diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index b6a299ba4009ba73afc9673fe008b97ca139c57b..1584bc76a9dd8ddff9d05a8cb693bcbd2e09fcde 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,11 +1,10 @@ -repos: - repo: https://github.com/PaddlePaddle/mirrors-yapf.git - rev: 0d79c0c469bab64f7229c9aca2b1186ef47f0e37 + sha: 0d79c0c469bab64f7229c9aca2b1186ef47f0e37 hooks: - id: yapf files: \.py$ - repo: https://github.com/pre-commit/pre-commit-hooks - rev: a11d9314b22d8f8c7556443875b731ef05965464 + sha: a11d9314b22d8f8c7556443875b731ef05965464 hooks: - id: check-merge-conflict - id: check-symlinks @@ -16,7 +15,7 @@ repos: - id: trailing-whitespace files: \.md$ - repo: https://github.com/Lucas-C/pre-commit-hooks - rev: v1.0.1 + sha: v1.0.1 hooks: - id: forbid-crlf files: \.md$ diff --git a/configs/rec/rec_d28_can.yml b/configs/rec/rec_d28_can.yml index aeaccb6b0d245cc4be479d17597d0029647db574..9fe936ae1111e08abd3734c67d29b7d39fe4e9e3 100644 --- a/configs/rec/rec_d28_can.yml +++ b/configs/rec/rec_d28_can.yml @@ -5,14 +5,14 @@ Global: print_batch_step: 10 save_model_dir: ./output/rec/can/ save_epoch_step: 1 - # evaluation is run every 1105 iterations + # evaluation is run every 1105 iterations (1 epoch)(batch_size = 8) eval_batch_step: [0, 1105] cal_metric_during_train: True - pretrained_model: ./output/rec/can/CAN - checkpoints: ./output/rec/can/CAN - save_inference_dir: ./inference/rec_d28_can/ + pretrained_model: + checkpoints: + save_inference_dir: use_visualdl: False - infer_img: doc/imgs_hme/hme_01.jpeg + infer_img: doc/datasets/crohme_demo/hme_00.jpg # for data or label process character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt max_text_length: 36 @@ -75,7 +75,7 @@ Metric: Train: dataset: - name: HMERDataSet + name: PGDataSet data_dir: ./train_data/CROHME/training/images/ transforms: - DecodeImage: @@ -83,19 +83,22 @@ Train: - GrayImageChannelFormat: normalize: True inverse: True + - SeqLabelEncode: + character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt + lower: False - KeepKeys: keep_keys: ['image', 'label'] - label_file_list: ["./train_data/CROHME/training/labels.json"] + label_file_list: ["./train_data/CROHME/training/labels.txt"] loader: shuffle: True - batch_size_per_card: 2 - drop_last: True - num_workers: 1 + batch_size_per_card: 8 + drop_last: False + num_workers: 4 collate_fn: DyMaskCollator Eval: dataset: - name: HMERDataSet + name: PGDataSet data_dir: ./train_data/CROHME/evaluation/images/ transforms: - DecodeImage: @@ -103,9 +106,12 @@ Eval: - GrayImageChannelFormat: normalize: True inverse: True + - SeqLabelEncode: + character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt + lower: False - KeepKeys: keep_keys: ['image', 'label'] - label_file_list: ["./train_data/CROHME/evaluation/labels.json"] + label_file_list: ["./train_data/CROHME/evaluation/labels.txt"] loader: shuffle: False drop_last: False diff --git a/doc/imgs_hme/hme_00.jpg b/doc/datasets/crohme_demo/hme_00.jpg similarity index 100% rename from doc/imgs_hme/hme_00.jpg rename to doc/datasets/crohme_demo/hme_00.jpg diff --git a/doc/imgs_hme/hme_01.jpg b/doc/datasets/crohme_demo/hme_01.jpg similarity index 100% rename from doc/imgs_hme/hme_01.jpg rename to doc/datasets/crohme_demo/hme_01.jpg diff --git a/doc/imgs_hme/hme_02.jpg b/doc/datasets/crohme_demo/hme_02.jpg similarity index 100% rename from doc/imgs_hme/hme_02.jpg rename to doc/datasets/crohme_demo/hme_02.jpg diff --git a/doc/doc_ch/algorithm_rec_can.md b/doc/doc_ch/algorithm_rec_can.md index 9585dae0cf9ecc215b4ff2f6d656345418e197d4..8a012b490458b2d804d2ab69953a3c6dff25347b 100644 --- a/doc/doc_ch/algorithm_rec_can.md +++ b/doc/doc_ch/algorithm_rec_can.md @@ -1,4 +1,4 @@ -# 手写数学公式识别算法-ABINet +# 手写数学公式识别算法-CAN - [1. 算法简介](#1) - [2. 环境配置](#2) @@ -27,7 +27,7 @@ |模型 |骨干网络|配置文件|ExpRate|下载链接| | ----- | ----- | ----- | ----- | ----- | -|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72|[训练模型](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar)| +|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72|[训练模型](https://paddleocr.bj.bcebos.com/contribution/can_train.tar)| ## 2. 环境配置 @@ -60,16 +60,21 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs python3 tools/train.py -c configs/rec/rec_d28_can.yml -o Train.dataset.transforms.GrayImageChannelFormat.inverse=False ``` +- 默认每训练1个epoch（1105次iteration）进行1次评估，若您更改训练的batch_size，或更换数据集，请在训练时作出如下修改 +``` +python3 tools/train.py -c configs/rec/rec_d28_can.yml +-o Global.eval_batch_step=[0, {length_of_dataset//batch_size}] +``` # ### 3.2 评估 -可下载已训练完成的[模型文件](#model)，使用如下命令进行评估： +可下载已训练完成的[模型文件](https://paddleocr.bj.bcebos.com/contribution/can_train.tar)，使用如下命令进行评估： ```shell -# 注意将pretrained_model的路径设置为本地路径。 -python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/best_accuracy +# 注意将pretrained_model的路径设置为本地路径。若使用自行训练保存的模型，请注意修改路径和文件名为{path/to/weights}/{model_name}。 +python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/CAN ``` @@ -78,9 +83,9 @@ python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec 使用如下命令进行单张图片预测： ```shell # 注意将pretrained_model的路径设置为本地路径。 -python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.attdecoder.is_train=False Global.infer_img='./doc/imgs_hme/hme_01.jpg' Global.pretrained_model=./rec_d28_can_train/best_accuracy +python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.attdecoder.is_train=False Global.infer_img='./doc/datasets/crohme_demo/hme_00.jpg' Global.pretrained_model=./rec_d28_can_train/CAN -# 预测文件夹下所有图像时，可修改infer_img为文件夹，如 Global.infer_img='./doc/imgs_hme/'。 +# 预测文件夹下所有图像时，可修改infer_img为文件夹，如 Global.infer_img='./doc/datasets/crohme_demo/'。 ``` @@ -89,17 +94,16 @@ python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.a ### 4.1 Python推理 -首先将训练得到best模型，转换成inference model。这里以训练完成的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/rec_d28_can_train.tar) )，可以使用如下命令进行转换： +首先将训练得到best模型，转换成inference model。这里以训练完成的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/contribution/can_train.tar) )，可以使用如下命令进行转换： ```shell # 注意将pretrained_model的路径设置为本地路径。 -python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.save_inference_dir=./inference/rec_d28_can/ Architecture.Head.attdecoder.is_train=False +python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/CAN Global.save_inference_dir=./inference/rec_d28_can/ Architecture.Head.attdecoder.is_train=False # 目前的静态图模型默认的输出长度最大为36，如果您需要预测更长的序列，请在导出模型时指定其输出序列为合适的值，例如 Architecture.Head.max_text_length=72 ``` **注意：** - 如果您是在自己的数据集上训练的模型，并且调整了字典文件，请注意修改配置文件中的`character_dict_path`是否是所需要的字典文件。 -- 如果您修改了训练时的输入大小，请修改`tools/export_model.py`文件中的对应ABINet的`infer_shape`。 转换成功后，在目录下有三个文件： ``` @@ -112,18 +116,18 @@ python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.save_infe 执行如下命令进行模型推理： ```shell -python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_hme/hme_01.jpg" --rec_algorithm="CAN" --rec_batch_num=1 --rec_model_dir="./inference/rec_d28_can/" --rec_char_dict_path="./ppocr/utils/dict/latex_symbol_dict.txt" +python3 tools/infer/predict_rec.py --image_dir="./doc/datasets/crohme_demo/hme_00.jpg" --rec_algorithm="CAN" --rec_batch_num=1 --rec_model_dir="./inference/rec_d28_can/" --rec_char_dict_path="./ppocr/utils/dict/latex_symbol_dict.txt" -# 预测文件夹下所有图像时，可修改image_dir为文件夹，如 --image_dir='./doc/imgs_hme/'。 +# 预测文件夹下所有图像时，可修改image_dir为文件夹，如 --image_dir='./doc/datasets/crohme_demo/'。 # 如果您需要在白底黑字的图片上进行预测，请设置 --rec_image_inverse=False ``` -![测试图片样例](../imgs_hme/hme_00.jpg) +![测试图片样例](../datasets/crohme_demo/hme_00.jpg) 执行命令后，上面图像的预测结果（识别的文本）会打印到屏幕上，示例如下： ```shell -Predicts of ./doc/imgs_hme/hme_03.jpg:['x _ { k } x x _ { k } + y _ { k } y x _ { k }', []] +Predicts of ./doc/imgs_hme/hme_00.jpg:['x _ { k } x x _ { k } + y _ { k } y x _ { k }', []] ``` diff --git a/doc/doc_en/algorithm_rec_can_en.md b/doc/doc_en/algorithm_rec_can_en.md index 4d7a64f994e75c0ca3b2f6238465e3ad418b89a5..da6c9c6096fa7170b108012165b7c69862671e1a 100644 --- a/doc/doc_en/algorithm_rec_can_en.md +++ b/doc/doc_en/algorithm_rec_can_en.md @@ -25,7 +25,7 @@ Using CROHME handwrittem mathematical expression recognition datasets for traini |Model|Backbone|config|exprate|Download link| | --- | --- | --- | --- | --- | -|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72|coming soon| +|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72|[trained model](https://paddleocr.bj.bcebos.com/contribution/can_train.tar)| ## 2. Environment @@ -53,14 +53,14 @@ Evaluation: ``` # GPU evaluation -python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/best_accuracy +python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/CAN ``` Prediction: ``` # The configuration file used for prediction must match the training -python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.attdecoder.is_train=False Global.infer_img='./doc/imgs_hme/hme_01.jpg' Global.pretrained_model=./rec_d28_can_train/best_accuracy +python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.attdecoder.is_train=False Global.infer_img='./doc/crohme_demo/hme_00.jpg' Global.pretrained_model=./rec_d28_can_train/CAN ``` @@ -68,16 +68,20 @@ python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.a ### 4.1 Python Inference -First, the model saved during the RobustScanner text recognition training process is converted into an inference model. you can use the following command to convert: +First, the model saved during the CAN handwritten mathematical expression recognition training process is converted into an inference model. you can use the following command to convert: ``` python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.save_inference_dir=./inference/rec_d28_can/ Architecture.Head.attdecoder.is_train=False + +# The default output max length of the model is 36. If you need to predict a longer sequence, please specify its output sequence as an appropriate value when exporting the model, as: Architecture.Head.max_ text_ length=72 ``` -For RobustScanner text recognition model inference, the following commands can be executed: +For CAN handwritten mathematical expression recognition model inference, the following commands can be executed: ``` -python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_hme/hme_01.jpg" --rec_algorithm="CAN" --rec_batch_num=1 --rec_model_dir="./inference/rec_d28_can/" --rec_image_shape="1, 100, 100" --rec_char_dict_path="./ppocr/utils/dict/latex_symbol_dict.txt" +python3 tools/infer/predict_rec.py --image_dir="./doc/crohme_demo/hme_00.jpg" --rec_algorithm="CAN" --rec_batch_num=1 --rec_model_dir="./inference/rec_d28_can/" --rec_char_dict_path="./ppocr/utils/dict/latex_symbol_dict.txt" + +# If you need to predict on a picture with black characters on a white background, please set: -- rec_ image_ inverse=False ``` diff --git a/ppocr/data/__init__.py b/ppocr/data/__init__.py index 1f3de63de72f44a7daf8d641b2d5c6bc5568df8f..b602a346dbe4b0d45af287f25f05ead0f62daf44 100644 --- a/ppocr/data/__init__.py +++ b/ppocr/data/__init__.py @@ -37,7 +37,6 @@ from ppocr.data.simple_dataset import SimpleDataSet from ppocr.data.lmdb_dataset import LMDBDataSet, LMDBDataSetSR from ppocr.data.pgnet_dataset import PGDataSet from ppocr.data.pubtab_dataset import PubTabDataSet -from ppocr.data.hmer_dataset import HMERDataSet __all__ = ['build_dataloader', 'transform', 'create_operators'] @@ -56,7 +55,7 @@ def build_dataloader(config, mode, device, logger, seed=None): support_dict = [ 'SimpleDataSet', 'LMDBDataSet', 'PGDataSet', 'PubTabDataSet', - 'LMDBDataSetSR', 'HMERDataSet' + 'LMDBDataSetSR' ] module_name = config[mode]['dataset']['name'] assert module_name in support_dict, Exception( diff --git a/ppocr/data/collate_fn.py b/ppocr/data/collate_fn.py index fec1e895ff033cd991ed3a927ad7e158989bab45..067b2158aca183c68c3a09999483c059bb10eb14 100644 --- a/ppocr/data/collate_fn.py +++ b/ppocr/data/collate_fn.py @@ -95,8 +95,8 @@ class DyMaskCollator(object): 1] > max_height else max_height max_width = item[0].shape[2] if item[0].shape[ 2] > max_width else max_width - max_length = item[1].shape[0] if item[1].shape[ - 0] > max_length else max_length + max_length = len(item[1]) if len(item[ + 1]) > max_length else max_length proper_items.append(item) images, image_masks = np.zeros( @@ -111,7 +111,7 @@ class DyMaskCollator(object): _, h, w = proper_items[i][0].shape images[i][:, :h, :w] = proper_items[i][0] image_masks[i][:, :h, :w] = 1 - l = proper_items[i][1].shape[0] + l = len(proper_items[i][1]) labels[i][:l] = proper_items[i][1] label_masks[i][:l] = 1 diff --git a/ppocr/data/hmer_dataset.py b/ppocr/data/hmer_dataset.py deleted file mode 100644 index d5d92f264b2604d68929bf08e8514c1a87b9198b..0000000000000000000000000000000000000000 --- a/ppocr/data/hmer_dataset.py +++ /dev/null @@ -1,99 +0,0 @@ -# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import os, json, random, traceback -import numpy as np - -from PIL import Image -from paddle.io import Dataset - -from .imaug import transform, create_operators - - -class HMERDataSet(Dataset): - def __init__(self, config, mode, logger, seed=None): - super(HMERDataSet, self).__init__() - - self.logger = logger - self.seed = seed - self.mode = mode - - global_config = config['Global'] - dataset_config = config[mode]['dataset'] - self.data_dir = config[mode]['dataset']['data_dir'] - - label_file_list = dataset_config['label_file_list'] - data_source_num = len(label_file_list) - ratio_list = dataset_config.get("ratio_list", [1.0]) - - self.data_lines, self.labels = self.get_image_info_list(label_file_list, - ratio_list) - self.data_idx_order_list = list(range(len(self.data_lines))) - if self.mode == "train" and self.do_shuffle: - self.shuffle_data_random() - - if isinstance(ratio_list, (float, int)): - ratio_list = [float(ratio_list)] * int(data_source_num) - - assert len( - ratio_list - ) == data_source_num, "The length of ratio_list should be the same as the file_list." - - self.ops = create_operators(dataset_config['transforms'], global_config) - self.need_reset = True in [x < 1 for x in ratio_list] - - def get_image_info_list(self, file_list, ratio_list): - if isinstance(file_list, str): - file_list = [file_list] - labels = {} - for idx, file in enumerate(file_list): - with open(file, "r") as f: - lines = json.load(f) - labels.update(lines) - data_lines = [name for name in labels.keys()] - return data_lines, labels - - def shuffle_data_random(self): - random.seed(self.seed) - random.shuffle(self.data_lines) - return - - def __len__(self): - return len(self.data_idx_order_list) - - def __getitem__(self, idx): - file_idx = self.data_idx_order_list[idx] - data_name = self.data_lines[file_idx] - try: - file_name = data_name + '.jpg' - img_path = os.path.join(self.data_dir, file_name) - if not os.path.exists(img_path): - raise Exception("{} does not exist!".format(img_path)) - with open(img_path, 'rb') as f: - img = f.read() - - label = self.labels.get(data_name).split() - label = np.array([int(item) for item in label]) - - data = {'image': img, 'label': label} - outs = transform(data, self.ops) - except: - self.logger.error( - "When parsing line {}, error happened with msg: {}".format( - file_name, traceback.format_exc())) - outs = None - if outs is None: - # during evaluation, we should fix the idx to get same results for many times of evaluation. - rnd_idx = np.random.randint(self.__len__()) - return self.__getitem__(rnd_idx) - return outs diff --git a/ppocr/data/imaug/label_ops.py b/ppocr/data/imaug/label_ops.py index 2a2ac2decd1abf4daf2c5325a8f69fc26f4fc0ef..ae916b2ee7003d9b96933f042c4c756f9105be40 100644 --- a/ppocr/data/imaug/label_ops.py +++ b/ppocr/data/imaug/label_ops.py @@ -1476,4 +1476,33 @@ class CTLabelEncode(object): data['polys'] = boxes data['texts'] = txts - return data \ No newline at end of file + return data + + +class SeqLabelEncode(BaseRecLabelEncode): + def __init__(self, + character_dict_path, + max_text_length=100, + use_space_char=False, + lower=True, + **kwargs): + super(SeqLabelEncode, self).__init__( + max_text_length, character_dict_path, use_space_char, lower) + + def encode(self, text_seq): + text_seq_encoded = [] + for text in text_seq: + if text not in self.character: + continue + text_seq_encoded.append(self.dict.get(text)) + if len(text_seq_encoded) == 0: + return None + return text_seq_encoded + + def __call__(self, data): + label = data['label'] + if isinstance(label, str): + label = label.strip().split() + label.append(self.end_str) + data['label'] = self.encode(label) + return data diff --git a/test_tipc/configs/rec_d28_can/rec_d28_can.yml b/test_tipc/configs/rec_d28_can/rec_d28_can.yml index aeaccb6b0d245cc4be479d17597d0029647db574..ac7b07712621d40aa33e2f0dd53c907b21d2e5f9 100644 --- a/test_tipc/configs/rec_d28_can/rec_d28_can.yml +++ b/test_tipc/configs/rec_d28_can/rec_d28_can.yml @@ -5,14 +5,14 @@ Global: print_batch_step: 10 save_model_dir: ./output/rec/can/ save_epoch_step: 1 - # evaluation is run every 1105 iterations + # evaluation is run every 1105 iterations (1 epoch)(batch_size = 8) eval_batch_step: [0, 1105] cal_metric_during_train: True - pretrained_model: ./output/rec/can/CAN - checkpoints: ./output/rec/can/CAN - save_inference_dir: ./inference/rec_d28_can/ + pretrained_model: + checkpoints: + save_inference_dir: use_visualdl: False - infer_img: doc/imgs_hme/hme_01.jpeg + infer_img: doc/datasets/crohme_demo/hme_00.jpg # for data or label process character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt max_text_length: 36 @@ -75,37 +75,43 @@ Metric: Train: dataset: - name: HMERDataSet - data_dir: ./train_data/CROHME/training/images/ + name: PGDataSet + data_dir: ./train_data/CROHME_lite/training/images/ transforms: - DecodeImage: channel_first: False - GrayImageChannelFormat: normalize: True inverse: True + - SeqLabelEncode: + character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt + lower: False - KeepKeys: keep_keys: ['image', 'label'] - label_file_list: ["./train_data/CROHME/training/labels.json"] + label_file_list: ["./train_data/CROHME_lite/training/labels.txt"] loader: shuffle: True - batch_size_per_card: 2 - drop_last: True - num_workers: 1 + batch_size_per_card: 8 + drop_last: False + num_workers: 4 collate_fn: DyMaskCollator Eval: dataset: - name: HMERDataSet - data_dir: ./train_data/CROHME/evaluation/images/ + name: PGDataSet + data_dir: ./train_data/CROHME_lite/evaluation/images/ transforms: - DecodeImage: channel_first: False - GrayImageChannelFormat: normalize: True inverse: True + - SeqLabelEncode: + character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt + lower: False - KeepKeys: keep_keys: ['image', 'label'] - label_file_list: ["./train_data/CROHME/evaluation/labels.json"] + label_file_list: ["./train_data/CROHME_lite/evaluation/labels.txt"] loader: shuffle: False drop_last: False diff --git a/test_tipc/configs/rec_d28_can/train_infer_python.txt b/test_tipc/configs/rec_d28_can/train_infer_python.txt index be50c59805b2f1b8a0baa5a61b9d5cd4a21b68df..731d327cd085b41a6bade9b7092dda7b2de9d9f9 100644 --- a/test_tipc/configs/rec_d28_can/train_infer_python.txt +++ b/test_tipc/configs/rec_d28_can/train_infer_python.txt @@ -1,6 +1,6 @@ ===========================train_params=========================== model_name:rec_d28_can -python:python +python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True Global.auto_cast:null @@ -9,7 +9,7 @@ Global.save_model_dir:./output/ Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=8 Global.pretrained_model:null train_model_name:latest -train_infer_img_dir:./doc/imgs_hme +train_infer_img_dir:./doc/datasets/crohme_demo null:null ## trainer:norm_train @@ -37,15 +37,15 @@ export2:null train_model:./inference/rec_d28_can_train/best_accuracy infer_export:tools/export_model.py -c test_tipc/configs/rec_d28_can/rec_d28_can.yml -o infer_quant:False -inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/dict/latex_symbol_dict.txt --rec_image_shape="1,100,100" --rec_algorithm="CAN" +inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/dict/latex_symbol_dict.txt --rec_algorithm="CAN" --use_gpu:True|False --enable_mkldnn:False --cpu_threads:6 --rec_batch_num:1 --use_tensorrt:False --precision:fp32 ---rec_model_dir:./output/ ---image_dir:./doc/imgs_hme +--rec_model_dir: +--image_dir:./doc/datasets/crohme_demo --save_log_path:./test/output/ --benchmark:True null:null diff --git a/test_tipc/prepare.sh b/test_tipc/prepare.sh index 5ca426e28cb6c2d499e5bfeca7e997e6175b0ce5..4aab17019f39edd15288b51564177b4b4f4b36c4 100644 --- a/test_tipc/prepare.sh +++ b/test_tipc/prepare.sh @@ -257,6 +257,13 @@ if [ ${MODE} = "lite_train_lite_infer" ];then wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/rec_r32_gaspin_bilstm_att_train.tar --no-check-certificate cd ./pretrain_models/ && tar xf rec_r32_gaspin_bilstm_att_train.tar && cd ../ fi + if [ ${model_name} == "rec_d28_can" ]; then + wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/contribution/can_train.tar --no-check-certificate + cd ./pretrain_models/ && tar xf can_train.tar && cd ../ + wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/CROHME_lite.tar --no-check-certificate + cd ./train_data/ && tar xf CROHME_lite.tar && cd ../ + + fi if [ ${model_name} == "layoutxlm_ser" ]; then ${python_name} -m pip install -r ppstructure/kie/requirements.txt ${python_name} -m pip install opencv-python -U diff --git a/test_tipc/readme.md b/test_tipc/readme.md index 1442ee1c86a7c1319446a0eb22c08287e1ce689a..9f02c2e3084585618cb1424b6858d16b79494d9b 100644 --- a/test_tipc/readme.md +++ b/test_tipc/readme.md @@ -44,6 +44,7 @@ | SAST |det_r50_vd_sast_totaltext_v2.0 | 检测 | 支持 | 多机多卡
混合精度 | - | - | | Rosetta|rec_mv3_none_none_ctc_v2.0 | 识别 | 支持 | 多机多卡
混合精度 | - | - | | Rosetta|rec_r34_vd_none_none_ctc_v2.0 | 识别 | 支持 | 多机多卡
混合精度 | - | - | +| CAN |rec_d28_can | 识别 | 支持 | 多机多卡
混合精度 | - | - | | CRNN |rec_mv3_none_bilstm_ctc_v2.0 | 识别 | 支持 | 多机多卡
混合精度 | - | - | | CRNN |rec_r34_vd_none_bilstm_ctc_v2.0| 识别 | 支持 | 多机多卡
混合精度 | - | - | | StarNet|rec_mv3_tps_bilstm_ctc_v2.0 | 识别 | 支持 | 多机多卡
混合精度 | - | - | diff --git a/tools/program.py b/tools/program.py index c491247a64697774cca73e209b714f7560c5fcdd..a0594e950d969c39eb1cb363435897c5f219f0e4 100755 --- a/tools/program.py +++ b/tools/program.py @@ -544,7 +544,7 @@ def eval(model, elif model_type in ['sr']: eval_class(preds, batch_numpy) elif model_type in ['can']: - eval_class(preds[0], batch_numpy[2:], epoch_reset=False) + eval_class(preds[0], batch_numpy[2:], epoch_reset=(idx == 0)) else: post_result = post_process_class(preds, batch_numpy[1]) eval_class(post_result, batch_numpy)