From 5f62c7fbf0e0bd620c62b38f81e5282039470af3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B8=89=E6=B4=8B=E4=B8=89=E6=B4=8B?= <1258009915@qq.com> Date: Sun, 21 Apr 2024 12:09:26 +0000 Subject: [PATCH 1/2] =?UTF-8?q?1)=20=E4=BF=AE=E5=A4=8D=E4=BA=86to=5Fkatex.?= =?UTF-8?q?py=E7=9A=84bug;=202)=E6=8A=8ABox.py=E4=B8=AD=E7=9A=84=E8=BD=AC?= =?UTF-8?q?=E5=8C=96=E7=BB=93=E6=9E=9C=E5=86=99=E5=9C=A8logs?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/models/det_model/Bbox.py | 8 +++++++- src/models/ocr_model/train/train.py | 4 ++-- src/models/ocr_model/utils/to_katex.py | 1 + src/start_web.sh | 2 +- 4 files changed, 11 insertions(+), 4 deletions(-) diff --git a/src/models/det_model/Bbox.py b/src/models/det_model/Bbox.py index 93f4723..9784541 100644 --- a/src/models/det_model/Bbox.py +++ b/src/models/det_model/Bbox.py @@ -1,5 +1,8 @@ +import os + from PIL import Image, ImageDraw from typing import List +from pathlib import Path class Point: @@ -63,6 +66,9 @@ class Bbox: def draw_bboxes(img: Image.Image, bboxes: List[Bbox], name="annotated_image.png"): + curr_work_dir = Path(os.getcwd()) + log_dir = curr_work_dir / "logs" + log_dir.mkdir(exist_ok=True) drawer = ImageDraw.Draw(img) for bbox in bboxes: # Calculate the coordinates for the rectangle to be drawn @@ -82,4 +88,4 @@ def draw_bboxes(img: Image.Image, bboxes: List[Bbox], name="annotated_image.png" drawer.text((left, bottom - 10), bbox.content[:10], fill="red") # Save the image with drawn rectangles - img.save(name) \ No newline at end of file + img.save(log_dir / name) \ No newline at end of file diff --git a/src/models/ocr_model/train/train.py b/src/models/ocr_model/train/train.py index 2a4e2dd..840b123 100644 --- a/src/models/ocr_model/train/train.py +++ b/src/models/ocr_model/train/train.py @@ -38,7 +38,7 @@ def train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokeniz ) # trainer.train(resume_from_checkpoint=None) - trainer.train(resume_from_checkpoint='/home/lhy/code/TexTeller/src/models/ocr_model/train/train_result/TexTellerv3/checkpoint-644000') + trainer.train(resume_from_checkpoint='/home/lhy/code/TexTeller/src/models/ocr_model/train/train_result/TexTellerv3/checkpoint-788000') def evaluate(model, tokenizer, eval_dataset, collate_fn): @@ -96,7 +96,7 @@ if __name__ == '__main__': collate_fn_with_tokenizer = partial(collate_fn, tokenizer=tokenizer) # model = TexTeller() - model = TexTeller.from_pretrained('/home/lhy/code/TexTeller/src/models/ocr_model/train/train_result/TexTellerv3/checkpoint-644000') + model = TexTeller.from_pretrained('/home/lhy/code/TexTeller/src/models/ocr_model/train/train_result/TexTellerv3/checkpoint-788000') # ================= debug ======================= # foo = train_dataset[:50] diff --git a/src/models/ocr_model/utils/to_katex.py b/src/models/ocr_model/utils/to_katex.py index 5a0a95b..98a6acf 100644 --- a/src/models/ocr_model/utils/to_katex.py +++ b/src/models/ocr_model/utils/to_katex.py @@ -51,6 +51,7 @@ def change(input_str, old_inst, new_inst, old_surr_l, old_surr_r, new_surr_l, ne i = start + 1 continue else: + result += input_str[i:start] i = start if old_inst != new_inst and old_inst in result: diff --git a/src/start_web.sh b/src/start_web.sh index 6ec8f7b..ef96338 100755 --- a/src/start_web.sh +++ b/src/start_web.sh @@ -1,7 +1,7 @@ #!/usr/bin/env bash set -exu -export CHECKPOINT_DIR="default" +export CHECKPOINT_DIR="/home/lhy/code/TexTeller/src/models/ocr_model/train/train_result/TexTellerv3/checkpoint-788000" export TOKENIZER_DIR="default" streamlit run web.py From 1997145cf62ac1ab0994ed50e385da30770d3238 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B8=89=E6=B4=8B=E4=B8=89=E6=B4=8B?= <1258009915@qq.com> Date: Sun, 21 Apr 2024 13:06:01 +0000 Subject: [PATCH 2/2] Update README.md --- README.md | 5 +---- assets/README_zh.md | 27 +++++++++++---------------- 2 files changed, 12 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index 0a79ce2..fb2b5a2 100644 --- a/README.md +++ b/README.md @@ -29,7 +29,6 @@ TexTeller was trained with ~~550K~~7.5M image-formula pairs (dataset available [ * 📮[2024-04-12] Trained a **formula detection model**, thereby enhancing the capability to detect and recognize formulas in entire documents (whole-image inference)! - ## 🔑 Prerequisites python=3.10 @@ -85,9 +84,7 @@ TexTeller also supports **formula detection and recognition** on full images, al ### Download Weights -English documentation formula detection [[link](https://huggingface.co/TonyLee1256/texteller_det/resolve/main/rtdetr_r50vd_6x_coco_trained_on_IBEM_en_papers.onnx?download=true)]: Trained on 8272 images from the [IBEM dataset](https://zenodo.org/records/4757865). - -Chinese documentation formula detection [[link](https://huggingface.co/TonyLee1256/texteller_det/blob/main/rtdetr_r50vd_6x_coco_trained_on_cn_textbook.onnx)]: Trained on 2560 Chinese textbook images (100+ layouts). +Chinese-English documentation formula detection [[link](https://huggingface.co/TonyLee1256/texteller_det/resolve/main/rtdetr_r50vd_6x_coco.onnx?download=true)]: Trained on 3415 Chinese textbook images (130+ layouts). ### Formula Detection diff --git a/assets/README_zh.md b/assets/README_zh.md index b946d49..1b44cef 100644 --- a/assets/README_zh.md +++ b/assets/README_zh.md @@ -1,4 +1,4 @@ -📄 English | 中文 +📄 ``English`` | 中文

@@ -48,6 +48,7 @@ python=3.10 ``` 2. [安装pytorch](https://pytorch.org/get-started/locally/#start-locally) + 3. 安装本项目的依赖包: ```bash @@ -112,9 +113,7 @@ TexTeller还支持对整张图片进行**公式检测+公式识别**，从而对 ### 下载权重 -英文文档公式检测 [[link](https://huggingface.co/TonyLee1256/texteller_det/resolve/main/rtdetr_r50vd_6x_coco_trained_on_IBEM_en_papers.onnx?download=true)]：在8272张[IBEM数据集](https://zenodo.org/records/4757865)上训练得到 - -中文文档公式检测 [[link](https://huggingface.co/TonyLee1256/texteller_det/blob/main/rtdetr_r50vd_6x_coco_trained_on_cn_textbook.onnx)]：在2560张中文教材数据(100+版式)上训练得到 +中文英文文档公式检测 [[link](https://huggingface.co/TonyLee1256/texteller_det/resolve/main/rtdetr_r50vd_6x_coco.onnx?download=true)]：在3415张中文教材数据(130+版式)上训练得到 ### 公式检测 @@ -149,14 +148,14 @@ python server.py ``` | 参数 | 描述 | -| - | - | -| `-ckpt` | 权重文件的路径，*默认为TexTeller的预训练权重*。 | -| `-tknz` | 分词器的路径，*默认为TexTeller的分词器*。 | -| `-port` | 服务器的服务端口，*默认是8000*。 | -| `--inference-mode`| 是否使用GPU(cuda或mps)推理，*默认为CPU*。 | -| `--num_beams` | beam search的beam数量，*默认是1*。 | -| `--num_replicas`| 在服务器上运行的服务副本数量，*默认1个副本*。你可以使用更多的副本来获取更大的吞吐量。 | -| `--ncpu_per_replica` | 每个服务副本所用的CPU核心数，*默认为1*。 | +| --- | --- | +| `-ckpt` | 权重文件的路径，*默认为TexTeller的预训练权重*。| +| `-tknz` | 分词器的路径，*默认为TexTeller的分词器*。| +| `-port` | 服务器的服务端口，*默认是8000*。| +| `--inference-mode` | 是否使用GPU(cuda或mps)推理，*默认为CPU*。| +| `--num_beams` | beam search的beam数量，*默认是1*。| +| `--num_replicas` | 在服务器上运行的服务副本数量，*默认1个副本*。你可以使用更多的副本来获取更大的吞吐量。| +| `--ncpu_per_replica` | 每个服务副本所用的CPU核心数，*默认为1*。| | `--ngpu_per_replica` | 每个服务副本所用的GPU数量，*默认为1*。你可以把这个值设置成 0~1之间的数，这样会在一个GPU上运行多个服务副本来共享GPU，从而提高GPU的利用率。(注意，如果 --num_replicas 2, --ngpu_per_replica 0.7, 那么就必须要有2个GPU可用) | > [!NOTE] @@ -207,13 +206,9 @@ python -m models.ocr_model.train.train ## 📅 计划 - [X] ~~使用更大的数据集来训练模型(7.5M样本，即将发布)~~ - - [ ] 扫描图片识别 - - [ ] PDF文档识别 + 中英文场景支持 - - [ ] 推理加速 - - [ ] ... ## ⭐️ 观星曲线