From c5fa54a35efbda482390a2235348bdcdbe8385a4 Mon Sep 17 00:00:00 2001
From: Yoann Schneider <yschneider@teklia.com>
Date: Mon, 21 Aug 2023 13:38:49 +0000
Subject: [PATCH] Parse tokens during argparse parsing and expose start_token
---
dan/ocr/predict/__init__.py | 14 ++++++++----
dan/ocr/predict/prediction.py | 15 ++++++++-----
dan/utils.py | 4 ++--
docs/usage/predict.md | 41 ++++++++++++++++++-----------------
tests/test_prediction.py | 7 ++++--
5 files changed, 48 insertions(+), 33 deletions(-)
diff --git a/dan/ocr/predict/__init__.py b/dan/ocr/predict/__init__.py
index 1c65254a..0ee92b8b 100644
--- a/dan/ocr/predict/__init__.py
+++ b/dan/ocr/predict/__init__.py
@@ -6,6 +6,7 @@ Predict on an image using a trained DAN model.
import pathlib
from dan.ocr.predict.prediction import run
+from dan.utils import parse_tokens
def add_predict_parser(subcommands) -> None:
@@ -51,13 +52,13 @@ def add_predict_parser(subcommands) -> None:
help="Path to the output folder.",
required=True,
)
+ # Optional arguments.
parser.add_argument(
"--tokens",
- type=pathlib.Path,
- required=True,
+ type=parse_tokens,
+ required=False,
help="Path to a yaml file containing a mapping between starting tokens and end tokens. Needed for entities.",
)
- # Optional arguments.
parser.add_argument(
"--image-extension",
type=str,
@@ -154,5 +155,10 @@ def add_predict_parser(subcommands) -> None:
default=1,
required=False,
)
-
+ parser.add_argument(
+ "--start-token",
+ help="Use a specific starting token at the beginning of the prediction. Useful when making predictions on different single pages.",
+ type=str,
+ required=False,
+ )
parser.set_defaults(func=run)
diff --git a/dan/ocr/predict/prediction.py b/dan/ocr/predict/prediction.py
index 32758f8a..cdca2a90 100644
--- a/dan/ocr/predict/prediction.py
+++ b/dan/ocr/predict/prediction.py
@@ -5,7 +5,7 @@ import pickle
import re
from itertools import pairwise
from pathlib import Path
-from typing import Iterable, List, Optional, Tuple
+from typing import Dict, Iterable, List, Optional, Tuple
import numpy as np
import torch
@@ -22,10 +22,10 @@ from dan.ocr.predict.attention import (
)
from dan.ocr.transforms import get_preprocessing_transforms
from dan.utils import (
+ EntityType,
ind_to_token,
list_to_batches,
pad_images,
- parse_tokens,
read_image,
)
@@ -287,7 +287,8 @@ def process_batch(
predict_objects: bool,
threshold_method: str,
threshold_value: int,
- tokens: Path,
+ tokens: Dict[str, EntityType],
+ start_token: str,
) -> None:
input_images, visu_images, input_sizes = [], [], []
logger.info("Loading images...")
@@ -320,6 +321,7 @@ def process_batch(
line_separators=line_separators,
threshold_method=threshold_method,
threshold_value=threshold_value,
+ start_token=start_token,
)
logger.info("Prediction parsing...")
@@ -336,7 +338,6 @@ def process_batch(
result["confidences"] = {}
char_confidences = prediction["confidences"][0]
text = result["text"]
- tokens = parse_tokens(tokens)
start_tokens, end_tokens = zip(*list(tokens.values()))
end_tokens = list(filter(bool, end_tokens))
@@ -422,7 +423,8 @@ def run(
image_extension: str,
gpu_device: int,
batch_size: int,
- tokens: Path,
+ tokens: Dict[str, EntityType],
+ start_token: str,
) -> None:
"""
Predict a single image save the output
@@ -443,6 +445,8 @@ def run(
:param threshold_value: Thresholding value to use for the "simple" thresholding method.
:param gpu_device: Use a specific GPU if available.
:param batch_size: Size of the batches for prediction.
+ :param tokens: NER tokens used.
+ :param start_token: Use a specific starting token at the beginning of the prediction. Useful when making predictions on different single pages.
"""
# Create output directory if necessary
if not output.exists():
@@ -472,4 +476,5 @@ def run(
threshold_method,
threshold_value,
tokens,
+ start_token,
)
diff --git a/dan/utils.py b/dan/utils.py
index 92954f8a..5cb62ff5 100644
--- a/dan/utils.py
+++ b/dan/utils.py
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
from itertools import islice
from pathlib import Path
-from typing import NamedTuple
+from typing import Dict, NamedTuple
import torch
import torchvision.io as torchvision
@@ -106,7 +106,7 @@ def list_to_batches(iterable, n):
yield batch
-def parse_tokens(filename: Path) -> dict:
+def parse_tokens(filename: Path) -> Dict[str, EntityType]:
return {
name: EntityType(**tokens)
for name, tokens in yaml.safe_load(filename.read_text()).items()
diff --git a/docs/usage/predict.md b/docs/usage/predict.md
index 294e67aa..d6d46350 100644
--- a/docs/usage/predict.md
+++ b/docs/usage/predict.md
@@ -4,26 +4,27 @@ Use the `teklia-dan predict` command to apply a trained DAN model on an image.
## Description of parameters
-| Parameter | Description | Type | Default |
-| --------------------------- | ----------------------------------------------------------------------------------------------- | ------- | ------------- |
-| `--image` | Path to the image to predict. Must not be provided with `--image-dir`. | `Path` | |
-| `--image-dir` | Path to the folder where the images to predict are stored. Must not be provided with `--image`. | `Path` | |
-| `--image-extension` | The extension of the images in the folder. Ignored if `--image-dir` is not provided. | `str` | .jpg |
-| `--model` | Path to the model to use for prediction | `Path` | |
-| `--parameters` | Path to the YAML parameters file. | `Path` | |
-| `--charset` | Path to the charset file. | `Path` | |
-| `--output` | Path to the output folder. Results will be saved in this directory. | `Path` | |
-| `--confidence-score` | Whether to return confidence scores. | `bool` | `False` |
-| `--confidence-score-levels` | Level to return confidence scores. Should be any combination of `["line", "word", "char"]`. | `str` | |
-| `--attention-map` | Whether to plot attention maps. | `bool` | `False` |
-| `--attention-map-scale` | Image scaling factor before creating the GIF. | `float` | `0.5` |
-| `--attention-map-level` | Level to plot the attention maps. Should be in `["line", "word", "char"]`. | `str` | `"line"` |
-| `--predict-objects` | Whether to return polygons coordinates. | `bool` | `False` |
-| `--word-separators` | List of word separators. | `list` | `[" ", "\n"]` |
-| `--line-separators` | List of line separators. | `list` | `["\n"]` |
-| `--threshold-method` | Method to use for attention mask thresholding. Should be in `["otsu", "simple"]`. | `str` | `"otsu"` |
-| `--threshold-value ` | Threshold to use for the "simple" thresholding method. | `int` | `0` |
-| `--batch-size ` | Size of the batches for prediction. | `int` | `1` |
+| Parameter | Description | Type | Default |
+| --------------------------- | --------------------------------------------------------------------------------------------------------------------------- | ------- | ------------- |
+| `--image` | Path to the image to predict. Must not be provided with `--image-dir`. | `Path` | |
+| `--image-dir` | Path to the folder where the images to predict are stored. Must not be provided with `--image`. | `Path` | |
+| `--image-extension` | The extension of the images in the folder. Ignored if `--image-dir` is not provided. | `str` | .jpg |
+| `--model` | Path to the model to use for prediction | `Path` | |
+| `--parameters` | Path to the YAML parameters file. | `Path` | |
+| `--charset` | Path to the charset file. | `Path` | |
+| `--output` | Path to the output folder. Results will be saved in this directory. | `Path` | |
+| `--confidence-score` | Whether to return confidence scores. | `bool` | `False` |
+| `--confidence-score-levels` | Level to return confidence scores. Should be any combination of `["line", "word", "char"]`. | `str` | |
+| `--attention-map` | Whether to plot attention maps. | `bool` | `False` |
+| `--attention-map-scale` | Image scaling factor before creating the GIF. | `float` | `0.5` |
+| `--attention-map-level` | Level to plot the attention maps. Should be in `["line", "word", "char"]`. | `str` | `"line"` |
+| `--predict-objects` | Whether to return polygons coordinates. | `bool` | `False` |
+| `--word-separators` | List of word separators. | `list` | `[" ", "\n"]` |
+| `--line-separators` | List of line separators. | `list` | `["\n"]` |
+| `--threshold-method` | Method to use for attention mask thresholding. Should be in `["otsu", "simple"]`. | `str` | `"otsu"` |
+| `--threshold-value ` | Threshold to use for the "simple" thresholding method. | `int` | `0` |
+| `--batch-size ` | Size of the batches for prediction. | `int` | `1` |
+| `--start-token ` | Use a specific starting token at the beginning of the prediction. Useful when making predictions on different single pages. | `str` | `None` |
## Examples
diff --git a/tests/test_prediction.py b/tests/test_prediction.py
index b9e6e877..3acbb433 100644
--- a/tests/test_prediction.py
+++ b/tests/test_prediction.py
@@ -7,6 +7,7 @@ import pytest
from dan.ocr.predict.prediction import DAN
from dan.ocr.predict.prediction import run as run_prediction
+from dan.utils import parse_tokens
@pytest.mark.parametrize(
@@ -316,7 +317,8 @@ def test_run_prediction(
image_extension=None,
gpu_device=None,
batch_size=1,
- tokens=prediction_data_path / "tokens.yml",
+ tokens=parse_tokens(prediction_data_path / "tokens.yml"),
+ start_token=None,
)
prediction = json.loads((tmp_path / image_name).with_suffix(".json").read_text())
@@ -514,7 +516,8 @@ def test_run_prediction_batch(
image_extension=".png",
gpu_device=None,
batch_size=batch_size,
- tokens=prediction_data_path / "tokens.yml",
+ tokens=parse_tokens(prediction_data_path / "tokens.yml"),
+ start_token=None,
)
for image_name, expected_prediction in zip(image_names, expected_predictions):
--
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