is None or device == "auto": + if torch.cuda.is_available(): + return torch.device("cuda") + return torch.device("cpu") + return torch.device(device) + + +def resolve_amp(amp: Optional[bool], device: torch.device) -> bool: + """AMP default: on for CUDA, off elsewhere (MPS/CPU run fp32 — + GradScaler and autocast are only reliably supported on CUDA).""" + if amp is None: + return device.type == "cuda" + return amp and device.type == "cuda" diff --git a/excel_table_cnn/dl_classification/model/__init__.py b/excel_table_cnn/dl_classification/model/__init__.py deleted file mode 100644 index e355f15..0000000 --- a/excel_table_cnn/dl_classification/model/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__all__ = ['fcn_backbone', 'rcnn', 'model', 'rpn'] diff --git a/excel_table_cnn/dl_classification/model/fcn_backbone.py b/excel_table_cnn/dl_classification/model/fcn_backbone.py deleted file mode 100644 index 932eb1a..0000000 --- a/excel_table_cnn/dl_classification/model/fcn_backbone.py +++ /dev/null @@ -1,23 +0,0 @@ -import torch.nn as nn -import torchvision.models as models - -class FCNBackbone(nn.Module): - def __init__(self, num_features): - super().__init__() - self.conv1 = nn.Conv2d(num_features, 512, kernel_size=3, padding=1) - # self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1) - # self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1) # Increasing to 256 channels - # vgg16_features = models.vgg16(pretrained=True).features[:-1] # Use VGG16's features up to the second to last layer - - # Replace VGG16's first convolution layer with one that accepts num_features channels - # vgg16_features[0] = nn.Conv2d(num_features, 64, kernel_size=3, padding=1) - # self.vgg16_features = vgg16_features - self.out_channels = 512 # Set the correct number of output channels - - def forward(self, x): - x = self.conv1(x) - # x = self.conv2(x) - # x = self.conv3(x) - # x = self.vgg16_features(x) # Apply VGG16 features - # Assume x is shaped (N, 256, H', W') where H' and W' are downscaled from the original H and W. - return x # The features are now ready for the RPN diff --git a/excel_table_cnn/dl_classification/model/model.py b/excel_table_cnn/dl_classification/model/model.py deleted file mode 100644 index a334477..0000000 --- a/excel_table_cnn/dl_classification/model/model.py +++ /dev/null @@ -1,18 +0,0 @@ -import torch - -from .fcn_backbone import FCNBackbone -from .rcnn import CustomFasterRCNN - - -# Define the main model -class TableDetectionModel(torch.nn.Module): - def __init__(self, in_channels=3): - super().__init__() - # Initialize the backbone, a modified FCN with an appropriate number of input channels - self.backbone = FCNBackbone(num_features=in_channels) - - # Pass the backbone and the number of classes to the CustomFasterRCNN - self.model = CustomFasterRCNN(self.backbone, 2) - - def forward(self, images, targets=None): - return self.model(images, targets) diff --git a/excel_table_cnn/dl_classification/model/rcnn.py b/excel_table_cnn/dl_classification/model/rcnn.py deleted file mode 100644 index da1ba24..0000000 --- a/excel_table_cnn/dl_classification/model/rcnn.py +++ /dev/null @@ -1,72 +0,0 @@ -from torchvision.models.detection import FasterRCNN -from torchvision.models.detection.faster_rcnn import FastRCNNPredictor -from torchvision.ops import MultiScaleRoIAlign -from torchvision.models.detection.transform import GeneralizedRCNNTransform -from torchvision.models.detection.rpn import AnchorGenerator -from torchvision.models.detection.rpn import RPNHead, RegionProposalNetwork - - -def get_anchor_generator(): - # Use predefined sizes and aspect ratios. The sizes should be tuples of (min, max). - sizes = ((8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096),) # One sub-tuple for one feature level - aspect_ratios = ((0.00390625, 0.0078125, 0.015625, - 0.03125, 0.0625, 0.125, - 0.25, 0.5, 1.0, 2.0, - 4.0, 8.0, 16.0, 32.0, - 64.0, 128.0, 256.0),) - return AnchorGenerator(sizes=sizes, aspect_ratios=aspect_ratios) - - -# Create a lightweight transform class that overrides the normalize and resize methods -class SkipTransform(GeneralizedRCNNTransform): - def __init__(self, min_size, max_size, image_mean, image_std): - super().__init__(min_size, max_size, image_mean, image_std) - - def normalize(self, image): - # Skip normalization or do your custom normalization if needed - return image - - def resize(self, image, target): - # Skip resizing or do your custom resize if needed - # Returning the original image and target as is - return image, target - - -# Define the custom Faster R-CNN class -class CustomFasterRCNN(FasterRCNN): - def __init__(self, backbone, num_classes): - # Initialize the RPN (Region Proposal Network) - - anchor_generator = get_anchor_generator() - rpn_head = RPNHead(512, anchor_generator.num_anchors_per_location()[0]) - - # Define the box ROI Pooler using RoIAlign - roi_pooler = MultiScaleRoIAlign( - featmap_names=['0'], # Assuming '0' is the name of the feature map to be used for RoI Align - output_size=7, - sampling_ratio=2 - ) - - # Instantiate the Faster R-CNN model using our backbone, our custom RPN, and RoI Align - super().__init__( - backbone=backbone, - num_classes=num_classes, - rpn_anchor_generator=anchor_generator, - rpn_head=rpn_head, - rpn_fg_iou_thresh=0.7, - rpn_bg_iou_thresh=0.3, - rpn_batch_size_per_image=256, - rpn_positive_fraction=0.5, - rpn_pre_nms_top_n=dict(training=2000, testing=1000), - rpn_post_nms_top_n=dict(training=2000, testing=1000), - rpn_nms_thresh=0.7, - box_roi_pool=roi_pooler, - ) - - # Replace the pre-trained head with a new one (number of classes is different) - in_features = self.roi_heads.box_predictor.cls_score.in_features - self.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes) - - # Set the custom skip transform as the transform for the CustomFasterRCNN with the dummy values - min_size, max_size, image_mean, image_std = 100, 1333, ([0.485] * 17), ([0.229] * 17) - self.transform = SkipTransform(min_size, max_size, image_mean, image_std) diff --git a/excel_table_cnn/dl_classification/model/train_eval.py b/excel_table_cnn/dl_classification/model/train_eval.py deleted file mode 100644 index 2d4fc44..0000000 --- a/excel_table_cnn/dl_classification/model/train_eval.py +++ /dev/null @@ -1,143 +0,0 @@ -import torch -from torch.utils.data import DataLoader -from collections import defaultdict - -from .model import TableDetectionModel - - -def get_model(num_classes=2): - model = TableDetectionModel(num_classes) - return model - - -def get_dataloader(dataset): - def collate_fn(batch): - return tuple(zip(*batch)) - - loader = DataLoader(dataset, batch_size=1, shuffle=True, collate_fn=collate_fn) - return loader - - -def train_model(model, train_loader, optimizer, num_epochs, device): - # Send the model to the device (GPU or CPU) - model.to(device) - - # Set the model in training mode - model.train() - - for epoch in range(num_epochs): - epoch_loss = 0 - for images, targets in train_loader: - images = [image.to(device) for image in images] - targets = [{k: v.to(device) for k, v in t.items()} for t in targets] - - # Reset gradients - optimizer.zero_grad() - - # Forward pass - loss_dict = model(images, targets) - - losses = sum(loss for loss in loss_dict.values()) - epoch_loss += losses.item() - - # Backpropagation - losses.backward() - optimizer.step() - - print(f"Epoch {epoch}: Loss: {epoch_loss / len(train_loader)}") - - -def calculate_iou(pred_box, gt_box): - # Determine the coordinates of the intersection rectangle - xA = max(pred_box[0], gt_box[0]) - yA = max(pred_box[1], gt_box[1]) - xB = min(pred_box[2], gt_box[2]) - yB = min(pred_box[3], gt_box[3]) - - # Compute the area of intersection - interArea = max(0, xB - xA) * max(0, yB - yA) - - # Compute the area of both the prediction and ground-truth rectangles - predBoxArea = (pred_box[2] - pred_box[0]) * (pred_box[3] - pred_box[1]) - gtBoxArea = (gt_box[2] - gt_box[0]) * (gt_box[3] - gt_box[1]) - - # Compute the intersection over union by taking the intersection - # area and dividing it by the sum of prediction + ground-truth - # areas - the interesection area - iou = interArea / float(predBoxArea + gtBoxArea - interArea) - - return iou - - -def evaluate_model(model, test_loader, device, iou_threshold=0.5): - model.to(device) - model.eval() - - all_detections = defaultdict(list) - all_ground_truths = defaultdict(list) - - with torch.no_grad(): - for images, targets in test_loader: - images = [img.to(device) for img in images] - outputs = model(images) - - for i, output in enumerate(outputs): - # Assuming single class; adapt as needed for multiple classes - for box, score in zip(output["boxes"], output["scores"]): - all_detections[i].append((box.cpu().numpy(), score.item())) - - for gt_box in targets[i]["boxes"]: - all_ground_truths[i].append(gt_box.cpu().numpy()) - - # Calculate TP, FP, FN for each image - TPs, FPs, FNs = [], [], [] - for image_id, detections in all_detections.items(): - gt_boxes = all_ground_truths[image_id] - detected = [] - - for pred_box, score in sorted(detections, key=lambda x: x[1], reverse=True): - if score < iou_threshold: - continue - - for gt_box in gt_boxes: - iou = calculate_iou(pred_box, gt_box) - - if iou >= iou_threshold: - if gt_box not in detected: - TPs.append((image_id, pred_box, gt_box)) - detected.append(gt_box) - break - else: - FPs.append((image_id, pred_box)) - - for gt_box in gt_boxes: - if gt_box not in detected: - FNs.append((image_id, gt_box)) - - # Calculate precision and recall - precision = len(TPs) / (len(TPs) + len(FPs)) if TPs or FPs else 0 - recall = len(TPs) / (len(TPs) + len(FNs)) if TPs or FNs else 0 - - # Compute AP as the area under the precision-recall curve - # This can be more complex in practice. Here's a simple version - AP = precision * recall - - # Assuming single class. For multiple classes, calculate AP for each and then average - mAP = AP - - return mAP - - -def get_model_output(model, test_loader, device): - model.to(device) - model.eval() # Set the model in inference mode - - eval_loss = 0 - for images, targets in test_loader: - images = [image.to(device) for image in images] - targets = [{k: v.to(device) for k, v in t.items()} for t in targets] - - with torch.no_grad(): - # Forward pass - outputs = model(images) - print(outputs) diff --git a/excel_table_cnn/dl_classification/spreadsheet_dataset.py b/excel_table_cnn/dl_classification/spreadsheet_dataset.py deleted file mode 100644 index 702b346..0000000 --- a/excel_table_cnn/dl_classification/spreadsheet_dataset.py +++ /dev/null @@ -1,37 +0,0 @@ -import torch -from torch.utils.data import Dataset - -from .tensors import DataframeTensors - - -def get_bounding_box(table_ranges): - boxes = torch.tensor( - [ - [min_col, min_row, max_col, max_row] # x_min, y_min, x_max, y_max - for min_col, min_row, max_col, max_row in table_ranges - ], - dtype=torch.float32, - ) - # Assuming '1' is the label for tables: - labels = torch.ones((boxes.shape[0],), dtype=torch.int64) - return {"boxes": boxes, "labels": labels} - - -class SpreadsheetDataset(Dataset): - def __init__(self, tensors: DataframeTensors): - self.tensors = tensors - self.num_cell_features = tensors.num_cell_features - - def __len__(self): - # The length of the dataset is the number of spreadsheets - return len(self.tensors.hwc_tensors) - - def __getitem__(self, idx): - tensor = self.tensors.hwc_tensors[idx] - # Permute tensor to C x H x W - tensor = tensor.permute(2, 0, 1) - - # Get labels - labels = get_bounding_box(self.tensors.zero_indexed_table_ranges[idx]) - - return tensor, labels diff --git a/excel_table_cnn/dl_classification/tensors.py b/excel_table_cnn/dl_classification/tensors.py deleted file mode 100644 index 3c73338..0000000 --- a/excel_table_cnn/dl_classification/tensors.py +++ /dev/null @@ -1,66 +0,0 @@ -from typing import List, Tuple, Optional -from tqdm import tqdm -import torch -from openpyxl.utils.cell import coordinate_to_tuple, range_boundaries - - -def parse_coordinate(coordinate): - # Convert Excel-style coordinate (e.g., 'A1') to numerical indices - # Placeholder for actual implementation - row_index, col_index = coordinate_to_tuple(coordinate) - return (row_index - 1, col_index - 1) - - -def parse_table_range(table_range): - # Convert table range string to numerical coordinates - # Placeholder for actual implementation - min_col, min_row, max_col, max_row = range_boundaries(table_range) - # Match the R-CNN convention: x_min, y_min, x_max, y_max - return [min_col - 1, min_row - 1, max_col - 1, max_row - 1] - - -def preprocess_features(row): - # Convert the pandas Series directly to a tensor - return torch.tensor(row.astype(float).values, dtype=torch.float32) - - -class DataframeTensors: - def __init__(self, dataframe): - # Make tensors in CxHxW format - self.hwc_tensors: List[torch.Tensor] = [] - self.zero_indexed_table_ranges: List[List[Tuple[int, int]]] = [] - self.num_cell_features: Optional[int] = None - - non_feature_columns = ["coordinate", "file_path", "sheet_name", "table_range"] - - # Group by file_path and sheet_name to process each sheet separately - grouped = dataframe.groupby(["file_path", "sheet_name"]) - for _, group in tqdm( - grouped, total=len(grouped), desc="Creating tensors and labels" - ): - max_rows, max_cols = self._get_max_dimensions(group) - if self.num_cell_features is None: - self.num_cell_features = len(group.columns) - len(non_feature_columns) - - sheet_tensor = torch.zeros((max_rows, max_cols, self.num_cell_features)) - - for _, row in group.iterrows(): - row_idx, col_idx = parse_coordinate(row["coordinate"]) - cell_features = preprocess_features(row.drop(non_feature_columns)) - sheet_tensor[row_idx, col_idx, :] = cell_features - - self.hwc_tensors.append(sheet_tensor) - - table_ranges = [ - parse_table_range(tr) for tr in group["table_range"].iloc[0] - ] - self.zero_indexed_table_ranges.append(table_ranges) - - def _get_max_dimensions(self, group): - # Compute the max row and column indices for this spreadsheet - max_row, max_col = 0, 0 - for _, row in group.iterrows(): - row_idx, col_idx = parse_coordinate(row["coordinate"]) - max_row = max(max_row, row_idx) - max_col = max(max_col, col_idx) - return max_row + 1, max_col + 1 # Add 1 because indices are zero-based diff --git a/excel_table_cnn/evaluation/__init__.py b/excel_table_cnn/evaluation/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/excel_table_cnn/evaluation/eob.py b/excel_table_cnn/evaluation/eob.py new file mode 100644 index 0000000..9a4231a --- /dev/null +++ b/excel_table_cnn/evaluation/eob.py @@ -0,0 +1,72 @@ +"""Error-of-Boundary (EoB) metric. + +EoB of a (prediction, ground truth) pair is the maximum absolute deviation of +the four boundaries, in cells. A detection is correct at threshold t if +EoB <= t; we report EoB-0 (exact) and EoB-2. Both boxes must use the same +convention (this package: half-open cell coordinates), which makes the +deviations identical to the closed-range ones. +""" + +from typing import Dict, List, Sequence, Tuple + + +def eob(pred_box: Sequence[float], gt_box: Sequence[float]) -> float: + """Max absolute boundary deviation between two boxes, in cells.""" + return max(abs(float(p) - float(g)) for p, g in zip(pred_box, gt_box)) + + +def match_detections( + pred_boxes: Sequence[Sequence[float]], + pred_scores: Sequence[float], + gt_boxes: Sequence[Sequence[float]], + threshold: float, +) -> Tuple[int, int, int]: + """Greedy score-ordered matching. Returns (tp, fp, fn). + + Each ground-truth table can be matched by at most one detection; extra + detections of the same table count as false positives. + """ + order = sorted(range(len(pred_boxes)), key=lambda i: -float(pred_scores[i])) + matched_gt = set() + tp = fp = 0 + for i in order: + best_gt, best_eob = None, None + for j, gt_box in enumerate(gt_boxes): + if j in matched_gt: + continue + candidate = eob(pred_boxes[i], gt_box) + if best_eob is None or candidate < best_eob: + best_gt, best_eob = j, candidate + if best_gt is not None and best_eob <= threshold: + matched_gt.add(best_gt) + tp += 1 + else: + fp += 1 + fn = len(gt_boxes) - len(matched_gt) + return tp, fp, fn + + +def eob_precision_recall( + per_sheet_predictions: List[Tuple[Sequence[Sequence[float]], Sequence[float]]], + per_sheet_gt_boxes: List[Sequence[Sequence[float]]], + thresholds: Sequence[float] = (0, 2), +) -> Dict[str, Dict[str, float]]: + """Corpus-level precision/recall at each EoB threshold. + + ``per_sheet_predictions`` is a list of (boxes, scores) pairs, one entry + per sheet, aligned with ``per_sheet_gt_boxes``. + """ + report: Dict[str, Dict[str, float]] = {} + for threshold in thresholds: + tp = fp = fn = 0 + for (boxes, scores), gt_boxes in zip(per_sheet_predictions, per_sheet_gt_boxes): + sheet_tp, sheet_fp, sheet_fn = match_detections(boxes, scores, gt_boxes, threshold) + tp, fp, fn = tp + sheet_tp, fp + sheet_fp, fn + sheet_fn + report[f"eob{int(threshold)}"] = { + "precision": tp / (tp + fp) if tp + fp else 0.0, + "recall": tp / (tp + fn) if tp + fn else 0.0, + "tp": tp, + "fp": fp, + "fn": fn, + } + return report diff --git a/excel_table_cnn/evaluation/evaluate.py b/excel_table_cnn/evaluation/evaluate.py new file mode 100644 index 0000000..c94b2fe --- /dev/null +++ b/excel_table_cnn/evaluation/evaluate.py @@ -0,0 +1,143 @@ +"""Evaluation harness: run the detector over a dataset and report EoB metrics.""" + +import argparse +import logging +from typing import Dict, List, Optional, Sequence + +import torch + +from .eob import eob, eob_precision_recall +from ..device import DeviceLike, resolve_device +from ..training.dataset import SpreadsheetDataset, box_to_range + +DEFAULT_THRESHOLDS = (0, 2) + + +@torch.no_grad() +def evaluate_model( + model, + dataset: SpreadsheetDataset, + device: DeviceLike = "auto", + score_threshold: float = 0.5, + eob_thresholds: Sequence[float] = DEFAULT_THRESHOLDS, +) -> Dict: + """Returns EoB precision/recall plus per-sheet details for error analysis.""" + device = resolve_device(device) + model.to(device) + model.eval() + + per_sheet_predictions = [] + per_sheet_gt_boxes = [] + per_sheet_details = [] + + for sample in dataset.samples: + output = model([sample["tensor"].to(device)])[0] + keep = output["scores"] >= score_threshold + boxes = output["boxes"][keep].cpu().tolist() + scores = output["scores"][keep].cpu().tolist() + gt_boxes = sample["boxes"].tolist() + + per_sheet_predictions.append((boxes, scores)) + per_sheet_gt_boxes.append(gt_boxes) + per_sheet_details.append( + { + "file_path": sample.get("file_path"), + "sheet_name": sample.get("sheet_name"), + "predictions": [ + {"range": box_to_range(box), "score": score} + for box, score in zip(boxes, scores) + ], + "ground_truth": [box_to_range(box) for box in gt_boxes], + # Best (lowest) EoB achieved for each ground-truth table: + # the number to look at when hunting systematic boundary errors. + "best_eob_per_gt": [ + min((eob(box, gt) for box in boxes), default=None) + for gt in gt_boxes + ], + } + ) + + report = eob_precision_recall( + per_sheet_predictions, per_sheet_gt_boxes, thresholds=eob_thresholds + ) + report["n_sheets"] = len(dataset) + report["n_gt"] = sum(len(boxes) for boxes in per_sheet_gt_boxes) + report["per_sheet"] = per_sheet_details + return report + + +def format_report(report: Dict) -> str: + lines = [f"Evaluated {report['n_sheets']} sheets, {report['n_gt']} tables:"] + for key, metrics in report.items(): + if not key.startswith("eob"): + continue + lines.append( + f" EoB-{key.removeprefix('eob')}: " + f"precision={metrics['precision']:.3f} recall={metrics['recall']:.3f} " + f"(tp={metrics['tp']} fp={metrics['fp']} fn={metrics['fn']})" + ) + return "\n".join(lines) + + +def main(argv: Optional[List[str]] = None) -> None: + parser = argparse.ArgumentParser( + description="Evaluate a trained checkpoint on the annotated test split." + ) + parser.add_argument("--weights", required=True, + help="checkpoint produced by excel-table-cnn-train") + parser.add_argument("--data-dir", default="./data") + parser.add_argument("--cache-dir", default=None) + parser.add_argument("--dataset", default="VEnron2") + parser.add_argument("--test-size", type=int, default=None) + parser.add_argument("--score-threshold", type=float, default=0.5) + parser.add_argument("--device", default="auto") + parser.add_argument("--max-rows", type=int, default=None) + parser.add_argument("--max-cols", type=int, default=None) + parser.add_argument("--worst", type=int, default=0, + help="also print the N worst sheets for error analysis") + args = parser.parse_args(argv) + + logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s") + + from ..data.features import DEFAULT_MAX_COLS, DEFAULT_MAX_ROWS + from ..data.pipeline import get_train_test + from ..training.train import load_checkpoint + + _, test_samples = get_train_test( + data_folder_path=args.data_dir, + dataset_name=args.dataset, + train_size=0, + testing_size=args.test_size, + cache_dir=args.cache_dir, + max_rows=args.max_rows or DEFAULT_MAX_ROWS, + max_cols=args.max_cols or DEFAULT_MAX_COLS, + ) + dataset = SpreadsheetDataset(test_samples) + # Rebuild with the internal RoI score gate at (or below) the requested + # threshold — otherwise the model default (0.5) silently filters + # detections before --score-threshold ever sees them. + model = load_checkpoint( + args.weights, box_score_thresh=min(args.score_threshold, 0.05) + ) + report = evaluate_model( + model, dataset, device=args.device, score_threshold=args.score_threshold + ) + print(format_report(report)) + + if args.worst: + worst = sorted( + report["per_sheet"], + key=lambda s: max((e for e in s["best_eob_per_gt"] if e is not None), + default=float("inf")), + reverse=True, + ) + for sheet in worst[: args.worst]: + print(f"\n{sheet['file_path']} [{sheet['sheet_name']}]") + print(f" ground truth: {sheet['ground_truth']}") + print(f" predictions : " + f"{[(p['range'], round(p['score'], 2)) for p in sheet['predictions']]}") + print(f" best EoB : {sheet['best_eob_per_gt']}") + + +if __name__ == "__main__": + main() diff --git a/excel_table_cnn/inference.py b/excel_table_cnn/inference.py new file mode 100644 index 0000000..6620a67 --- /dev/null +++ b/excel_table_cnn/inference.py @@ -0,0 +1,108 @@ +"""User-facing inference API: spreadsheet file in, table ranges out. + +Reads .xlsx/.xlsm (openpyxl) and legacy .xls (xlrd) natively — no LibreOffice +needed; .xlsb must be converted first (see ``excel_table_cnn.data.converter``). +""" + +import argparse +import logging +from typing import Dict, List, Optional + +import torch + +from .data.features import DEFAULT_MAX_COLS, DEFAULT_MAX_ROWS, NUM_FEATURES +from .data.workbook import WorkbookReader +from .device import DeviceLike, resolve_device +from .model.detector import TableDetectionModel, build_model +from .training.dataset import box_to_range +from .training.train import load_checkpoint + +logger = logging.getLogger(__name__) + + +def load_model(weights_path: str, device: DeviceLike = "auto", **rcnn_kwargs) -> TableDetectionModel: + """Load a model from a checkpoint produced by the trainer.""" + return load_checkpoint(weights_path, device=resolve_device(device), **rcnn_kwargs) + + +@torch.no_grad() +def detect_tables( + file_path: str, + sheet_name: Optional[str] = None, + model: Optional[TableDetectionModel] = None, + weights: Optional[str] = None, + device: DeviceLike = "auto", + score_threshold: float = 0.5, + max_rows: int = DEFAULT_MAX_ROWS, + max_cols: int = DEFAULT_MAX_COLS, +) -> List[Dict]: + """Detect tables in a spreadsheet file (.xlsx, .xlsm or .xls). + + Returns one dict per detection: ``{"sheet", "range", "score"}``, e.g. + ``{"sheet": "Q3", "range": "B2:H45", "score": 0.97}``. + ``sheet_name=None`` scans all sheets. + """ + device = resolve_device(device) + if model is None: + if weights is not None: + # Keep the model's internal score gate at or below the requested + # threshold so score_threshold alone governs what's returned. + model = load_model( + weights, device=device, + box_score_thresh=min(score_threshold, 0.05), + ) + else: + logger.warning( + "No model or weights given — using randomly initialized weights; " + "detections will be meaningless." + ) + model = build_model(in_channels=NUM_FEATURES) + model.to(device) + model.eval() + + detections: List[Dict] = [] + with WorkbookReader(file_path) as reader: + sheet_names = [sheet_name] if sheet_name is not None else reader.sheet_names + for name in sheet_names: + array = reader.sheet_array(name, max_rows=max_rows, max_cols=max_cols) + tensor = torch.from_numpy(array).permute(2, 0, 1).contiguous().to(device) + output = model([tensor])[0] + for box, score in zip(output["boxes"], output["scores"]): + if float(score) < score_threshold: + continue + detections.append( + { + "sheet": name, + "range": box_to_range(box.cpu().tolist()), + "score": float(score), + } + ) + return detections + + +def main(argv: Optional[List[str]] = None) -> None: + parser = argparse.ArgumentParser( + description="Detect tables in a spreadsheet file (.xlsx, .xlsm or .xls)." + ) + parser.add_argument("file", help="path to the spreadsheet") + parser.add_argument("--weights", required=True, + help="checkpoint produced by excel-table-cnn-train") + parser.add_argument("--sheet", default=None, help="sheet name (default: all sheets)") + parser.add_argument("--score-threshold", type=float, default=0.5) + parser.add_argument("--device", default="auto") + args = parser.parse_args(argv) + + logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s") + detections = detect_tables( + args.file, sheet_name=args.sheet, weights=args.weights, + device=args.device, score_threshold=args.score_threshold, + ) + if not detections: + print("No tables detected above the score threshold.") + return + for det in detections: + print(f"{det['sheet']}!{det['range']}\tscore={det['score']:.3f}") + + +if __name__ == "__main__": + main() diff --git a/excel_table_cnn/model/__init__.py b/excel_table_cnn/model/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/excel_table_cnn/model/backbone.py b/excel_table_cnn/model/backbone.py new file mode 100644 index 0000000..7ff289b --- /dev/null +++ b/excel_table_cnn/model/backbone.py @@ -0,0 +1,61 @@ +"""Fully convolutional backbone for spreadsheet tensors. + +Stride 1 throughout: cell-level resolution is preserved so boundaries can be +located exactly. GroupNorm instead of BatchNorm because detection training +runs with batch size 1. + +With ``use_grid_context=True`` (default) the backbone additionally exploits +grid structure (see ``grid_context.py``): derived row/column priors, a +dilated middle stage (receptive field ~11 → ~30+ cells at zero parameter +cost; corpus median table height is 21 rows), and an axial strip-pooling +block that gives every cell a summary of its entire row and column. +""" + +import torch.nn as nn + +from .grid_context import NUM_DERIVED_CHANNELS, AxialStripBlock, DerivedChannels + + +class FCNBackbone(nn.Module): + def __init__( + self, + in_channels: int, + hidden_channels: int = 64, + out_channels: int = 256, + norm_groups: int = 8, + use_grid_context: bool = True, + ): + super().__init__() + self.in_channels = in_channels # data channels, before derived priors + self.use_grid_context = use_grid_context + + if use_grid_context: + self.derived = DerivedChannels() + conv_in = in_channels + NUM_DERIVED_CHANNELS + dilations = (1, 2, 4) + else: + self.derived = None + conv_in = in_channels + dilations = (1, 1, 1) + + layers = [ + nn.Conv2d(conv_in, hidden_channels, kernel_size=1), + nn.ReLU(inplace=True), + ] + for dilation in dilations: + layers += [ + nn.Conv2d(hidden_channels, hidden_channels, kernel_size=3, + padding=dilation, dilation=dilation), + nn.GroupNorm(norm_groups, hidden_channels), + nn.ReLU(inplace=True), + ] + if use_grid_context: + layers.append(AxialStripBlock(hidden_channels)) + layers.append(nn.Conv2d(hidden_channels, out_channels, kernel_size=3, padding=1)) + self.body = nn.Sequential(*layers) + self.out_channels = out_channels # required by torchvision's FasterRCNN + + def forward(self, x): + if self.derived is not None: + x = self.derived(x) + return self.body(x) diff --git a/excel_table_cnn/model/detector.py b/excel_table_cnn/model/detector.py new file mode 100644 index 0000000..76b2913 --- /dev/null +++ b/excel_table_cnn/model/detector.py @@ -0,0 +1,73 @@ +"""The table detection model: grid-context FCN backbone + customized Faster +R-CNN + PBR boundary snapping.""" + +import torch + +from .backbone import FCNBackbone +from .pbr import PBRHead +from .rcnn import CustomFasterRCNN + + +class TableDetectionModel(torch.nn.Module): + def __init__( + self, + in_channels: int, + num_classes: int = 2, + use_pbr: bool = True, + pbr_k: int = 7, + use_grid_context: bool = True, + **rcnn_kwargs, + ): + super().__init__() + self.in_channels = in_channels + self.num_classes = num_classes + self.use_grid_context = use_grid_context + self.pbr_k = pbr_k + self.backbone = FCNBackbone(in_channels, use_grid_context=use_grid_context) + self.model = CustomFasterRCNN( + self.backbone, in_channels=in_channels, num_classes=num_classes, + **rcnn_kwargs, + ) + self.pbr = PBRHead(self.backbone.out_channels, k=pbr_k) if use_pbr else None + + @property + def config(self) -> dict: + """Architecture flags needed to rebuild this model from a checkpoint.""" + return { + "use_pbr": self.pbr is not None, + "pbr_k": self.pbr_k, + "use_grid_context": self.use_grid_context, + } + + def forward(self, images, targets=None): + """Training mode (with targets): dict of losses (incl. ``loss_pbr`` + when PBR is enabled). Eval mode: list of {"boxes", "labels", + "scores"} per image, with boxes PBR-snapped to cell boundaries.""" + if self.training: + losses = self.model(images, targets) + if self.pbr is not None: + pbr_losses = [] + for image, target in zip(images, targets): + features = self.backbone(image.unsqueeze(0)) + _, height, width = image.shape + pbr_losses.append( + self.pbr.loss(features, target["boxes"], height, width) + ) + losses["loss_pbr"] = torch.stack(pbr_losses).mean() + return losses + + detections = self.model(images) + if self.pbr is not None: + for image, detection in zip(images, detections): + if len(detection["boxes"]) == 0: + continue + features = self.backbone(image.unsqueeze(0)) + _, height, width = image.shape + detection["boxes"] = self.pbr.refine( + features, detection["boxes"], height, width + ) + return detections + + +def build_model(in_channels: int, num_classes: int = 2, **kwargs) -> TableDetectionModel: + return TableDetectionModel(in_channels, num_classes=num_classes, **kwargs) diff --git a/excel_table_cnn/model/grid_context.py b/excel_table_cnn/model/grid_context.py new file mode 100644 index 0000000..3323667 --- /dev/null +++ b/excel_table_cnn/model/grid_context.py @@ -0,0 +1,70 @@ +"""Grid-context components + +Spreadsheets are not natural images: table boundaries are *global +row/column events* (a separator is a whole blank column; a header restyles a +whole row). Two cheap components exploit that grid structure directly: + +- ``DerivedChannels`` prepends four prior channels computed from the input + tensor itself: row fill-density, column fill-density (the exact "blank + separator" signal that heuristics key on) and normalized row/column + coordinates (CoordConv-style; tables cluster toward A1). +- ``AxialStripBlock`` (strip pooling à la Hou et al. 2020, applied to cell + grids): each position receives a learned summary of its *entire row and + entire column* — global context along exactly the two axes that matter in + a grid, far cheaper than attention. +""" + +import torch +from torch import nn + +# Number of channels DerivedChannels prepends. +NUM_DERIVED_CHANNELS = 4 + + +class DerivedChannels(nn.Module): + """Append row/col fill-density and normalized coordinates to the input. + + ``is_empty_index`` locates the emptiness channel in the featurization + (channel 0 in this project's FEATURE_NAMES). + """ + + def __init__(self, is_empty_index: int = 0): + super().__init__() + self.is_empty_index = is_empty_index + + def forward(self, x: torch.Tensor) -> torch.Tensor: # (B, C, H, W) + batch, _, height, width = x.shape + non_empty = 1.0 - x[:, self.is_empty_index : self.is_empty_index + 1] + row_density = non_empty.mean(dim=3, keepdim=True).expand(-1, -1, -1, width) + col_density = non_empty.mean(dim=2, keepdim=True).expand(-1, -1, height, -1) + rows = torch.linspace(0, 1, height, device=x.device, dtype=x.dtype) + cols = torch.linspace(0, 1, width, device=x.device, dtype=x.dtype) + row_coord = rows.view(1, 1, height, 1).expand(batch, 1, height, width) + col_coord = cols.view(1, 1, 1, width).expand(batch, 1, height, width) + return torch.cat([x, row_density, col_density, row_coord, col_coord], dim=1) + + +class AxialStripBlock(nn.Module): + """Residual strip-pooling: fuse whole-row and whole-column summaries.""" + + def __init__(self, channels: int, kernel_size: int = 7, norm_groups: int = 8): + super().__init__() + padding = kernel_size // 2 + # Row branch: pool width away, convolve along H, broadcast back. + self.row_conv = nn.Conv2d(channels, channels, kernel_size=(kernel_size, 1), + padding=(padding, 0)) + # Column branch: pool height away, convolve along W, broadcast back. + self.col_conv = nn.Conv2d(channels, channels, kernel_size=(1, kernel_size), + padding=(0, padding)) + self.fuse = nn.Sequential( + nn.Conv2d(channels, channels, kernel_size=1), + nn.GroupNorm(norm_groups, channels), + ) + self.activation = nn.ReLU(inplace=True) + + def forward(self, x: torch.Tensor) -> torch.Tensor: # (B, C, H, W) + _, _, height, width = x.shape + row_summary = self.row_conv(x.mean(dim=3, keepdim=True)).expand(-1, -1, -1, width) + col_summary = self.col_conv(x.mean(dim=2, keepdim=True)).expand(-1, -1, height, -1) + context = self.fuse(row_summary + col_summary) + return self.activation(x + context) diff --git a/excel_table_cnn/model/pbr.py b/excel_table_cnn/model/pbr.py new file mode 100644 index 0000000..532c28c --- /dev/null +++ b/excel_table_cnn/model/pbr.py @@ -0,0 +1,146 @@ +"""Precise boundary refinement (PBR): snap detected edges to exact cells. + +Generic box regression cannot deliver cell-exact boundaries; this module +refines each edge from features local to that edge, within a hard tolerance +of k cells: + +- the offset is **classified over the 2k+1 integer positions** rather than + regressed: cell boundaries are discrete, and argmax-snapping directly + optimizes EoB-0 (exact-boundary accuracy); +- it is trained on **jittered ground-truth boxes** (each edge displaced by + Uniform{-k..k}) rather than inside the RoI head, which teaches the same + skill — recover the true boundary from a near-miss — without surgery on + torchvision internals. Offsets beyond ±k cannot occur in training, so + there is no gradient outside the tolerance window. +""" + +from typing import Tuple + +import torch +import torch.nn.functional as F +from torch import nn +from torchvision.ops import roi_align + +EDGE_NAMES = ("left", "top", "right", "bottom") # == box coordinate order + + +def jitter_boxes( + boxes: torch.Tensor, k: int, height: int, width: int +) -> Tuple[torch.Tensor, torch.Tensor]: + """Displace every edge by Uniform{-k..k}, keeping boxes valid. + + Returns (jittered_boxes, offsets) where ``offsets = true - jittered`` + per edge — the classification targets, guaranteed within [-k, k]. + """ + if boxes.numel() == 0: + return boxes, boxes + jitter = torch.randint(-k, k + 1, boxes.shape, device=boxes.device).float() + jittered = boxes + jitter + # Keep at least one cell of extent and stay on the sheet. + jittered[:, 0] = jittered[:, 0].clamp(0, width - 1).minimum(boxes[:, 2] - 1) + jittered[:, 1] = jittered[:, 1].clamp(0, height - 1).minimum(boxes[:, 3] - 1) + jittered[:, 2] = jittered[:, 2].clamp(1, width).maximum(jittered[:, 0] + 1) + jittered[:, 3] = jittered[:, 3].clamp(1, height).maximum(jittered[:, 1] + 1) + offsets = (boxes - jittered).clamp(-k, k) + return jittered, offsets + + +class PBRHead(nn.Module): + def __init__( + self, + in_channels: int = 256, + k: int = 7, + band_pool: int = 7, + trunk_channels: int = 64, + hidden: int = 128, + ): + super().__init__() + self.k = k + self.band_pool = band_pool + self.trunk = nn.Sequential( + nn.Conv2d(in_channels, trunk_channels, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(trunk_channels, trunk_channels, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + ) + # One classifier per edge type: the band around a left edge (table to + # the right) looks nothing like the band around a right edge. + flat = trunk_channels * (2 * k + 1) + self.edge_heads = nn.ModuleList( + nn.Sequential( + nn.Linear(flat, hidden), nn.ReLU(inplace=True), + nn.Linear(hidden, 2 * k + 1), + ) + for _ in EDGE_NAMES + ) + + def _band_rois(self, boxes: torch.Tensor, edge: int) -> torch.Tensor: + """Band region covering cells e-k .. e+k around the given edge, with + full box extent along the other axis.""" + k = float(self.k) + x1, y1, x2, y2 = boxes.unbind(dim=1) + edge_coord = boxes[:, edge] + if edge in (0, 2): # vertical edge: band of columns + rois = torch.stack([edge_coord - k, y1, edge_coord + k + 1, y2], dim=1) + else: # horizontal edge: band of rows + rois = torch.stack([x1, edge_coord - k, x2, edge_coord + k + 1], dim=1) + batch_idx = torch.zeros((len(boxes), 1), device=boxes.device) + return torch.cat([batch_idx, rois], dim=1) + + def forward(self, features: torch.Tensor, boxes: torch.Tensor) -> torch.Tensor: + """features: (1, C, H, W) of ONE sheet; boxes: (N, 4). + Returns logits (N, 4 edges, 2k+1 offsets).""" + width = 2 * self.k + 1 + logits = [] + for edge in range(4): + rois = self._band_rois(boxes, edge) + if edge in (0, 2): + band = roi_align(features, rois, output_size=(self.band_pool, width), + aligned=True) + else: + band = roi_align(features, rois, output_size=(width, self.band_pool), + aligned=True) + band = band.permute(0, 1, 3, 2) # offset axis last + band = self.trunk(band) + band = band.mean(dim=2) # pool over the box-extent axis -> (N, C, width) + logits.append(self.edge_heads[edge](band.flatten(1))) + return torch.stack(logits, dim=1) # (N, 4, 2k+1) + + def loss(self, features: torch.Tensor, gt_boxes: torch.Tensor, + height: int, width: int, jitter_repeats: int = 4) -> torch.Tensor: + """Jitter GT boxes, predict the recovery offsets, cross-entropy. + + Features are detached: the detection losses already shape the + backbone, and a stationary feature space lets the snapping head + converge orders of magnitude faster. Each GT box is jittered + ``jitter_repeats`` times per step for denser supervision (bands are + tiny, so this is nearly free). + """ + if gt_boxes.numel() == 0: + return features.sum() * 0.0 + features = features.detach() + repeated = gt_boxes.repeat(jitter_repeats, 1) + jittered, offsets = jitter_boxes(repeated, self.k, height, width) + logits = self(features, jittered) # (N*J, 4, 2k+1) + targets = (offsets + self.k).long() + return F.cross_entropy(logits.reshape(-1, 2 * self.k + 1), targets.reshape(-1)) + + def refine(self, features: torch.Tensor, boxes: torch.Tensor, + height: int, width: int) -> torch.Tensor: + """Snap detector boxes to cell-exact boundaries (one pass). + + Boxes are grid-rounded first so bands are cell-aligned like in + training; boxes that would degenerate keep their rounded coordinates. + """ + if boxes.numel() == 0: + return boxes + rounded = boxes.round() + rounded[:, 2] = rounded[:, 2].maximum(rounded[:, 0] + 1) + rounded[:, 3] = rounded[:, 3].maximum(rounded[:, 1] + 1) + logits = self(features, rounded) + offsets = logits.argmax(dim=2).float() - self.k + refined = rounded + offsets + refined[:, 0::2] = refined[:, 0::2].clamp(0, width) + refined[:, 1::2] = refined[:, 1::2].clamp(0, height) + valid = (refined[:, 2] > refined[:, 0]) & (refined[:, 3] > refined[:, 1]) + return torch.where(valid.unsqueeze(1), refined, rounded) diff --git a/excel_table_cnn/model/rcnn.py b/excel_table_cnn/model/rcnn.py new file mode 100644 index 0000000..2b2f16b --- /dev/null +++ b/excel_table_cnn/model/rcnn.py @@ -0,0 +1,66 @@ +"""Faster R-CNN customized for spreadsheet tensors. + +Differences from the stock torchvision detector: +- anchors sized in cell units for table shapes (few-cell tables up to + tall multi-hundred-row ones); +- the input transform skips resizing and normalization: a sheet tensor is + not a natural image — channels are binary features and coordinates are + cells, so rescaling would destroy cell alignment. +""" + +from torchvision.models.detection import FasterRCNN +from torchvision.models.detection.rpn import AnchorGenerator +from torchvision.models.detection.transform import GeneralizedRCNNTransform +from torchvision.ops import MultiScaleRoIAlign + +# Tuned on the 2,613 annotated VEnron2 ground-truth boxes via +# excel_table_cnn.data.census (2026-07): width p50=8 p95=31; height p50=21 +# p95=167; h/w ratio p50=2.5 p95=26 — spreadsheet tables are tall. This +# 8x9 lattice covers 99.5% of GT boxes at IoU>=0.5 and 71.3% at IoU>=0.7 +# (previous 7x7 lattice with ratios capped at 10: 93.5% / 45.2%). +DEFAULT_ANCHOR_SIZES = ((3, 5, 8, 13, 21, 34, 64, 128),) +DEFAULT_ASPECT_RATIOS = ((0.15, 0.35, 0.7, 1.4, 2.8, 5.5, 11.0, 22.0, 45.0),) + + +class SkipTransform(GeneralizedRCNNTransform): + """Batches images (padding only) but performs no resize/normalize.""" + + def normalize(self, image): + return image + + def resize(self, image, target): + return image, target + + +class CustomFasterRCNN(FasterRCNN): + def __init__( + self, + backbone, + in_channels: int, + num_classes: int = 2, + anchor_sizes=DEFAULT_ANCHOR_SIZES, + aspect_ratios=DEFAULT_ASPECT_RATIOS, + box_score_thresh: float = 0.5, + ): + anchor_generator = AnchorGenerator(sizes=anchor_sizes, aspect_ratios=aspect_ratios) + # 14x14 pooling — finer boundary evidence per RoI. + roi_pooler = MultiScaleRoIAlign( + featmap_names=["0"], output_size=14, sampling_ratio=2 + ) + super().__init__( + backbone=backbone, + num_classes=num_classes, + rpn_anchor_generator=anchor_generator, + rpn_pre_nms_top_n=dict(training=2000, testing=1000), + rpn_post_nms_top_n=dict(training=2000, testing=1000), + box_roi_pool=roi_pooler, + box_score_thresh=box_score_thresh, + ) + # min/max size and mean/std are unused because SkipTransform bypasses + # resize() and normalize(); they only satisfy the constructor. + self.transform = SkipTransform( + min_size=1, + max_size=100000, + image_mean=[0.0] * in_channels, + image_std=[1.0] * in_channels, + ) diff --git a/excel_table_cnn/train_test_helpers/__init__.py b/excel_table_cnn/train_test_helpers/__init__.py deleted file mode 100644 index af23078..0000000 --- a/excel_table_cnn/train_test_helpers/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -__all__ = ['dataset_loader', 'train_test_composer', 'cell_features', 'markup_loader'] - -from .dataset_loader import DatasetLoader -from .markup_loader import MarkupLoader -from .cell_features import get_table_features -from .train_test_composer import get_train_test diff --git a/excel_table_cnn/train_test_helpers/cell_features.py b/excel_table_cnn/train_test_helpers/cell_features.py deleted file mode 100644 index ec19dd1..0000000 --- a/excel_table_cnn/train_test_helpers/cell_features.py +++ /dev/null @@ -1,38 +0,0 @@ -import pandas as pd -import openpyxl - - -def get_cell_features_xlsx(cur_cell): - cell_features = { - "coordinate": cur_cell.coordinate, - "is_empty": cur_cell.value is None, - "is_string": cur_cell.data_type in ["s", "str"], - "is_merged": type(cur_cell).__name__ == "MergedCell", - "is_bold": cur_cell.font.b or False, - "is_italic": cur_cell.font.i or False, - "left_border": cur_cell.border.left is not None, - "right_border": cur_cell.border.right is not None, - "top_border": cur_cell.border.top is not None, - "bottom_border": cur_cell.border.bottom is not None, - "is_filled": cur_cell.fill.patternType is not None, - "horizontal_alignment": cur_cell.alignment.horizontal is not None, - "left_horizontal_alignment": cur_cell.alignment.horizontal == "left", - "right_horizontal_alignment": cur_cell.alignment.horizontal == "right", - "center_horizontal_alignment": cur_cell.alignment.horizontal == "center", - "wrapped_text": cur_cell.alignment.wrapText or False, - "indent": cur_cell.alignment.indent != 0, - "formula": cur_cell.data_type == "f", - } - return cell_features - - -def get_table_features(file_path, sheet_name) -> pd.DataFrame: - wb = openpyxl.load_workbook(file_path) - ws = wb[sheet_name] - data = [] - for row in ws.iter_rows(): - for cell in row: - data.append(get_cell_features_xlsx(cell)) - - result_df = pd.DataFrame(data) - return result_df diff --git a/excel_table_cnn/train_test_helpers/dataset_loader.py b/excel_table_cnn/train_test_helpers/dataset_loader.py deleted file mode 100644 index 9d08ba6..0000000 --- a/excel_table_cnn/train_test_helpers/dataset_loader.py +++ /dev/null @@ -1,85 +0,0 @@ -import os -import requests -import pandas as pd -from py7zr import unpack_7zarchive -import shutil - -class DatasetLoader: - def __init__(self, save_path): - # Where the datasets will be saved - self.save_path = save_path - - # URLs of datasets - self.datasets = { - 'VEnron2': 'https://figshare.com/ndownloader/files/8639470', - 'VFUSE': 'https://figshare.com/ndownloader/files/7889911', - 'VEUSES': 'https://figshare.com/ndownloader/files/7889902', - 'VEnron1.1': 'https://figshare.com/ndownloader/files/7889866', - 'VEnron1.0': 'https://figshare.com/ndownloader/files/7889947' - } - - def download_dataset(self, dataset_name): - # Check if dataset_name is valid - if dataset_name not in self.datasets: - print(f"Dataset {dataset_name} not found.") - return - - url = self.datasets[dataset_name] - response = requests.get(url, stream=True) - dataset_path = os.path.join(self.save_path, f"{dataset_name}.7z") - - with open(dataset_path, 'wb') as file: - for chunk in response.iter_content(chunk_size=1024): - if chunk: - file.write(chunk) - print(f"Dataset {dataset_name} downloaded successfully.") - - def unpack_dataset(self, dataset_name): - dataset_path = os.path.join(self.save_path, f"{dataset_name}.7z") - extract_path = os.path.join(self.save_path, dataset_name) - - if not os.path.exists(dataset_path): - print(f"Dataset {dataset_name} not found at {dataset_path}.") - return - - # Register the .7z format with shutil, only if it's not already registered - if '7zip' not in shutil._UNPACK_FORMATS: - shutil.register_unpack_format('7zip', ['.7z'], unpack_7zarchive) - - shutil.unpack_archive(dataset_path, extract_path, '7zip') - print(f"Dataset {dataset_name} unpacked successfully.") - - # Cleanup: Delete the .7z file after unpacking - os.remove(dataset_path) - print(f"{dataset_name}.7z removed successfully.") - - def get_dataset(self, dataset_name, check_exists=True): - # Construct the path to the dataset directory - dataset_dir = os.path.join(self.save_path, dataset_name) - - # Check if the dataset already exists - if check_exists and os.path.exists(dataset_dir): - print(f"Dataset {dataset_name} already exists at {dataset_dir}.") - return - - # Download and unpack the dataset - self.download_dataset(dataset_name) - self.unpack_dataset(dataset_name) - - def get_files(self, dataset_name): - dataset_dir = os.path.join(self.save_path, dataset_name) - - if not os.path.exists(dataset_dir): - print(f"Directory {dataset_dir} does not exist.") - return - - result = [] - for root, dirs, files in os.walk(dataset_dir): - for file in files: - relative_path = os.path.relpath(root, self.save_path) - result.append({ - "file_name": file, - "parent_path": relative_path - }) - - return pd.DataFrame(result) diff --git a/excel_table_cnn/train_test_helpers/train_test_composer.py b/excel_table_cnn/train_test_helpers/train_test_composer.py deleted file mode 100644 index 6cb4a71..0000000 --- a/excel_table_cnn/train_test_helpers/train_test_composer.py +++ /dev/null @@ -1,104 +0,0 @@ -import os -import pandas as pd -import subprocess -from tqdm import tqdm -from .dataset_loader import DatasetLoader -from .markup_loader import MarkupLoader -from .cell_features import get_table_features - - -def convert_file(file_path, output_dir): - output_file_path = os.path.splitext(file_path)[0] + '.xlsx' - if os.path.exists(output_file_path): - return # Skip if .xlsx file already exists - try: - subprocess.run([ - 'libreoffice', '--headless', '--convert-to', - 'xlsx', file_path, - '--outdir', output_dir - ], check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) - except subprocess.CalledProcessError as e: - print(f"Error converting {file_path}: {e}") - - -def convert_files(files_df, data_folder_path): - # Create a copy of the original DataFrame to preserve other columns - updated_files_df = files_df.copy() - - for index, row in tqdm(files_df.iterrows(), total=files_df.shape[0], desc="Converting Files"): - data_file_path = os.path.join(row['parent_path'], row['file_name']) - file_path = os.path.join(data_folder_path, data_file_path) - output_directory = os.path.join(data_folder_path, row['parent_path']) - file_name, file_ext = os.path.splitext(row['file_name']) - - if file_ext.lower() in ['.xls', '.xlsb']: - convert_file(file_path, output_directory) - # Update only the file name's extension to .xlsx - updated_files_df.at[index, 'file_name'] = file_name + '.xlsx' - - return updated_files_df - - -def extract_features(files_df, data_folder_path): - features_dfs = [] - # Iterate over the unique pairs - for _, row in tqdm(files_df.iterrows(), total=files_df.shape[0], desc="Extracting features from files"): - try: - file_path = os.path.join(row['parent_path'], row['file_name']) - features_df = get_table_features(os.path.join(data_folder_path, file_path), row['sheet_name']) - features_df["file_path"] = file_path - features_df["sheet_name"] = row['sheet_name'] - features_df["set_type"] = row["set_type"] - features_df["table_range"] = [row["table_range"] for _ in range(len(features_df.index))] - features_dfs.append(features_df) - except Exception as e: - print(f"Error extracting features from {file_path}: {e}") - return pd.concat(features_dfs, ignore_index=True) - - -def get_train_test(train_size=None, testing_size=None, - data_folder_path="./ExcelTableCNN/data/", - dataset_name="VEnron2", markup_name="tablesense", - backup_result=True): - print("Downloading dataset...") - dataset_loader = DatasetLoader(save_path=data_folder_path) - dataset_loader.get_dataset(dataset_name) - dataset_files = dataset_loader.get_files(dataset_name) - - print("Getting markup...") - markup_loader = MarkupLoader() - markup_files = markup_loader.get_markup(markup_name) - - # different extensions fix: - dataset_files["file_name_no_ext"] = dataset_files["file_name"].apply(lambda x: os.path.splitext(x)[0]) - markup_files["file_name_no_ext"] = markup_files["file_name"].apply(lambda x: os.path.splitext(x)[0]) - - files_df = markup_files.merge(dataset_files, how="inner", on=["file_name_no_ext", "parent_path"]) - files_df = files_df.drop(columns=["file_name_x", "file_name_no_ext"]) - files_df = files_df.rename(columns={"file_name_y": "file_name"}) - - if train_size is None: - training_samples = files_df[files_df["set_type"] == "training_set"] - else: - training_samples = files_df[files_df["set_type"] == "training_set"].sample(train_size) - if testing_size is None: - testing_samples = files_df[files_df["set_type"] == "testing_set"] - else: - testing_samples = files_df[files_df["set_type"] == "testing_set"].sample(testing_size) - files_df_sample = pd.concat([training_samples, testing_samples]) - - # Converting files - dataset_files_converted = convert_files(files_df_sample, data_folder_path) - - # Getting table features - features_df = extract_features(dataset_files_converted, data_folder_path) - - train_df = features_df[features_df["set_type"] == "training_set"].drop(columns=["set_type"]) - test_df = features_df[features_df["set_type"] == "testing_set"].drop(columns=["set_type"]) - - if backup_result: - print("Backing up results...") - train_df.to_pickle(os.path.join(data_folder_path, "train_features.pkl")) - test_df.to_pickle(os.path.join(data_folder_path, "test_features.pkl")) - - return train_df, test_df diff --git a/excel_table_cnn/training/__init__.py b/excel_table_cnn/training/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/excel_table_cnn/training/dataset.py b/excel_table_cnn/training/dataset.py new file mode 100644 index 0000000..321003c --- /dev/null +++ b/excel_table_cnn/training/dataset.py @@ -0,0 +1,97 @@ +"""Detection targets for spreadsheets. + +Box convention (used everywhere in this package): ``[x_min, y_min, x_max, +y_max]`` in cell units, zero-indexed, **half-open** — ``x_max``/``y_max`` are +one past the last column/row the table occupies. ``"A1:C3"`` maps to +``[0, 0, 3, 3]``; a single cell ``"A1:A1"`` maps to ``[0, 0, 1, 1]``, so every +valid table has strictly positive width and height (torchvision rejects +degenerate boxes). +""" + +import logging +from typing import Dict, List, Sequence + +import torch +from torch.utils.data import Dataset +from openpyxl.utils.cell import get_column_letter, range_boundaries + +logger = logging.getLogger(__name__) + + +def parse_table_range(table_range: str) -> List[float]: + """Excel range string -> half-open [x_min, y_min, x_max, y_max] box.""" + min_col, min_row, max_col, max_row = range_boundaries(table_range) + if None in (min_col, min_row, max_col, max_row): + raise ValueError(f"Range {table_range!r} is not a bounded cell range") + return [float(min_col - 1), float(min_row - 1), float(max_col), float(max_row)] + + +def box_to_range(box: Sequence[float]) -> str: + """Half-open box -> Excel range string (inverse of parse_table_range).""" + x_min, y_min, x_max, y_max = (int(round(float(v))) for v in box) + x_min, y_min = max(x_min, 0), max(y_min, 0) + x_max = max(x_max, x_min + 1) + y_max = max(y_max, y_min + 1) + return f"{get_column_letter(x_min + 1)}{y_min + 1}:{get_column_letter(x_max)}{y_max}" + + +def validate_boxes(boxes: torch.Tensor, height: int, width: int) -> torch.Tensor: + """Clamp boxes to the tensor bounds and drop degenerate ones.""" + if boxes.numel() == 0: + return boxes.reshape(0, 4) + boxes = boxes.clone() + boxes[:, 0::2] = boxes[:, 0::2].clamp(0, width) + boxes[:, 1::2] = boxes[:, 1::2].clamp(0, height) + keep = (boxes[:, 2] > boxes[:, 0]) & (boxes[:, 3] > boxes[:, 1]) + return boxes[keep] + + +class SpreadsheetDataset(Dataset): + """Dataset over featurized sheet samples. + + Each sample is a dict with keys ``tensor`` (C×H×W float32), ``boxes`` + (N×4 float32, half-open convention), ``file_path`` and ``sheet_name``. + Samples whose boxes are all invalid after clamping are skipped (recorded + in ``self.skipped``), not crashed on. + """ + + def __init__(self, samples: Sequence[Dict]): + self.samples: List[Dict] = [] + self.skipped: List[Dict] = [] + for sample in samples: + tensor = sample["tensor"] + _, height, width = tensor.shape + boxes = validate_boxes( + torch.as_tensor(sample["boxes"], dtype=torch.float32), height, width + ) + if len(boxes) == 0: + self.skipped.append(sample) + continue + self.samples.append({**sample, "boxes": boxes}) + if self.skipped: + logger.warning( + "Skipped %d sheet(s) with no valid table boxes (e.g. %s!%s)", + len(self.skipped), + self.skipped[0].get("file_path"), + self.skipped[0].get("sheet_name"), + ) + + @property + def num_cell_features(self) -> int: + return self.samples[0]["tensor"].shape[0] if self.samples else 0 + + def __len__(self) -> int: + return len(self.samples) + + def __getitem__(self, idx: int): + sample = self.samples[idx] + target = { + "boxes": sample["boxes"], + "labels": torch.ones((len(sample["boxes"]),), dtype=torch.int64), + } + return sample["tensor"], target + + +def collate_fn(batch): + """Detection collate: keep variable-size tensors as tuples.""" + return tuple(zip(*batch)) diff --git a/excel_table_cnn/training/train.py b/excel_table_cnn/training/train.py new file mode 100644 index 0000000..3d7eb4c --- /dev/null +++ b/excel_table_cnn/training/train.py @@ -0,0 +1,273 @@ +"""Training loop for the table detector. + +Every step logs all four Faster R-CNN loss components separately +(``loss_objectness``, ``loss_rpn_box_reg``, ``loss_classifier``, +``loss_box_reg``): a detector that silently stops learning (e.g. no positive +proposals) is visible in the logs instead of hiding inside one aggregate +number. +""" + +import argparse +import logging +import os +import random +from contextlib import nullcontext +from dataclasses import dataclass +from typing import Dict, List, Optional + +import numpy as np +import torch +from torch.utils.data import DataLoader + +from .dataset import SpreadsheetDataset, collate_fn +from ..data.features import FEATURE_NAMES, NUM_FEATURES +from ..device import resolve_amp, resolve_device +from ..model.detector import TableDetectionModel, build_model + +logger = logging.getLogger(__name__) + +LOSS_KEYS = ( + "loss_objectness", "loss_rpn_box_reg", "loss_classifier", "loss_box_reg", + "loss_pbr", # absent when the model is built with use_pbr=False +) + + +@dataclass +class TrainConfig: + epochs: int = 10 + lr: float = 0.005 + momentum: float = 0.9 + weight_decay: float = 5e-4 + warmup_steps: int = 100 + lr_step_size: Optional[int] = None # epochs between 10x LR decays; None = constant + grad_clip: float = 10.0 + amp: Optional[bool] = None # None = auto: on for CUDA, off for MPS/CPU + seed: int = 42 + device: str = "auto" # cuda -> mps -> cpu + checkpoint_dir: Optional[str] = None + log_every: int = 10 + + +def seed_everything(seed: int) -> None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + + +def make_dataloader(dataset: SpreadsheetDataset, shuffle: bool = True) -> DataLoader: + # Batch size 1: sheets have wildly different sizes (paper trains the same way). + return DataLoader(dataset, batch_size=1, shuffle=shuffle, collate_fn=collate_fn) + + +def save_checkpoint(model: TableDetectionModel, path: str, **extra) -> None: + os.makedirs(os.path.dirname(os.path.abspath(path)), exist_ok=True) + torch.save( + { + "state_dict": model.state_dict(), + "in_channels": model.in_channels, + "num_classes": model.num_classes, + "config": model.config, + "feature_names": list(FEATURE_NAMES), + **extra, + }, + path, + ) + + +def load_checkpoint(path: str, device: str = "cpu", **overrides) -> TableDetectionModel: + checkpoint = torch.load(path, map_location=device, weights_only=True) + kwargs = {**checkpoint.get("config", {}), **overrides} + model = build_model( + checkpoint["in_channels"], num_classes=checkpoint["num_classes"], **kwargs + ) + model.load_state_dict(checkpoint["state_dict"]) + return model.to(device) + + +def train_model( + model: TableDetectionModel, + dataset: SpreadsheetDataset, + config: TrainConfig, +) -> List[Dict[str, float]]: + """Train and return the per-step history of loss components.""" + seed_everything(config.seed) + device = resolve_device(config.device) + logger.info("Training on device: %s", device) + if device.type == "cuda": + # Allow TF32 matmuls on tensor-core GPUs — big speedup, no visible + # accuracy cost for detection training. + torch.set_float32_matmul_precision("high") + model.to(device) + model.train() + + optimizer = torch.optim.SGD( + model.parameters(), lr=config.lr, momentum=config.momentum, + weight_decay=config.weight_decay, + ) + scheduler = ( + torch.optim.lr_scheduler.StepLR(optimizer, step_size=config.lr_step_size, gamma=0.1) + if config.lr_step_size + else None + ) + use_amp = resolve_amp(config.amp, device) + scaler = torch.amp.GradScaler("cuda", enabled=use_amp) + + loader = make_dataloader(dataset) + history: List[Dict[str, float]] = [] + global_step = 0 + + for epoch in range(config.epochs): + epoch_sums: Dict[str, float] = {key: 0.0 for key in LOSS_KEYS} + epoch_total = 0.0 + + for images, targets in loader: + images = [image.to(device) for image in images] + targets = [{k: v.to(device) for k, v in t.items()} for t in targets] + + # Linear warmup: large detection losses on random weights can + # otherwise blow up the RPN in the first steps. + if global_step < config.warmup_steps: + warmup_factor = (global_step + 1) / config.warmup_steps + for group in optimizer.param_groups: + group["lr"] = config.lr * warmup_factor + + optimizer.zero_grad() + autocast = torch.amp.autocast("cuda") if use_amp else nullcontext() + with autocast: + loss_dict = model(images, targets) + total_loss = sum(loss_dict.values()) + + scaler.scale(total_loss).backward() + if config.grad_clip: + scaler.unscale_(optimizer) + torch.nn.utils.clip_grad_norm_(model.parameters(), config.grad_clip) + scaler.step(optimizer) + scaler.update() + + record = {key: float(loss_dict[key]) for key in LOSS_KEYS if key in loss_dict} + record.update( + epoch=epoch, + step=global_step, + loss_total=float(total_loss), + lr=optimizer.param_groups[0]["lr"], + ) + history.append(record) + for key in LOSS_KEYS: + epoch_sums[key] += record.get(key, 0.0) + epoch_total += record["loss_total"] + + if config.log_every and global_step % config.log_every == 0: + components = ", ".join( + f"{key.removeprefix('loss_')}={record.get(key, 0.0):.4f}" + for key in LOSS_KEYS + ) + logger.info( + "epoch %d step %d: loss=%.4f (%s)", + epoch, global_step, record["loss_total"], components, + ) + global_step += 1 + + if scheduler is not None: + scheduler.step() + + n = max(len(loader), 1) + components = ", ".join( + f"{key.removeprefix('loss_')}={epoch_sums[key] / n:.4f}" for key in LOSS_KEYS + ) + logger.info( + "Epoch %d/%d: mean loss=%.4f (%s)", + epoch + 1, config.epochs, epoch_total / n, components, + ) + + if config.checkpoint_dir: + save_checkpoint( + model, os.path.join(config.checkpoint_dir, "last.pt"), + epoch=epoch, mean_loss=epoch_total / n, + ) + + return history + + +def main(argv: Optional[List[str]] = None) -> None: + parser = argparse.ArgumentParser( + description="Train the ExcelTableCNN table detector on VEnron2." + ) + parser.add_argument("--data-dir", default="./data", help="dataset/download directory") + parser.add_argument("--cache-dir", default=None, + help="feature cache directory (default: /feature_cache)") + parser.add_argument("--dataset", default="VEnron2") + parser.add_argument("--train-size", type=int, default=None, + help="subsample N training sheets (default: all)") + parser.add_argument("--test-size", type=int, default=None) + parser.add_argument("--epochs", type=int, default=10) + parser.add_argument("--lr", type=float, default=0.005) + parser.add_argument("--lr-step-size", type=int, default=None, + help="epochs between 10x LR decays (default: constant LR)") + parser.add_argument("--device", default="auto", + help="cpu, cuda, mps or auto (auto = cuda if available, else cpu)") + parser.add_argument("--checkpoint-dir", default="./checkpoints") + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--amp", action=argparse.BooleanOptionalAction, default=None, + help="mixed precision; default: on for CUDA, off otherwise") + parser.add_argument("--use-libreoffice", action="store_true", + help="convert legacy files to .xlsx via LibreOffice instead of " + "reading .xls natively (required only for .xlsb)") + parser.add_argument("--max-rows", type=int, default=None, + help="cap featurized sheet height (cells); lower = faster/less RAM") + parser.add_argument("--max-cols", type=int, default=None, + help="cap featurized sheet width (cells)") + parser.add_argument("--pbr", action=argparse.BooleanOptionalAction, default=True, + help="PBR boundary-snapping head (--no-pbr to ablate)") + parser.add_argument("--grid-context", action=argparse.BooleanOptionalAction, + default=True, + help="grid-context backbone (--no-grid-context to ablate)") + parser.add_argument("--no-eval", action="store_true", + help="skip evaluation on the test split after training") + args = parser.parse_args(argv) + + logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s") + + from ..data.features import DEFAULT_MAX_COLS, DEFAULT_MAX_ROWS + from ..data.pipeline import get_train_test + from ..evaluation.evaluate import evaluate_model, format_report + + device = resolve_device(args.device) + + train_samples, test_samples = get_train_test( + data_folder_path=args.data_dir, + dataset_name=args.dataset, + train_size=args.train_size, + testing_size=args.test_size, + cache_dir=args.cache_dir, + seed=args.seed, + use_libreoffice=args.use_libreoffice, + max_rows=args.max_rows or DEFAULT_MAX_ROWS, + max_cols=args.max_cols or DEFAULT_MAX_COLS, + ) + train_dataset = SpreadsheetDataset(train_samples) + logger.info("Training on %d sheets (skipped %d)", + len(train_dataset), len(train_dataset.skipped)) + + model = build_model( + in_channels=NUM_FEATURES, use_pbr=args.pbr, use_grid_context=args.grid_context + ) + config = TrainConfig( + epochs=args.epochs, lr=args.lr, lr_step_size=args.lr_step_size, + device=str(device), seed=args.seed, + amp=args.amp, checkpoint_dir=args.checkpoint_dir, + ) + train_model(model, train_dataset, config) + final_path = os.path.join(args.checkpoint_dir, "final.pt") + save_checkpoint(model, final_path) + logger.info("Saved final checkpoint to %s", final_path) + + if not args.no_eval and test_samples: + test_dataset = SpreadsheetDataset(test_samples) + report = evaluate_model(model, test_dataset, device=str(device)) + print(format_report(report)) + + +if __name__ == "__main__": + main() diff --git a/notebooks/train_kaggle_colab.ipynb b/notebooks/train_kaggle_colab.ipynb new file mode 100644 index 0000000..8966d7c --- /dev/null +++ b/notebooks/train_kaggle_colab.ipynb @@ -0,0 +1,132 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": "# ExcelTableCNN — training runbook (Kaggle / Colab)\n\nTrains the table detector on the VEnron2 corpus with its public table-range annotations.\n\n**Before running:**\n- Enable a GPU (Kaggle: *Settings → Accelerator → GPU*; Colab: *Runtime → Change runtime type → GPU*). CUDA and mixed precision are picked up automatically (`device=\"auto\"`).\n- The first run downloads the corpus (~hundreds of MB) and featurizes it — `.xls` files are read natively via xlrd, **no LibreOffice needed**. Featurized tensors are cached, so reruns are fast.\n\n**Where things land:** on Kaggle use `/kaggle/working` (persisted with the notebook output); on Colab mount Drive if you want the feature cache and checkpoints to survive the session." + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "# Environment setup — just the package; .xls files are read natively.\n# (LibreOffice is only needed for .xlsb corpora: !apt-get install -y -qq libreoffice,\n# then pass use_libreoffice=True to get_train_test.)\n%pip install -q git+https://github.com/Flagro/ExcelTableCNN.git" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import logging, os\n", + "logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s')\n", + "\n", + "WORK_DIR = '/kaggle/working' if os.path.isdir('/kaggle/working') else '.'\n", + "DATA_DIR = os.path.join(WORK_DIR, 'data')\n", + "CHECKPOINT_DIR = os.path.join(WORK_DIR, 'checkpoints')\n", + "\n", + "# On Colab, optionally persist across sessions:\n", + "# from google.colab import drive\n", + "# drive.mount('/content/drive')\n", + "# DATA_DIR = '/content/drive/MyDrive/exceltablecnn/data'\n", + "# CHECKPOINT_DIR = '/content/drive/MyDrive/exceltablecnn/checkpoints'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download the corpus + annotations and build cached feature tensors.\n", + "# Start small (50/20 sheets); raise or set to None (= all) once the run works.\n", + "from excel_table_cnn import get_train_test\n", + "\n", + "train_samples, test_samples = get_train_test(\n", + " data_folder_path=DATA_DIR,\n", + " train_size=50,\n", + " testing_size=20,\n", + ")\n", + "len(train_samples), len(test_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "from excel_table_cnn import (\n NUM_FEATURES, SpreadsheetDataset, TrainConfig, build_model, train_model,\n)\n\ntrain_dataset = SpreadsheetDataset(train_samples)\nprint(f'{len(train_dataset)} training sheets ({len(train_dataset.skipped)} skipped)')\n\nmodel = build_model(in_channels=NUM_FEATURES)\nconfig = TrainConfig(\n epochs=20,\n lr=0.005,\n # device=\"auto\" resolves to CUDA when available (else CPU); AMP follows suit.\n checkpoint_dir=CHECKPOINT_DIR,\n)\nhistory = train_model(model, train_dataset, config)" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Watch the loss components: if loss_objectness or loss_classifier sit at ~0\n", + "# from the very start, something upstream is broken (see project docs).\n", + "import pandas as pd\n", + "history_df = pd.DataFrame(history)\n", + "history_df[['loss_total', 'loss_objectness', 'loss_rpn_box_reg',\n", + " 'loss_classifier', 'loss_box_reg']].rolling(20).mean().plot(figsize=(10, 4));" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "# Evaluate with the Error-of-Boundary metric.\nfrom excel_table_cnn import SpreadsheetDataset, evaluate_model, format_report\n\ntest_dataset = SpreadsheetDataset(test_samples)\nreport = evaluate_model(model, test_dataset) # device=\"auto\"\nprint(format_report(report))" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Error analysis: worst sheets first (highest best-EoB per ground-truth table).\n", + "worst = sorted(\n", + " report['per_sheet'],\n", + " key=lambda s: max((e for e in s['best_eob_per_gt'] if e is not None), default=999),\n", + " reverse=True,\n", + ")\n", + "for sheet in worst[:5]:\n", + " print(sheet['file_path'], sheet['sheet_name'])\n", + " print(' ground truth:', sheet['ground_truth'])\n", + " print(' predictions :', [(p['range'], round(p['score'], 2)) for p in sheet['predictions']])\n", + " print(' best EoB :', sheet['best_eob_per_gt'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "# Inference on any .xlsx/.xlsm/.xls file with the trained model:\n# from excel_table_cnn import detect_tables\n# detect_tables('some_file.xls', model=model)" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scaling up\n", + "\n", + "- Set `train_size=None` to use every annotated training sheet; raise `epochs` accordingly.\n", + "- Checkpoints: `checkpoints/last.pt` is refreshed every epoch — reload with `excel_table_cnn.load_checkpoint(path)`.\n", + "- If GPU memory is tight, lower the featurization caps: `get_train_test(..., max_rows=1024, max_cols=256)`." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..a7c5d7c --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,54 @@ +[build-system] +requires = ["setuptools>=68"] +build-backend = "setuptools.build_meta" + +[project] +name = "excel-table-cnn" +version = "0.3.0" +description = "Open-source spreadsheet table detection with CNNs" +readme = "README.md" +license = { text = "MIT" } +authors = [{ name = "Anton Potapov" }] +requires-python = ">=3.10" +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3", + "Topic :: Scientific/Engineering :: Artificial Intelligence", +] +dependencies = [ + "torch>=2.0", + "torchvision>=0.15", + "openpyxl>=3.1", + "xlrd>=2.0", + "numpy>=1.24", + "pandas>=2.0", + "tqdm>=4.60", + "requests>=2.28", + "py7zr>=0.20", +] + +[project.optional-dependencies] +dev = [ + "pytest>=8.0", + "xlwt>=1.3", # writes .xls fixtures for the xlrd-featurizer tests +] + +[project.scripts] +excel-table-cnn-train = "excel_table_cnn.training.train:main" +excel-table-cnn-eval = "excel_table_cnn.evaluation.evaluate:main" +excel-table-cnn-detect = "excel_table_cnn.inference:main" + +[project.urls] +Repository = "https://github.com/Flagro/ExcelTableCNN" + +[tool.setuptools.packages.find] +include = ["excel_table_cnn*"] + +[tool.pytest.ini_options] +testpaths = ["tests"] +markers = [ + "slow: long-running tests (overfit smoke test); deselect with -m 'not slow'", +] diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 0b0f7c4..0000000 --- a/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -py7zr -openpyxl -pandas -numpy -tqdm diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..334c67b --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,153 @@ +"""Shared fixtures: a crafted .xlsx workbook with known cell features, and a +synthetic tensor sample factory for model tests that don't need Excel I/O.""" + +import datetime + +import pytest +import torch +from openpyxl import Workbook +from openpyxl.styles import Alignment, Border, Font, PatternFill, Side + +from excel_table_cnn.data.features import FEATURE_NAMES + +THIN = Side(style="thin") +THIN_BORDER = Border(left=THIN, right=THIN, top=THIN, bottom=THIN) +FILL = PatternFill(fill_type="solid", fgColor="FFFF00") + +# Ground-truth table range of the toy workbook (headers B2:D2 + data B3:D6). +TOY_TABLE_RANGE = "B2:D6" +TOY_SHEET = "Data" + + +def feature_index(name: str) -> int: + return FEATURE_NAMES.index(name) + + +@pytest.fixture() +def toy_workbook_path(tmp_path): + """A workbook exercising every feature channel at known coordinates.""" + wb = Workbook() + ws = wb.active + ws.title = TOY_SHEET + + # A 3-column table at B2:D6: bold, bordered, filled, centered headers. + for col, header in zip("BCD", ["Name", "Qty", "Price"]): + cell = ws[f"{col}2"] + cell.value = header + cell.font = Font(b=True) + cell.border = THIN_BORDER + cell.fill = FILL + cell.alignment = Alignment(horizontal="center") + for row in range(3, 7): + ws[f"B{row}"] = f"item-{row}" + ws[f"C{row}"] = row * 10 + ws[f"D{row}"] = row * 1.5 + + # Merged region F2:G3, value on the anchor cell only. + ws["F2"] = "merged title" + ws.merge_cells("F2:G3") + + # Assorted single-feature cells. + ws["F6"] = "italic" + ws["F6"].font = Font(i=True) + ws["B8"] = "=SUM(C3:C6)" # formula + ws["F8"] = "wrapped text cell" + ws["F8"].alignment = Alignment(wrapText=True) + ws["G8"] = "indented" + ws["G8"].alignment = Alignment(indent=1) + ws["F10"] = "left" + ws["F10"].alignment = Alignment(horizontal="left") + ws["G10"] = "right" + ws["G10"].alignment = Alignment(horizontal="right") + + # v2 feature cells: formats, colors, merge directions, text stats. + ws["B12"] = 0.125 + ws["B12"].number_format = "0.00%" # numeric (percent) format + ws["C12"] = 3.14 # decimal point in value text + ws["D12"] = datetime.date(2001, 3, 14) + ws["D12"].number_format = "yyyy-mm-dd" + ws["E12"] = datetime.time(9, 30) + ws["E12"].number_format = "hh:mm" + ws["F12"] = "red text" + ws["F12"].font = Font(color="FFFF0000") + ws["G12"] = "12.5%" # percent symbol in value text + ws["B14"] = "wide merge" + ws.merge_cells("B14:D14") + ws["F14"] = "tall merge" + ws.merge_cells("F14:F16") + + path = tmp_path / "toy.xlsx" + wb.save(path) + wb.close() + return str(path) + + +@pytest.fixture() +def toy_xls_path(tmp_path): + """Legacy .xls twin of the toy workbook (written with xlwt, read with + xlrd — no LibreOffice involved). Mirrors ``toy_workbook_path`` cell for + cell, except the formula cell: the formula channel is undetectable in + .xls and stays 0.""" + import xlwt + + wb = xlwt.Workbook() + ws = wb.add_sheet(TOY_SHEET) + + header_style = xlwt.easyxf( + "font: bold on; " + "borders: left thin, right thin, top thin, bottom thin; " + "pattern: pattern solid, fore_colour yellow; " + "align: horiz center" + ) + for col, header in zip((1, 2, 3), ["Name", "Qty", "Price"]): + ws.write(1, col, header, header_style) # row 2 in Excel terms + for row in range(2, 6): # Excel rows 3..6 + ws.write(row, 1, f"item-{row + 1}") + ws.write(row, 2, (row + 1) * 10) + ws.write(row, 3, (row + 1) * 1.5) + + ws.write_merge(1, 2, 5, 6, "merged title") # F2:G3 + ws.write(5, 5, "italic", xlwt.easyxf("font: italic on")) # F6 + ws.write(7, 5, "wrapped text cell", xlwt.easyxf("align: wrap on")) # F8 + ws.write(7, 6, "indented", xlwt.easyxf("align: indent 1")) # G8 + ws.write(9, 5, "left", xlwt.easyxf("align: horiz left")) # F10 + ws.write(9, 6, "right", xlwt.easyxf("align: horiz right")) # G10 + + # v2 feature cells, mirroring the xlsx fixture. + ws.write(11, 1, 0.125, xlwt.easyxf(num_format_str="0.00%")) # B12 + ws.write(11, 2, 3.14) # C12 + ws.write(11, 3, datetime.date(2001, 3, 14), + xlwt.easyxf(num_format_str="YYYY-MM-DD")) # D12 + ws.write(11, 4, datetime.time(9, 30), xlwt.easyxf(num_format_str="hh:mm")) # E12 + ws.write(11, 5, "red text", xlwt.easyxf("font: colour red")) # F12 + ws.write(11, 6, "12.5%") # G12 + ws.write_merge(13, 13, 1, 3, "wide merge") # B14:D14 + ws.write_merge(13, 15, 5, 5, "tall merge") # F14:F16 + + path = tmp_path / "toy.xls" + wb.save(str(path)) + return str(path) + + +def make_synthetic_sample(height=30, width=15, box=(3, 5, 11, 21)): + """A sheet tensor with one clearly featurized table at ``box`` (half-open + [x_min, y_min, x_max, y_max]). Outside the box: empty cells.""" + tensor = torch.zeros((len(FEATURE_NAMES), height, width), dtype=torch.float32) + tensor[feature_index("is_empty")] = 1.0 + + x_min, y_min, x_max, y_max = box + tensor[feature_index("is_empty"), y_min:y_max, x_min:x_max] = 0.0 + tensor[feature_index("is_string"), y_min:y_max, x_min:x_max] = 1.0 + # Header row: bold + filled + bordered. + tensor[feature_index("is_bold"), y_min, x_min:x_max] = 1.0 + tensor[feature_index("is_filled"), y_min, x_min:x_max] = 1.0 + tensor[feature_index("top_border"), y_min, x_min:x_max] = 1.0 + tensor[feature_index("bottom_border"), y_max - 1, x_min:x_max] = 1.0 + + return { + "tensor": tensor, + "boxes": torch.tensor([list(map(float, box))], dtype=torch.float32), + "file_path": "", + "sheet_name": "synthetic", + "feature_names": list(FEATURE_NAMES), + } diff --git a/tests/test_box_convention.py b/tests/test_box_convention.py new file mode 100644 index 0000000..a4b2ed7 --- /dev/null +++ b/tests/test_box_convention.py @@ -0,0 +1,61 @@ +import pytest +import torch + +from excel_table_cnn.training.dataset import ( + box_to_range, + parse_table_range, + validate_boxes, +) + + +def test_parse_basic(): + assert parse_table_range("A1:C3") == [0.0, 0.0, 3.0, 3.0] + + +def test_parse_single_cell_has_positive_extent(): + # Regression: the old inclusive convention produced zero-area boxes here. + box = parse_table_range("A1:A1") + assert box == [0.0, 0.0, 1.0, 1.0] + assert box[2] > box[0] and box[3] > box[1] + + +def test_parse_single_column_has_positive_width(): + box = parse_table_range("A1:A5") + assert box == [0.0, 0.0, 1.0, 5.0] + assert box[2] > box[0] + + +@pytest.mark.parametrize("rng", ["A1:C3", "B2:H45", "AA10:AB12", "A1:A1", "D7:D20"]) +def test_round_trip(rng): + assert box_to_range(parse_table_range(rng)) == rng + + +def test_box_to_range_rounds_float_predictions(): + assert box_to_range([0.3, 0.4, 2.8, 3.2]) == "A1:C3" + + +def test_parse_unbounded_range_raises(): + with pytest.raises(ValueError): + parse_table_range("A:C") + + +def test_parse_garbage_raises(): + with pytest.raises(ValueError): + parse_table_range("not a range") + + +def test_validate_boxes_clamps_out_of_bounds(): + boxes = torch.tensor([[5.0, 5.0, 50.0, 50.0]]) + result = validate_boxes(boxes, height=20, width=10) + assert result.tolist() == [[5.0, 5.0, 10.0, 20.0]] + + +def test_validate_boxes_drops_degenerate(): + boxes = torch.tensor([[3.0, 3.0, 3.0, 10.0], [1.0, 1.0, 4.0, 4.0]]) + result = validate_boxes(boxes, height=20, width=10) + assert result.tolist() == [[1.0, 1.0, 4.0, 4.0]] + + +def test_validate_boxes_empty_input(): + result = validate_boxes(torch.empty((0, 4)), height=5, width=5) + assert result.shape == (0, 4) diff --git a/tests/test_census.py b/tests/test_census.py new file mode 100644 index 0000000..da98ae5 --- /dev/null +++ b/tests/test_census.py @@ -0,0 +1,35 @@ +import pandas as pd + +from excel_table_cnn.data.census import box_census, collect_box_dims, lattice_coverage +from excel_table_cnn.model.rcnn import DEFAULT_ANCHOR_SIZES, DEFAULT_ASPECT_RATIOS + + +def toy_markup(): + return pd.DataFrame( + {"table_range": [["A1:C3", "B2:B21"], ["A1:J2", "bogus", ""]]} + ) + + +def test_collect_box_dims_parses_and_skips_garbage(): + dims = collect_box_dims(toy_markup()) + assert dims == [(3.0, 3.0), (1.0, 20.0), (10.0, 2.0)] + + +def test_box_census_percentiles(): + stats = box_census(toy_markup()) + assert stats["width"]["p50"] == 3.0 + assert stats["hw_ratio"]["p99"] == 20.0 # the 1x20 column table + + +def test_perfect_lattice_covers_exactly(): + dims = [(4.0, 4.0)] + coverage = lattice_coverage(dims, sizes=[4], ratios=[1.0]) + assert coverage[0.5] == 1.0 and coverage[0.7] == 1.0 + + +def test_default_lattice_covers_the_toy_shapes(): + dims = collect_box_dims(toy_markup()) + coverage = lattice_coverage( + dims, sizes=DEFAULT_ANCHOR_SIZES[0], ratios=DEFAULT_ASPECT_RATIOS[0] + ) + assert coverage[0.5] == 1.0 # tall 1x20 boxes must be covered (ratio 22/45) diff --git a/tests/test_dataset.py b/tests/test_dataset.py new file mode 100644 index 0000000..ce4a281 --- /dev/null +++ b/tests/test_dataset.py @@ -0,0 +1,41 @@ +import torch + +from excel_table_cnn.data.features import NUM_FEATURES +from excel_table_cnn.training.dataset import SpreadsheetDataset, collate_fn +from .conftest import make_synthetic_sample + + +def test_getitem_shapes_and_labels(): + sample = make_synthetic_sample(height=20, width=10, box=(2, 3, 8, 9)) + dataset = SpreadsheetDataset([sample]) + tensor, target = dataset[0] + assert tensor.shape == (NUM_FEATURES, 20, 10) + assert tensor.dtype == torch.float32 + assert target["boxes"].shape == (1, 4) + assert target["labels"].tolist() == [1] + assert dataset.num_cell_features == NUM_FEATURES + + +def test_out_of_bounds_boxes_clamped(): + sample = make_synthetic_sample(height=20, width=10, box=(2, 3, 8, 9)) + sample["boxes"] = torch.tensor([[5.0, 5.0, 50.0, 50.0]]) + dataset = SpreadsheetDataset([sample]) + _, target = dataset[0] + assert target["boxes"].tolist() == [[5.0, 5.0, 10.0, 20.0]] + + +def test_sheet_with_only_degenerate_boxes_is_skipped(): + good = make_synthetic_sample(height=20, width=10, box=(2, 3, 8, 9)) + bad = make_synthetic_sample(height=20, width=10, box=(2, 3, 8, 9)) + bad["boxes"] = torch.tensor([[3.0, 3.0, 3.0, 10.0]]) + dataset = SpreadsheetDataset([good, bad]) + assert len(dataset) == 1 + assert len(dataset.skipped) == 1 + + +def test_collate_keeps_variable_sizes(): + a = SpreadsheetDataset([make_synthetic_sample()])[0] + b = SpreadsheetDataset([make_synthetic_sample(height=12, width=8, box=(1, 1, 5, 6))])[0] + images, targets = collate_fn([a, b]) + assert len(images) == 2 and len(targets) == 2 + assert images[0].shape != images[1].shape diff --git a/tests/test_device.py b/tests/test_device.py new file mode 100644 index 0000000..daff541 --- /dev/null +++ b/tests/test_device.py @@ -0,0 +1,33 @@ +import torch + +from excel_table_cnn.device import resolve_amp, resolve_device + + +def test_explicit_devices_pass_through(): + assert resolve_device("cpu") == torch.device("cpu") + assert resolve_device(torch.device("cpu")) == torch.device("cpu") + + +def test_auto_prefers_cuda_else_cpu_never_mps(): + device = resolve_device("auto") + expected = "cuda" if torch.cuda.is_available() else "cpu" + assert device.type == expected + assert resolve_device(None) == device + + +def test_mps_is_explicit_opt_in(): + # Constructing the device object works regardless of availability. + assert resolve_device("mps").type == "mps" + + +def test_amp_auto_only_on_cuda(): + assert resolve_amp(None, torch.device("cuda")) is True + assert resolve_amp(None, torch.device("cpu")) is False + assert resolve_amp(None, torch.device("mps")) is False + + +def test_amp_never_forced_off_cuda(): + # Even an explicit True is clamped off-CUDA: GradScaler needs CUDA. + assert resolve_amp(True, torch.device("cpu")) is False + assert resolve_amp(True, torch.device("mps")) is False + assert resolve_amp(False, torch.device("cuda")) is False diff --git a/tests/test_eob.py b/tests/test_eob.py new file mode 100644 index 0000000..c57aeea --- /dev/null +++ b/tests/test_eob.py @@ -0,0 +1,66 @@ +from excel_table_cnn.evaluation.eob import eob, eob_precision_recall, match_detections + + +def test_eob_exact_match(): + assert eob([0, 0, 3, 3], [0, 0, 3, 3]) == 0 + + +def test_eob_is_max_deviation(): + assert eob([0, 0, 5, 3], [0, 0, 3, 3]) == 2 + assert eob([1, 0, 4, 3], [0, 0, 3, 3]) == 1 + assert eob([2, 5, 9, 20], [3, 5, 11, 21]) == 2 + + +def test_match_exact_detections(): + gts = [[0, 0, 3, 3], [10, 10, 20, 20]] + preds = [[0, 0, 3, 3], [10, 10, 20, 20]] + scores = [0.9, 0.8] + assert match_detections(preds, scores, gts, threshold=0) == (2, 0, 0) + + +def test_duplicate_detection_counts_as_fp(): + gts = [[0, 0, 3, 3]] + preds = [[0, 0, 3, 3], [0, 0, 3, 3]] + scores = [0.9, 0.8] + assert match_detections(preds, scores, gts, threshold=0) == (1, 1, 0) + + +def test_missed_gt_counts_as_fn(): + gts = [[0, 0, 3, 3], [10, 10, 20, 20]] + preds = [[0, 0, 3, 3]] + scores = [0.9] + assert match_detections(preds, scores, gts, threshold=0) == (1, 0, 1) + + +def test_near_miss_passes_only_looser_threshold(): + gts = [[0, 0, 3, 3]] + preds = [[0, 0, 5, 3]] # EoB = 2 + scores = [0.9] + assert match_detections(preds, scores, gts, threshold=0) == (0, 1, 1) + assert match_detections(preds, scores, gts, threshold=2) == (1, 0, 0) + + +def test_precision_recall_aggregates_over_sheets(): + per_sheet_preds = [ + ([[0, 0, 3, 3]], [0.9]), # sheet 1: exact hit + ([[0, 0, 9, 9], [40, 40, 50, 50]], [0.8, 0.7]), # sheet 2: 1 hit + 1 FP + ([], []), # sheet 3: missed its table + ] + per_sheet_gts = [ + [[0, 0, 3, 3]], + [[0, 0, 9, 9]], + [[5, 5, 8, 8]], + ] + report = eob_precision_recall(per_sheet_preds, per_sheet_gts, thresholds=(0, 2)) + assert report["eob0"]["tp"] == 2 + assert report["eob0"]["fp"] == 1 + assert report["eob0"]["fn"] == 1 + assert report["eob0"]["precision"] == 2 / 3 + assert report["eob0"]["recall"] == 2 / 3 + assert report["eob2"] == report["eob0"] # same outcome at looser threshold here + + +def test_no_predictions_no_gt(): + report = eob_precision_recall([([], [])], [[]], thresholds=(0,)) + assert report["eob0"]["precision"] == 0.0 + assert report["eob0"]["recall"] == 0.0 diff --git a/tests/test_evaluate.py b/tests/test_evaluate.py new file mode 100644 index 0000000..75c4b2f --- /dev/null +++ b/tests/test_evaluate.py @@ -0,0 +1,68 @@ +import torch + +from excel_table_cnn.evaluation.evaluate import evaluate_model, format_report +from excel_table_cnn.training.dataset import SpreadsheetDataset +from .conftest import make_synthetic_sample + + +class OracleModel(torch.nn.Module): + """Fake detector that returns configured boxes — lets the harness be + tested independently of a real model.""" + + def __init__(self, boxes, scores): + super().__init__() + self._boxes = boxes + self._scores = scores + + def forward(self, images): + return [ + { + "boxes": torch.tensor(self._boxes, dtype=torch.float32), + "scores": torch.tensor(self._scores, dtype=torch.float32), + "labels": torch.ones(len(self._boxes), dtype=torch.int64), + } + ] + + +def test_perfect_oracle_scores_perfectly(): + sample = make_synthetic_sample(box=(3, 5, 11, 21)) + dataset = SpreadsheetDataset([sample]) + model = OracleModel([[3, 5, 11, 21]], [0.99]) + + report = evaluate_model(model, dataset, score_threshold=0.5) + assert report["eob0"] == {"precision": 1.0, "recall": 1.0, "tp": 1, "fp": 0, "fn": 0} + assert report["n_sheets"] == 1 + assert report["n_gt"] == 1 + detail = report["per_sheet"][0] + assert detail["predictions"][0]["range"] == detail["ground_truth"][0] + assert detail["best_eob_per_gt"] == [0] + + +def test_near_miss_counts_at_eob2_only(): + sample = make_synthetic_sample(box=(3, 5, 11, 21)) + dataset = SpreadsheetDataset([sample]) + model = OracleModel([[3, 5, 13, 21]], [0.9]) # right edge off by 2 + + report = evaluate_model(model, dataset, score_threshold=0.5) + assert report["eob0"]["recall"] == 0.0 + assert report["eob2"]["recall"] == 1.0 + assert report["per_sheet"][0]["best_eob_per_gt"] == [2] + + +def test_low_scores_are_filtered(): + sample = make_synthetic_sample(box=(3, 5, 11, 21)) + dataset = SpreadsheetDataset([sample]) + model = OracleModel([[3, 5, 11, 21]], [0.3]) + + report = evaluate_model(model, dataset, score_threshold=0.5) + assert report["eob0"]["fn"] == 1 + assert report["per_sheet"][0]["predictions"] == [] + + +def test_format_report_is_readable(): + sample = make_synthetic_sample(box=(3, 5, 11, 21)) + dataset = SpreadsheetDataset([sample]) + model = OracleModel([[3, 5, 11, 21]], [0.99]) + text = format_report(evaluate_model(model, dataset)) + assert "EoB-0" in text and "EoB-2" in text + assert "precision=1.000" in text diff --git a/tests/test_features.py b/tests/test_features.py new file mode 100644 index 0000000..ad57608 --- /dev/null +++ b/tests/test_features.py @@ -0,0 +1,195 @@ +import numpy as np +import openpyxl +import pytest +from openpyxl import Workbook + +from excel_table_cnn.data.features import ( + FEATURE_NAMES, + NUM_FEATURES, + default_cell_vector, + featurize_sheet, + load_sheet_features, +) +from .conftest import TOY_SHEET, feature_index + + +def cell(arr, ref): + """Feature vector of an Excel-style cell reference in the array.""" + from openpyxl.utils.cell import coordinate_to_tuple + + row, col = coordinate_to_tuple(ref) + return arr[row - 1, col - 1] + + +@pytest.fixture() +def toy_features(toy_workbook_path): + return load_sheet_features(toy_workbook_path, TOY_SHEET) + + +def test_shape_and_dtype(toy_features): + assert toy_features.ndim == 3 + assert toy_features.shape[2] == NUM_FEATURES + assert toy_features.dtype == np.float32 + # Used range reaches G10 at least (content), so >= 10 rows and 7 cols. + assert toy_features.shape[0] >= 10 + assert toy_features.shape[1] >= 7 + + +def test_header_cell_features(toy_features): + b2 = cell(toy_features, "B2") + assert b2[feature_index("is_empty")] == 0 + assert b2[feature_index("is_string")] == 1 + assert b2[feature_index("is_bold")] == 1 + assert b2[feature_index("is_filled")] == 1 + for side in ("left_border", "right_border", "top_border", "bottom_border"): + assert b2[feature_index(side)] == 1 + assert b2[feature_index("horizontal_alignment")] == 1 + assert b2[feature_index("center_horizontal_alignment")] == 1 + assert b2[feature_index("left_horizontal_alignment")] == 0 + assert b2[feature_index("formula")] == 0 + + +def test_numeric_cell_is_not_string(toy_features): + c3 = cell(toy_features, "C3") + assert c3[feature_index("is_empty")] == 0 + assert c3[feature_index("is_string")] == 0 + + +def test_untouched_cell_is_default(toy_features): + e4 = cell(toy_features, "E4") + assert (e4 == default_cell_vector()).all() + + +def test_borders_not_constant_true(toy_features): + """Regression for the openpyxl border bug: default cells must NOT report + borders (``cell.border.left`` is always a Side object; only its style + indicates a drawn border).""" + e4 = cell(toy_features, "E4") + for side in ("left_border", "right_border", "top_border", "bottom_border"): + assert e4[feature_index(side)] == 0 + border_channel = toy_features[:, :, feature_index("left_border")] + assert border_channel.sum() < border_channel.size # not constant 1 + + +def test_merged_cells(toy_features): + assert cell(toy_features, "F2")[feature_index("is_merged")] == 1 # anchor + assert cell(toy_features, "G3")[feature_index("is_merged")] == 1 # covered + assert cell(toy_features, "G3")[feature_index("is_empty")] == 1 # no own value + assert cell(toy_features, "B2")[feature_index("is_merged")] == 0 + + +def test_assorted_features(toy_features): + assert cell(toy_features, "F6")[feature_index("is_italic")] == 1 + assert cell(toy_features, "B8")[feature_index("formula")] == 1 + assert cell(toy_features, "B8")[feature_index("is_string")] == 0 + assert cell(toy_features, "F8")[feature_index("wrapped_text")] == 1 + assert cell(toy_features, "G8")[feature_index("indent")] == 1 + assert cell(toy_features, "F10")[feature_index("left_horizontal_alignment")] == 1 + assert cell(toy_features, "G10")[feature_index("right_horizontal_alignment")] == 1 + + +def test_trailing_default_region_is_trimmed(): + wb = Workbook() + ws = wb.active + for ref in ("A1", "B2", "C3"): + ws[ref] = "x" + ws["J20"] = None # creates a default cell far away, inflating the used range + assert ws.max_row == 20 and ws.max_column == 10 + + arr = featurize_sheet(ws) + # Content ends at C3; TRIM_MARGIN=2 keeps two extra rows/cols. + assert arr.shape[:2] == (5, 5) + + +def test_used_range_cap_warns_and_clips(tmp_path, caplog): + wb = Workbook() + ws = wb.active + ws["A1"] = "x" + ws["A400"] = "y" + path = tmp_path / "cap.xlsx" + wb.save(path) + + wb2 = openpyxl.load_workbook(str(path)) + with caplog.at_level("WARNING"): + arr = featurize_sheet(wb2[ws.title], max_rows=100, max_cols=50) + assert arr.shape[0] <= 100 + assert any("exceeds cap" in record.message for record in caplog.records) + + +def test_feature_names_stable(): + """Channel order is a compatibility contract for cached tensors and + checkpoints — changing it must be a deliberate, version-bumped act.""" + assert NUM_FEATURES == 30 + assert FEATURE_NAMES[0] == "is_empty" + assert FEATURE_NAMES[17] == "string_length" # v1 prefix preserved + assert len(set(FEATURE_NAMES)) == NUM_FEATURES + + +def test_value_string_statistics(toy_features): + b2 = cell(toy_features, "B2") # "Name" + assert 0 < b2[feature_index("string_length")] < 1 + assert b2[feature_index("letter_ratio")] == 1.0 + assert b2[feature_index("digit_ratio")] == 0.0 + + c12 = cell(toy_features, "C12") # 3.14 + assert c12[feature_index("has_decimal_point")] == 1 + assert c12[feature_index("digit_ratio")] == 3 / 4 + + g12 = cell(toy_features, "G12") # "12.5%" + assert g12[feature_index("has_percent_symbol")] == 1 + assert g12[feature_index("has_decimal_point")] == 1 + + +def test_number_format_templates(toy_features): + b12 = cell(toy_features, "B12") # 0.00% + assert b12[feature_index("numeric_format")] == 1 + assert b12[feature_index("date_format")] == 0 + assert 0 < b12[feature_index("format_length")] < 1 + + d12 = cell(toy_features, "D12") # yyyy-mm-dd + assert d12[feature_index("date_format")] == 1 + assert d12[feature_index("time_format")] == 0 + + e12 = cell(toy_features, "E12") # hh:mm + assert e12[feature_index("time_format")] == 1 + assert e12[feature_index("date_format")] == 0 + + b2 = cell(toy_features, "B2") # General + assert b2[feature_index("numeric_format")] == 0 + assert b2[feature_index("format_length")] == 0 + + +def test_merge_direction_channels(toy_features): + b14 = cell(toy_features, "B14") # B14:D14 — wide only + assert b14[feature_index("merged_horizontal")] == 1 + assert b14[feature_index("merged_vertical")] == 0 + + f14 = cell(toy_features, "F14") # F14:F16 — tall only + assert f14[feature_index("merged_horizontal")] == 0 + assert f14[feature_index("merged_vertical")] == 1 + + f2 = cell(toy_features, "F2") # F2:G3 — both + assert f2[feature_index("merged_horizontal")] == 1 + assert f2[feature_index("merged_vertical")] == 1 + + +def test_color_channels(toy_features): + b2 = cell(toy_features, "B2") # yellow fill, default font color + assert b2[feature_index("non_default_fill_color")] == 1 + assert b2[feature_index("non_default_font_color")] == 0 + + f12 = cell(toy_features, "F12") # red font, no fill + assert f12[feature_index("non_default_font_color")] == 1 + assert f12[feature_index("non_default_fill_color")] == 0 + + e4 = cell(toy_features, "E4") # untouched + assert e4[feature_index("non_default_fill_color")] == 0 + assert e4[feature_index("non_default_font_color")] == 0 + + +def test_date_format_not_fooled_by_color_section(): + """'[Red]' contains 'd' — the format classifier must strip sections.""" + from excel_table_cnn.data.features import format_features + + numeric, date, time, _ = format_features("#,##0_);[Red](#,##0)") + assert numeric == 1 and date == 0 and time == 0 diff --git a/tests/test_features_xls.py b/tests/test_features_xls.py new file mode 100644 index 0000000..700ea1f --- /dev/null +++ b/tests/test_features_xls.py @@ -0,0 +1,135 @@ +"""The xlrd featurizer must produce the same channels as the openpyxl one — +this is what makes LibreOffice optional for the .xls training corpus.""" + +import numpy as np +import pytest +from openpyxl.utils.cell import coordinate_to_tuple + +from excel_table_cnn.data.features import NUM_FEATURES, default_cell_vector +from excel_table_cnn.data.workbook import load_sheet_array +from .conftest import TOY_SHEET, feature_index + + +def cell(arr, ref): + row, col = coordinate_to_tuple(ref) + return arr[row - 1, col - 1] + + +@pytest.fixture() +def xls_features(toy_xls_path): + return load_sheet_array(toy_xls_path, TOY_SHEET) + + +def test_shape_and_dtype(xls_features): + assert xls_features.ndim == 3 + assert xls_features.shape[2] == NUM_FEATURES + assert xls_features.dtype == np.float32 + + +def test_header_cell_features(xls_features): + b2 = cell(xls_features, "B2") + assert b2[feature_index("is_empty")] == 0 + assert b2[feature_index("is_string")] == 1 + assert b2[feature_index("is_bold")] == 1 + assert b2[feature_index("is_filled")] == 1 + for side in ("left_border", "right_border", "top_border", "bottom_border"): + assert b2[feature_index(side)] == 1 + assert b2[feature_index("horizontal_alignment")] == 1 + assert b2[feature_index("center_horizontal_alignment")] == 1 + + +def test_untouched_cell_is_default(xls_features): + assert (cell(xls_features, "E4") == default_cell_vector()).all() + + +def test_numeric_cell_is_not_string(xls_features): + c3 = cell(xls_features, "C3") + assert c3[feature_index("is_empty")] == 0 + assert c3[feature_index("is_string")] == 0 + + +def test_merged_cells(xls_features): + assert cell(xls_features, "F2")[feature_index("is_merged")] == 1 # anchor + assert cell(xls_features, "G3")[feature_index("is_merged")] == 1 # covered + assert cell(xls_features, "B2")[feature_index("is_merged")] == 0 + + +def test_assorted_features(xls_features): + assert cell(xls_features, "F6")[feature_index("is_italic")] == 1 + assert cell(xls_features, "F8")[feature_index("wrapped_text")] == 1 + assert cell(xls_features, "G8")[feature_index("indent")] == 1 + assert cell(xls_features, "F10")[feature_index("left_horizontal_alignment")] == 1 + assert cell(xls_features, "G10")[feature_index("right_horizontal_alignment")] == 1 + + +def test_formula_channel_is_always_zero(xls_features): + """Documented limitation: xlrd exposes cached formula results only.""" + assert xls_features[:, :, feature_index("formula")].sum() == 0 + + +def test_empty_xls_sheet_yields_default_array(tmp_path): + """Regression: real corpus files contain fully empty sheets (nrows=0) — + they must featurize to a default array, not crash on row_len().""" + import xlwt + + wb = xlwt.Workbook() + ws = wb.add_sheet("HasData") + ws.write(0, 0, "x") + wb.add_sheet("Empty") + path = tmp_path / "empty_sheet.xls" + wb.save(str(path)) + + arr = load_sheet_array(str(path), "Empty") + assert arr.shape == (1, 1, NUM_FEATURES) + assert (arr[0, 0] == default_cell_vector()).all() + + +def test_v2_channels_on_xls_backend(xls_features): + b12 = cell(xls_features, "B12") # 0.00% numeric format + assert b12[feature_index("numeric_format")] == 1 + d12 = cell(xls_features, "D12") # YYYY-MM-DD + assert d12[feature_index("date_format")] == 1 + e12 = cell(xls_features, "E12") # hh:mm + assert e12[feature_index("time_format")] == 1 + assert cell(xls_features, "G12")[feature_index("has_percent_symbol")] == 1 + b14 = cell(xls_features, "B14") # wide merge + assert b14[feature_index("merged_horizontal")] == 1 + assert b14[feature_index("merged_vertical")] == 0 + f14 = cell(xls_features, "F14") # tall merge + assert f14[feature_index("merged_vertical")] == 1 + assert cell(xls_features, "B2")[feature_index("non_default_fill_color")] == 1 + assert cell(xls_features, "F12")[feature_index("non_default_font_color")] == 1 + + +# Value-string channels legitimately diverge across backends for cells whose +# *value type* differs (dates/times: datetime vs serial float). The format +# and style channels must still agree everywhere. +VALUE_STRING_CHANNELS = frozenset( + ["string_length", "digit_ratio", "letter_ratio", + "has_percent_symbol", "has_decimal_point"] +) +DATE_LIKE_PROBES = frozenset(["D12", "E12"]) + + +def test_parity_with_openpyxl_featurizer(toy_workbook_path, toy_xls_path): + """The two backends must agree channel-for-channel on equivalent content + (except ``formula``, absent from the .xls fixture by design, and + value-string stats on date/time cells, where the raw value types differ).""" + from excel_table_cnn.data.features import FEATURE_NAMES + + xlsx = load_sheet_array(toy_workbook_path, TOY_SHEET) + xls = load_sheet_array(toy_xls_path, TOY_SHEET) + + probes = ["B2", "C2", "D2", "B3", "C3", "D6", "E4", + "F2", "G2", "G3", "F6", "F8", "G8", "F10", "G10", + "B12", "C12", "D12", "E12", "F12", "G12", "B14", "C14", "F14", "F15"] + channels = [name for name in FEATURE_NAMES if name != "formula"] + for ref in probes: + for name in channels: + if ref in DATE_LIKE_PROBES and name in VALUE_STRING_CHANNELS: + continue + idx = feature_index(name) + assert cell(xlsx, ref)[idx] == cell(xls, ref)[idx], ( + f"{ref} channel {name}: openpyxl={cell(xlsx, ref)[idx]} " + f"xlrd={cell(xls, ref)[idx]}" + ) diff --git a/tests/test_grid_context.py b/tests/test_grid_context.py new file mode 100644 index 0000000..18fdf87 --- /dev/null +++ b/tests/test_grid_context.py @@ -0,0 +1,49 @@ +import torch + +from excel_table_cnn.data.features import NUM_FEATURES +from excel_table_cnn.model.backbone import FCNBackbone +from excel_table_cnn.model.grid_context import ( + NUM_DERIVED_CHANNELS, + AxialStripBlock, + DerivedChannels, +) + + +def test_derived_channels_values(): + # 2 rows x 3 cols; is_empty is channel 0. + x = torch.zeros(1, 2, 2, 3) + x[0, 0] = torch.tensor([[1.0, 0.0, 1.0], # row 0: 1 of 3 filled + [0.0, 0.0, 0.0]]) # row 1: all filled + out = DerivedChannels(is_empty_index=0)(x) + assert out.shape == (1, 2 + NUM_DERIVED_CHANNELS, 2, 3) + + row_density = out[0, 2] + assert torch.allclose(row_density[0], torch.full((3,), 1 / 3)) + assert torch.allclose(row_density[1], torch.ones(3)) + + col_density = out[0, 3] + assert torch.allclose(col_density[:, 0], torch.tensor([0.5, 0.5])) + assert torch.allclose(col_density[:, 1], torch.ones(2)) + + row_coord, col_coord = out[0, 4], out[0, 5] + assert row_coord[0, 0] == 0.0 and row_coord[1, 0] == 1.0 + assert col_coord[0, 0] == 0.0 and col_coord[0, 2] == 1.0 + + +def test_axial_strip_block_preserves_shape(): + block = AxialStripBlock(channels=16) + x = torch.randn(2, 16, 20, 12) + out = block(x) + assert out.shape == x.shape + assert torch.isfinite(out).all() + + +def test_backbone_with_and_without_grid_context(): + x = torch.randn(1, NUM_FEATURES, 24, 16) + for use_grid_context in (True, False): + backbone = FCNBackbone(NUM_FEATURES, use_grid_context=use_grid_context) + out = backbone(x) + assert out.shape == (1, backbone.out_channels, 24, 16) # stride 1 + expected_in = NUM_FEATURES + (NUM_DERIVED_CHANNELS if use_grid_context else 0) + assert backbone.body[0].in_channels == expected_in + assert backbone.in_channels == NUM_FEATURES # data channels, always \ No newline at end of file diff --git a/tests/test_inference.py b/tests/test_inference.py new file mode 100644 index 0000000..987fa13 --- /dev/null +++ b/tests/test_inference.py @@ -0,0 +1,58 @@ +import re + +from excel_table_cnn.data.features import NUM_FEATURES +from excel_table_cnn.inference import detect_tables +from excel_table_cnn.model.detector import build_model +from excel_table_cnn.training.train import seed_everything +from .conftest import TOY_SHEET + +RANGE_PATTERN = re.compile(r"^[A-Z]+[0-9]+:[A-Z]+[0-9]+$") + + +def test_detect_tables_output_schema(toy_workbook_path): + seed_everything(0) + model = build_model(in_channels=NUM_FEATURES, box_score_thresh=0.0) + detections = detect_tables( + toy_workbook_path, model=model, score_threshold=0.0 + ) + assert isinstance(detections, list) + assert len(detections) > 0 # untrained but with threshold 0 something comes out + for det in detections: + assert det["sheet"] == TOY_SHEET + assert RANGE_PATTERN.match(det["range"]), det["range"] + assert 0.0 <= det["score"] <= 1.0 + + +def test_score_threshold_filters_everything(toy_workbook_path): + seed_everything(0) + model = build_model(in_channels=NUM_FEATURES, box_score_thresh=0.0) + detections = detect_tables(toy_workbook_path, model=model, score_threshold=1.1) + assert detections == [] + + +def test_explicit_sheet_name(toy_workbook_path): + seed_everything(0) + model = build_model(in_channels=NUM_FEATURES, box_score_thresh=0.0) + detections = detect_tables( + toy_workbook_path, sheet_name=TOY_SHEET, model=model, score_threshold=0.0 + ) + assert all(det["sheet"] == TOY_SHEET for det in detections) + + +def test_untrained_warning_when_no_model_given(toy_workbook_path, caplog): + with caplog.at_level("WARNING"): + detect_tables(toy_workbook_path, score_threshold=0.99, device="cpu") + assert any("randomly initialized" in record.message for record in caplog.records) + + +def test_detect_tables_on_native_xls(toy_xls_path): + """Inference reads legacy .xls directly — no LibreOffice.""" + seed_everything(0) + model = build_model(in_channels=NUM_FEATURES, box_score_thresh=0.0) + detections = detect_tables( + toy_xls_path, model=model, score_threshold=0.0, device="cpu" + ) + assert isinstance(detections, list) + for det in detections: + assert det["sheet"] == TOY_SHEET + assert RANGE_PATTERN.match(det["range"]) diff --git a/tests/test_loader.py b/tests/test_loader.py new file mode 100644 index 0000000..719e84e --- /dev/null +++ b/tests/test_loader.py @@ -0,0 +1,43 @@ +import hashlib + +import pytest + +from excel_table_cnn.data.loader import DatasetLoader + + +@pytest.fixture() +def loader_with_archive(tmp_path): + loader = DatasetLoader(save_path=str(tmp_path)) + archive = tmp_path / "VEnron2.7z" + archive.write_bytes(b"definitely an archive") + return loader, archive + + +def test_verify_archive_accepts_matching_md5(loader_with_archive): + loader, archive = loader_with_archive + loader.known_md5["VEnron2"] = hashlib.md5(archive.read_bytes()).hexdigest() + assert loader.verify_archive("VEnron2") is True + + +def test_verify_archive_rejects_mismatch(loader_with_archive): + loader, _ = loader_with_archive + loader.known_md5["VEnron2"] = "0" * 32 + with pytest.raises(RuntimeError, match="Checksum mismatch"): + loader.verify_archive("VEnron2") + + +def test_known_checksums_cover_all_datasets(): + loader = DatasetLoader(save_path=".") + assert set(loader.known_md5) == set(loader.datasets) + assert all(len(v) == 32 for v in loader.known_md5.values()) + + +def test_cli_helps_exit_cleanly(): + from excel_table_cnn.evaluation.evaluate import main as eval_main + from excel_table_cnn.inference import main as detect_main + from excel_table_cnn.training.train import main as train_main + + for main in (train_main, eval_main, detect_main): + with pytest.raises(SystemExit) as exc: + main(["--help"]) + assert exc.value.code == 0 diff --git a/tests/test_model.py b/tests/test_model.py new file mode 100644 index 0000000..4f59148 --- /dev/null +++ b/tests/test_model.py @@ -0,0 +1,96 @@ +import pytest +import torch + +from excel_table_cnn.data.features import NUM_FEATURES +from excel_table_cnn.model.detector import build_model +from excel_table_cnn.training.dataset import SpreadsheetDataset +from excel_table_cnn.training.train import LOSS_KEYS, seed_everything +from .conftest import make_synthetic_sample + + +@pytest.fixture() +def sample_batch(): + dataset = SpreadsheetDataset([make_synthetic_sample()]) + tensor, target = dataset[0] + return [tensor], [target] + + +def test_model_constructs_with_arbitrary_channels(): + from excel_table_cnn.model.grid_context import NUM_DERIVED_CHANNELS + + for channels in (NUM_FEATURES, 20): + model = build_model(in_channels=channels) # grid context on by default + assert model.in_channels == channels + assert model.backbone.body[0].in_channels == channels + NUM_DERIVED_CHANNELS + + plain = build_model(in_channels=NUM_FEATURES, use_grid_context=False) + assert plain.backbone.body[0].in_channels == NUM_FEATURES + + +def test_architecture_flags_round_trip(): + model = build_model(in_channels=NUM_FEATURES, use_pbr=False, use_grid_context=False) + assert model.config == {"use_pbr": False, "pbr_k": 7, "use_grid_context": False} + assert model.pbr is None + + full = build_model(in_channels=NUM_FEATURES) + assert full.config["use_pbr"] is True and full.config["use_grid_context"] is True + + +def test_wrong_channel_input_raises(sample_batch): + """Regression for the num_classes/in_channels argument mix-up: a model + built for N channels must reject differently-shaped input loudly.""" + images, targets = sample_batch + model = build_model(in_channels=5) + model.train() + with pytest.raises(RuntimeError): + model(images, targets) + + +def test_training_forward_returns_all_loss_components(sample_batch): + seed_everything(0) + images, targets = sample_batch + model = build_model(in_channels=NUM_FEATURES) + model.train() + loss_dict = model(images, targets) + + assert set(LOSS_KEYS) == set(loss_dict.keys()) + for key in LOSS_KEYS: + value = loss_dict[key] + assert torch.isfinite(value), f"{key} is not finite" + assert value.item() >= 0 + # With random weights on a sheet containing a table, classification + # losses cannot be zero — a zero here is the historic "loss 0" failure. + assert loss_dict["loss_objectness"].item() > 0 + assert loss_dict["loss_classifier"].item() > 0 + + +def test_eval_forward_returns_detection_dicts(sample_batch): + seed_everything(0) + images, _ = sample_batch + model = build_model(in_channels=NUM_FEATURES, box_score_thresh=0.0) + model.eval() + with torch.no_grad(): + outputs = model(images) + assert len(outputs) == 1 + output = outputs[0] + assert set(output.keys()) >= {"boxes", "scores", "labels"} + assert output["boxes"].shape[1] == 4 + assert output["scores"].shape[0] == output["boxes"].shape[0] + if len(output["scores"]): + assert float(output["scores"].max()) <= 1.0 + assert float(output["scores"].min()) >= 0.0 + + +def test_degenerate_target_rejected_by_torchvision(): + """The dataset layer must filter degenerate boxes because torchvision + refuses them — pin that contract.""" + sample = make_synthetic_sample() + tensor = sample["tensor"] + bad_target = { + "boxes": torch.tensor([[3.0, 5.0, 3.0, 21.0]]), # zero width + "labels": torch.ones((1,), dtype=torch.int64), + } + model = build_model(in_channels=NUM_FEATURES) + model.train() + with pytest.raises(Exception): + model([tensor], [bad_target]) diff --git a/tests/test_mps_smoke.py b/tests/test_mps_smoke.py new file mode 100644 index 0000000..87a5f26 --- /dev/null +++ b/tests/test_mps_smoke.py @@ -0,0 +1,45 @@ +"""Apple Silicon compatibility gate: every op the model uses (convs, +GroupNorm, RPN, NMS, RoIAlign, detection postprocess) must run on MPS. + +Kept short — MPS is *supported*, not auto-selected (measured slower than the +M-series CPU cores for this small model; see excel_table_cnn.device). +""" + +import pytest +import torch + +from excel_table_cnn.data.features import NUM_FEATURES +from excel_table_cnn.model.detector import build_model +from excel_table_cnn.training.dataset import SpreadsheetDataset +from .conftest import make_synthetic_sample + +requires_mps = pytest.mark.skipif( + not torch.backends.mps.is_available(), reason="MPS backend not available" +) + + +@requires_mps +@pytest.mark.slow +def test_train_and_eval_steps_run_on_mps(): + device = torch.device("mps") + dataset = SpreadsheetDataset([make_synthetic_sample()]) + image, target = dataset[0] + image = image.to(device) + target = {k: v.to(device) for k, v in target.items()} + + model = build_model(in_channels=NUM_FEATURES, box_score_thresh=0.0).to(device) + model.train() + optimizer = torch.optim.SGD(model.parameters(), lr=1e-3) + for _ in range(3): + loss_dict = model([image], [target]) + total = sum(loss_dict.values()) + assert torch.isfinite(total), loss_dict + optimizer.zero_grad() + total.backward() + optimizer.step() + + model.eval() + with torch.no_grad(): + output = model([image])[0] + assert output["boxes"].device.type == "mps" + assert torch.isfinite(output["boxes"]).all() diff --git a/tests/test_overfit.py b/tests/test_overfit.py new file mode 100644 index 0000000..d0ace30 --- /dev/null +++ b/tests/test_overfit.py @@ -0,0 +1,54 @@ +"""M0 smoke test: the model must be able to overfit a single synthetic sheet. + +This is the project's core regression gate. Both historic failure modes — +loss collapsing to 0 without learning, and construction bugs that crash +training — fail this test. If it is red, nothing downstream matters. +""" + +import pytest +import torch + +from excel_table_cnn.evaluation.eob import eob +from excel_table_cnn.data.features import NUM_FEATURES +from excel_table_cnn.model.detector import build_model +from excel_table_cnn.training.dataset import SpreadsheetDataset +from excel_table_cnn.training.train import TrainConfig, train_model +from .conftest import make_synthetic_sample + +GT_BOX = (3, 5, 11, 21) + + +@pytest.mark.slow +def test_overfit_single_sheet(): + sample = make_synthetic_sample(height=30, width=15, box=GT_BOX) + dataset = SpreadsheetDataset([sample]) + model = build_model(in_channels=NUM_FEATURES, box_score_thresh=0.05) + + config = TrainConfig( + epochs=250, # 1 sample per epoch = 250 steps + lr=0.01, + warmup_steps=50, + seed=42, + device="cpu", + log_every=0, + ) + history = train_model(model, dataset, config) + + first = sum(r["loss_total"] for r in history[:10]) / 10 + last = sum(r["loss_total"] for r in history[-10:]) / 10 + assert last < first * 0.5, f"loss did not halve: {first:.4f} -> {last:.4f}" + + # Early in training every component must be alive — a silent zero here + # is the historic "loss goes to 0 but nothing works" bug. + early = history[:5] + for key in ("loss_objectness", "loss_classifier"): + assert all(r[key] > 0 for r in early), f"{key} is zero from the start" + + model.eval() + with torch.no_grad(): + output = model([sample["tensor"]])[0] + assert len(output["boxes"]) > 0, "overfit model detects nothing on its own sheet" + top_box = output["boxes"][int(output["scores"].argmax())].tolist() + error = eob(top_box, GT_BOX) + # With the PBR snapping head the gate demands cell-exact boundaries. + assert error == 0, f"top detection {top_box} vs GT {GT_BOX}: EoB={error}" diff --git a/tests/test_pbr.py b/tests/test_pbr.py new file mode 100644 index 0000000..3790476 --- /dev/null +++ b/tests/test_pbr.py @@ -0,0 +1,78 @@ +import torch + +from excel_table_cnn.model.pbr import PBRHead, jitter_boxes + + +def test_jitter_stays_valid_and_targets_within_k(): + torch.manual_seed(0) + boxes = torch.tensor([[3.0, 5.0, 11.0, 21.0], [0.0, 0.0, 2.0, 2.0]]) + for _ in range(50): + jittered, offsets = jitter_boxes(boxes, k=7, height=30, width=15) + assert (jittered[:, 2] > jittered[:, 0]).all() + assert (jittered[:, 3] > jittered[:, 1]).all() + assert (jittered[:, 0] >= 0).all() and (jittered[:, 1] >= 0).all() + assert offsets.abs().max() <= 7 + assert torch.equal(jittered + offsets, (jittered + offsets)) # finite + + +def test_jitter_empty_boxes(): + boxes = torch.empty((0, 4)) + jittered, offsets = jitter_boxes(boxes, k=7, height=10, width=10) + assert jittered.numel() == 0 and offsets.numel() == 0 + + +def test_forward_shapes(): + head = PBRHead(in_channels=16, k=7) + features = torch.randn(1, 16, 40, 30) + boxes = torch.tensor([[3.0, 5.0, 11.0, 21.0], [10.0, 2.0, 20.0, 30.0]]) + logits = head(features, boxes) + assert logits.shape == (2, 4, 15) # N x edges x (2k+1) + + +def test_loss_scalar_and_refine_valid(): + torch.manual_seed(0) + head = PBRHead(in_channels=16, k=7) + features = torch.randn(1, 16, 40, 30) + boxes = torch.tensor([[3.0, 5.0, 11.0, 21.0]]) + loss = head.loss(features, boxes, height=40, width=30) + assert loss.ndim == 0 and torch.isfinite(loss) and loss > 0 + + refined = head.refine(features, boxes + 0.3, height=40, width=30) + assert refined.shape == boxes.shape + assert (refined[:, 2] > refined[:, 0]).all() + assert (refined[:, 3] > refined[:, 1]).all() + assert torch.equal(refined, refined.round()) # snapped to the cell grid + + +def test_refine_empty(): + head = PBRHead(in_channels=16, k=7) + features = torch.randn(1, 16, 20, 20) + out = head.refine(features, torch.empty((0, 4)), height=20, width=20) + assert out.numel() == 0 + + +def test_pbr_learns_to_snap_edges(): + """Overfit the head on one synthetic feature map: given jittered boxes it + must recover the true boundary. This is the module-level version of the + EoB-0 promise.""" + torch.manual_seed(42) + gt = torch.tensor([[8.0, 10.0, 20.0, 30.0]]) + features = torch.zeros(1, 8, 48, 32) + # Distinct signal inside the table region: boundaries are visible edges. + features[:, :4, 10:30, 8:20] = 1.0 + features[:, 4:, 10:30, 8:20] = -1.0 + + head = PBRHead(in_channels=8, k=7, trunk_channels=32, hidden=64) + optimizer = torch.optim.Adam(head.parameters(), lr=2e-3) + for _ in range(300): + loss = head.loss(features, gt, height=48, width=32) + optimizer.zero_grad() + loss.backward() + optimizer.step() + + torch.manual_seed(7) + jittered, _ = jitter_boxes(gt.repeat(8, 1), k=7, height=48, width=32) + with torch.no_grad(): + refined = head.refine(features, jittered, height=48, width=32) + recovered = (refined == gt).all(dim=1).float().mean() + assert recovered >= 0.75, f"only {recovered:.0%} of jittered boxes snapped back to GT" \ No newline at end of file diff --git a/tests/test_pipeline_cache.py b/tests/test_pipeline_cache.py new file mode 100644 index 0000000..762c75e --- /dev/null +++ b/tests/test_pipeline_cache.py @@ -0,0 +1,89 @@ +import pandas as pd +import pytest +import torch + +import excel_table_cnn.data.pipeline as pipeline +from excel_table_cnn.data.features import FEATURE_NAMES +from .conftest import TOY_SHEET, TOY_TABLE_RANGE + + +def test_build_sheet_sample_contents(toy_workbook_path, tmp_path): + sample = pipeline.build_sheet_sample( + toy_workbook_path, TOY_SHEET, [TOY_TABLE_RANGE], cache_dir=str(tmp_path / "cache") + ) + assert sample["tensor"].shape[0] == len(FEATURE_NAMES) + assert sample["boxes"].tolist() == [[1.0, 1.0, 4.0, 6.0]] # B2:D6 half-open + assert sample["sheet_name"] == TOY_SHEET + assert sample["feature_names"] == list(FEATURE_NAMES) + + +def test_second_call_hits_cache(toy_workbook_path, tmp_path, monkeypatch): + cache_dir = str(tmp_path / "cache") + first = pipeline.build_sheet_sample( + toy_workbook_path, TOY_SHEET, [TOY_TABLE_RANGE], cache_dir=cache_dir + ) + + class Boom: + def __init__(self, *args, **kwargs): + raise AssertionError("workbook opened despite warm cache") + + monkeypatch.setattr(pipeline, "WorkbookReader", Boom) + second = pipeline.build_sheet_sample( + toy_workbook_path, TOY_SHEET, [TOY_TABLE_RANGE], cache_dir=cache_dir + ) + assert torch.equal(first["tensor"], second["tensor"]) + assert torch.equal(first["boxes"], second["boxes"]) + + +def test_build_sheet_sample_from_native_xls(toy_xls_path, tmp_path): + """LibreOffice-free path: legacy .xls straight into a training sample.""" + sample = pipeline.build_sheet_sample( + toy_xls_path, TOY_SHEET, [TOY_TABLE_RANGE], cache_dir=str(tmp_path / "cache") + ) + assert sample["tensor"].shape[0] == len(FEATURE_NAMES) + assert sample["boxes"].tolist() == [[1.0, 1.0, 4.0, 6.0]] + + +def test_cache_key_changes_with_file_content(toy_workbook_path, tmp_path): + key_before = pipeline._cache_key(toy_workbook_path, TOY_SHEET) + with open(toy_workbook_path, "ab") as f: + f.write(b"tamper") + key_after = pipeline._cache_key(toy_workbook_path, TOY_SHEET) + assert key_before != key_after + + +def test_build_samples_from_files_df(toy_workbook_path, tmp_path): + import os + + files_df = pd.DataFrame( + [ + { + "parent_path": os.path.dirname(toy_workbook_path), + "file_name": os.path.basename(toy_workbook_path), + "sheet_name": TOY_SHEET, + "table_range": [TOY_TABLE_RANGE], + } + ] + ) + samples = pipeline.build_samples(files_df, data_folder_path="/") + assert len(samples) == 1 + assert samples[0]["boxes"].shape == (1, 4) + + +def test_build_samples_skips_broken_files(tmp_path, caplog): + broken = tmp_path / "broken.xlsx" + broken.write_bytes(b"this is not a workbook") + files_df = pd.DataFrame( + [ + { + "parent_path": str(tmp_path), + "file_name": "broken.xlsx", + "sheet_name": "Sheet1", + "table_range": ["A1:B2"], + } + ] + ) + with caplog.at_level("ERROR"): + samples = pipeline.build_samples(files_df, data_folder_path="/") + assert samples == [] + assert any("Skipping" in record.message for record in caplog.records) diff --git a/tests/test_train_loop.py b/tests/test_train_loop.py new file mode 100644 index 0000000..d9c1789 --- /dev/null +++ b/tests/test_train_loop.py @@ -0,0 +1,82 @@ +import os + +import torch + +from excel_table_cnn.data.features import NUM_FEATURES +from excel_table_cnn.model.detector import build_model +from excel_table_cnn.training.dataset import SpreadsheetDataset +from excel_table_cnn.training.train import ( + LOSS_KEYS, + TrainConfig, + load_checkpoint, + save_checkpoint, + train_model, +) +from .conftest import make_synthetic_sample + + +def test_train_loop_logs_all_components_and_checkpoints(tmp_path): + dataset = SpreadsheetDataset([make_synthetic_sample(height=16, width=10, box=(2, 3, 8, 12))]) + model = build_model(in_channels=NUM_FEATURES) + config = TrainConfig( + epochs=2, lr=0.001, warmup_steps=5, log_every=0, + checkpoint_dir=str(tmp_path), device="cpu", seed=0, + ) + history = train_model(model, dataset, config) + + assert len(history) == 2 # 1 sample x 2 epochs + for record in history: + for key in LOSS_KEYS: + assert key in record + assert record[key] >= 0 + assert torch.isfinite(torch.tensor(record["loss_total"])) + assert os.path.exists(tmp_path / "last.pt") + + +def test_checkpoint_round_trip(tmp_path): + model = build_model(in_channels=NUM_FEATURES) + path = str(tmp_path / "model.pt") + save_checkpoint(model, path, note="test") + + restored = load_checkpoint(path) + assert restored.in_channels == NUM_FEATURES + assert restored.num_classes == 2 + + +def test_checkpoint_restores_architecture_flags(tmp_path): + """A no-PBR/no-grid-context model must come back in the same shape.""" + model = build_model(in_channels=NUM_FEATURES, use_pbr=False, use_grid_context=False) + path = str(tmp_path / "ablated.pt") + save_checkpoint(model, path) + + restored = load_checkpoint(path) + assert restored.pbr is None + assert restored.use_grid_context is False + for key, value in restored.state_dict().items(): + assert torch.equal(value, model.state_dict()[key]) + + +def test_load_checkpoint_honors_score_thresh_override(tmp_path): + """Regression: eval/detect must be able to lower the internal RoI score + gate — the default (0.5) otherwise hides low-confidence detections from + any external --score-threshold below it.""" + model = build_model(in_channels=NUM_FEATURES) + path = str(tmp_path / "model.pt") + save_checkpoint(model, path) + + restored = load_checkpoint(path, box_score_thresh=0.05) + assert restored.model.roi_heads.score_thresh == 0.05 + original = model.state_dict() + for key, value in restored.state_dict().items(): + assert torch.equal(value, original[key]) + + +def test_checkpoint_stores_feature_contract(tmp_path): + from excel_table_cnn.data.features import FEATURE_NAMES + + model = build_model(in_channels=NUM_FEATURES) + path = str(tmp_path / "model.pt") + save_checkpoint(model, path) + payload = torch.load(path, weights_only=True) + assert payload["feature_names"] == list(FEATURE_NAMES) + assert payload["in_channels"] == NUM_FEATURES diff --git a/tests/test_workbook.py b/tests/test_workbook.py new file mode 100644 index 0000000..f56c83e --- /dev/null +++ b/tests/test_workbook.py @@ -0,0 +1,42 @@ +import numpy as np +import pytest + +from excel_table_cnn.data.workbook import ( + UnsupportedFormatError, + WorkbookReader, + load_sheet_array, +) +from .conftest import TOY_SHEET + + +def test_reader_xlsx(toy_workbook_path): + with WorkbookReader(toy_workbook_path) as reader: + assert reader.sheet_names == [TOY_SHEET] + arr = reader.sheet_array(TOY_SHEET) + assert isinstance(arr, np.ndarray) and arr.ndim == 3 + + +def test_reader_xls(toy_xls_path): + with WorkbookReader(toy_xls_path) as reader: + assert reader.sheet_names == [TOY_SHEET] + arr = reader.sheet_array(TOY_SHEET) + assert isinstance(arr, np.ndarray) and arr.ndim == 3 + + +def test_load_sheet_array_one_shot(toy_workbook_path): + arr = load_sheet_array(toy_workbook_path, TOY_SHEET) + assert arr.ndim == 3 + + +def test_xlsb_raises_with_conversion_hint(tmp_path): + path = tmp_path / "book.xlsb" + path.write_bytes(b"\x00") + with pytest.raises(UnsupportedFormatError, match="libreoffice"): + WorkbookReader(str(path)) + + +def test_unknown_extension_raises(tmp_path): + path = tmp_path / "table.csv" + path.write_text("a,b\n1,2\n") + with pytest.raises(UnsupportedFormatError): + WorkbookReader(str(path)) diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..6a0f3f7 --- /dev/null +++ b/uv.lock @@ -0,0 +1,1963 @@ +version = 1 +revision = 3 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' 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