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View file @ cf613b13
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"# Text detection FAQ\n",
"\n",
"This section lists some of the problems that developers often encounter when using PaddleOCR's text detection model, and gives corresponding solutions or suggestions.\n",
"\n",
"The FAQ is introduced in two parts, namely:\n",
" -Text detection training related\n",
" -Text detection and prediction related"
]
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"## 1. FAQ about Text Detection Training\n",
"\n",
"**1.1 What are the text detection algorithms provided by PaddleOCR?**\n",
"\n",
"**A**: PaddleOCR contains a variety of text detection models, including regression-based text detection methods EAST and SAST, and segmentation-based text detection methods DB, PSENet.\n",
"\n",
"\n",
"**1.2: What data sets are used in the Chinese ultra-lightweight and general models in the PaddleOCR project? How many samples were trained, what configuration of GPUs, how many epochs were run, and how long did they run?**\n",
"\n",
"**A**: For the ultra-lightweight DB detection model, the training data includes open source data sets lsvt, rctw, CASIA, CCPD, MSRA, MLT, BornDigit, iflytek, SROIE and synthetic data sets, etc. The total data volume is 10W, The data set is divided into 5 parts. A random sampling strategy is used during training. The training takes about 500 epochs on a 4-card V100GPU, which takes 3 days.\n",
"\n",
"\n",
"**1.3 Does the text detection training label require specific text labeling? What does the \"###\" in the label mean?**\n",
"\n",
"**A**: Text detection training only needs the coordinates of the text area. The label can be four or fourteen points, arranged in the order of upper left, upper right, lower right, and lower left. The label file provided by PaddleOCR contains text fields. For unclear text in the text area, ### will be used instead. When training the detection model, the text field in the label will not be used.\n",
" \n",
"**1.4 Is the effect of the text detection model trained when the text lines are tight?**\n",
"\n",
"**A**: When using segmentation-based methods, such as DB, to detect dense text lines, it is best to collect a batch of data for training, and during training, a binary image will be generated [shrink_ratio](https://github.com/PaddlePaddle/PaddleOCR/blob/8b656a3e13631dfb1ac21d2095d4d4a4993ef710/ppocr/data/imaug/make_shrink_map.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L37)Turn down the parameter. In addition, when forecasting, you can appropriately reduce [unclip_ratio](https://github.com/PaddlePaddle/PaddleOCR/blob/8b656a3e13631dfb1ac21d2095d4d4a4993ef710/configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L59) parameter, the larger the unclip_ratio parameter value, the larger the detection frame.\n",
"\n",
"\n",
"**1.5 For some large-sized document images, DB will have more missed inspections during inspection. How to avoid this kind of missed inspections?**\n",
"\n",
"**A**: First of all, you need to determine whether the model is not well-trained or is the problem handled during prediction. If the model is not well trained, it is recommended to add more data for training, or add more data to enhance it during training.\n",
"If the problem is that the predicted image is too large, you can increase the longest side setting parameter [det_limit_side_len] entered during prediction [det_limit_side_len](https://github.com/PaddlePaddle/PaddleOCR/blob/8b656a3e13631dfb1ac21d2095d4d4a4993ef710/tools/infer/utility.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L47), which is 960 by default.\n",
"Secondly, you can observe whether the missed text has segmentation results by visualizing the post-processed segmentation map. If there is no segmentation result, the model is not well trained. If there is a complete segmentation area, it means that it is a problem of post-prediction processing. In this case, it is recommended to adjust [DB post-processing parameters](https://github.com/PaddlePaddle/PaddleOCR/blob/8b656a3e13631dfb1ac21d2095d4d4a4993ef710/tools/infer/utility.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L51-L53)。\n",
"\n",
"\n",
"**1.6 The problem of missed detection of DB model bending text (such as a slightly deformed document image)?**\n",
"\n",
"**A**: When calculating the average score of the text box in the DB post-processing, it is the average score of the rectangle area, which is easy to cause the missed detection of the curved text. The average score of the polygon area has been added, which will be more accurate, but the speed is somewhat different. Decrease, can be selected as needed, and you can view the [Visual Contrast Effect] (https://github.com/PaddlePaddle/PaddleOCR/pull/2604) in the relevant pr. This function is selected by the parameter [det_db_score_mode](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/tools/infer/utility.py#L51), the parameter value is optional [`fast` (default) , `slow`], `fast` corresponds to the original rectangle mode, and `slow` corresponds to the polygon mode. Thanks to the user [buptlihang](https://github.com/buptlihang) for mentioning [pr](https://github.com/PaddlePaddle/PaddleOCR/pull/2574) to help solve this problem.\n",
"\n",
"\n",
"**1.7 For simple OCR tasks with low accuracy requirements, how many data sets do I need to prepare?**\n",
"\n",
"**A**: (1) The amount of training data is related to the complexity of the problem to be solved. The greater the difficulty and the higher the accuracy requirements, the greater the data set requirements, and in general, the more training data in practice, the better the effect.\n",
"\n",
"(2) For scenes with low accuracy requirements, the amount of data required for detection tasks and recognition tasks is different. For inspection tasks, 500 images can guarantee the basic inspection results. For recognition tasks, it is necessary to ensure that the number of line text images in which each character in the recognition dictionary appears in different scenes needs to be greater than 200 (for example, if there are 5 words in the dictionary, each word needs to appear in more than 200 pictures, then The minimum required number of images should be between 200-1000), so that the basic recognition effect can be guaranteed.\n",
"\n",
"\n",
"**1.8 How to get more data when the amount of training data is small?**\n",
"\n",
"**A**: When the amount of training data is small, you can try the following three ways to get more data: (1) Collect more training data manually, the most direct and effective way. (2) Basic image processing or transformation based on PIL and opencv. For example, the three modules of ImageFont, Image, ImageDraw in PIL write text into the background, opencv's rotating affine transformation, Gaussian filtering and so on. (3) Synthesize data using data generation algorithms, such as algorithms such as pix2pix.\n",
"\n",
"\n",
"**1.9 How to replace the backbone of text detection/recognition?**\n",
"\n",
"A: Whether it is text detection or text recognition, the choice of backbone network is a trade-off between prediction effect and prediction efficiency. Generally, if you choose a larger-scale backbone network, such as ResNet101_vd, the detection or recognition will be more accurate, but the prediction time will increase accordingly. However, choosing a smaller-scale backbone network, such as MobileNetV3_small_x0_35, will predict faster, but the accuracy of detection or recognition will be greatly reduced. Fortunately, the detection or recognition effects of different backbone networks are positively correlated with the image 1000 classification task in the ImageNet dataset. PaddleClas, a flying paddle image classification suite, summarizes 23 series of classification network structures such as ResNet_vd, Res2Net, HRNet, MobileNetV3, GhostNet, etc. The top1 recognition accuracy rate of the above image classification task, GPU (V100 and T4) and CPU (Snapdragon 855) The prediction time-consuming and the corresponding 117 pre-training model download addresses.\n",
"\n",
"(1) The replacement of the text detection backbone network is mainly to determine 4 stages similar to ResNet to facilitate the integration of subsequent detection heads similar to FPN. In addition, for the text detection problem, the classification pre-training model trained by ImageNet can accelerate the convergence and improve the effect.\n",
"\n",
"(2) The replacement of the backbone network for text recognition requires attention to the drop position of the network width and height stride. Since text recognition generally has a large ratio of width to height, the frequency of height reduction is less, and the frequency of width reduction is more. You can refer to [Changes to the MobileNetV3 backbone network in PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR/blob/release%2F2.3/ppocr/modeling/backbones/rec_mobilenet_v3.py)。\n",
"\n",
"\n",
"**1.10 How to finetune the detection model, such as freezing the previous layer or learning with a small learning rate for some layers?**\n",
"\n",
"**A**: If you freeze certain layers, you can set the stop_gradient property of the variable to True, so that all the parameters before calculating this variable will not be updated, refer to: https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/faq/train_cn.html#id4\n",
"\n",
"If learning with a smaller learning rate for some layers is not very convenient in the static graph, one method is to set a fixed learning rate for the weight attribute when the parameters are initialized, refer to: https://www.paddlepaddle.org.cn/documentation/docs/en/develop/api/paddle/fluid/param_attr/ParamAttr_cn.html#paramattr\n",
"\n",
"In fact, our experiment found that directly loading the model to fine-tune without setting different learning rates of certain layers, the effect is also good.\n",
"\n",
"**1.11 In the preprocessing part of DB, why should the length and width of the picture be processed into multiples of 32?**\n",
"\n",
"**A**: It is related to the stride of the network downsampling. Take the resnet backbone network under inspection as an example. After the image is input to the network, it needs to be downsampled by 2 times for 5 times, a total of 32 times. Therefore, it is recommended that the input image size be a multiple of 32.\n",
"\n",
"\n",
"**1.12 In the PP-OCR series models, why does the backbone network for text detection not use SEBlock?**\n",
"\n",
"**A**: The SE module is an important module of the MobileNetV3 network. Its purpose is to estimate the importance of each feature channel of the feature map, assign weights to each feature of the feature map, and improve the expressive ability of the network. However, for text detection, the resolution of the input network is relatively large, generally 640\\*640. It is difficult to use the SE module to estimate the importance of each feature channel of the feature map. The network improvement ability is limited, but the module is relatively time-consuming. In the PP-OCR system, the backbone network for text detection does not use the SE module. Experiments also show that when the SE module is removed, the size of the ultra-lightweight model can be reduced by 40%, and the text detection effect is basically not affected. For details, please refer to the PP-OCR technical article, https://arxiv.org/abs/2009.09941.\n",
"\n",
"\n",
"**1.13 The PP-OCR detection effect is not good, how to optimize it?**\n",
"\n",
"**A**: Specific analysis of specific issues:\n",
"- If the detection effect is not available on your scene, the first choice is to do finetune training on your data;\n",
"- If the image is too large and the text is too dense, it is recommended not to over-compress the image. You can try to modify the resize logic of the detection preprocessing to prevent the image from being over-compressed;\n",
"- The size of the detection frame is too close to the text or the detection frame is too large, you can adjust the db_unclip_ratio parameter, increasing the parameter can enlarge the detection frame, and reducing the parameter can reduce the size of the detection frame;\n",
"- There are many missed detection problems in the detection frame, which can reduce the threshold parameter det_db_box_thresh for DB detection to prevent some detection frames from being filtered out. You can also try to set det_db_score_mode to'slow';\n",
"- Other methods can choose use_dilation as True to expand the feature map of the detection output. In general, the effect will be improved.\n",
"\n",
"\n",
"## 2. FAQ about Text Detection and Prediction\n",
"\n",
"**2.1 In DB, some boxes are too pasted with text, but some corners of the text are removed to affect the recognition. Is there any way to alleviate this problem?**\n",
"\n",
"**A**: The post-processing parameter [unclip_ratio](https://github.com/PaddlePaddle/PaddleOCR/blob/d80afce9b51f09fd3d90e539c40eba8eb5e50dd6/tools/infer/utility.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L52) can be appropriately increased. the larger the parameter, the larger the text box.\n",
"\n",
"\n",
"**2.2 Why does the PaddleOCR detection prediction only support one image test? That is, test_batch_size_per_card=1**\n",
"\n",
"**A**: When predicting, the image is scaled in equal proportions, the longest side is 960, and the length and width of different images after scaling in equal proportions are inconsistent, and they cannot form a batch, so set test_batch_size to 1.\n",
"\n",
"\n",
"**2.3 Accelerate PaddleOCR's text detection model prediction on the CPU?**\n",
"\n",
"**A**: x86 CPU can use mkldnn (OneDNN) for acceleration; enable [enable_mkldnn](https://github.com/PaddlePaddle/PaddleOCR/blob/8b656a3e13631dfb1ac21d2095d4d4a4993ef710/tools/infer/utility.py#L105) Parameters. In addition, in conjunction with increasing the number of threads used for prediction on the CPU, [num_threads](https://github.com/PaddlePaddle/PaddleOCR/blob/8b656a3e13631dfb1ac21d2095d4d4a4993ef710/tools/infer/utility.py#L106) can effectively speed up the prediction speed on the CPU.\n",
"\n",
"**2.4 Accelerate PaddleOCR's text detection model prediction on GPU?**\n",
"\n",
"**A**: TensorRT is recommended for GPU accelerated prediction.\n",
"- 1. Download the Paddle installation package or prediction library with TensorRT from [link](https://paddleinference.paddlepaddle.org.cn/master/user_guides/download_lib.html).\n",
"- 2. Download the [TensorRT](https://developer.nvidia.com/tensorrt) from the Nvidia official website. Note that the downloaded TensorRT version is consistent with the TensorRT version compiled in the paddle installation package.\n",
"- 3. Set the environment variable `LD_LIBRARY_PATH` to point to the lib folder of TensorRT\n",
"```\n",
"export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<TensorRT-${version}/lib>\n",
"```\n",
"- 4. Enable [tensorrt option](https://github.com/PaddlePaddle/PaddleOCR/blob/8b656a3e13631dfb1ac21d2095d4d4a4993ef710/tools/infer/utility.py?_pjax=%23js-repo-pjax-container%2%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L38).\n",
"\n",
"**2.5 How to deploy PaddleOCR model on the mobile terminal?**\n",
"\n",
"**A**: Flying Oar Paddle has a special tool for mobile deployment [PaddleLite](https://github.com/PaddlePaddle/Paddle-Lite), and PaddleOCR provides DB+CRNN as the demo android arm deployment code , Refer to [link](https://github.com/PaddlePaddle/PaddleOCR/blob/release%2F2.3/deploy/lite/readme.md).\n",
"\n",
"\n",
"**2.6 How to use PaddleOCR multi-process prediction?**\n",
"\n",
"**A**: PaddleOCR recently added [Multi-Process Predictive Control Parameters](https://github.com/PaddlePaddle/PaddleOCR/blob/8b656a3e13631dfb1ac21d2095d4d4a4993ef710/tools/infer/utility.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L111), `use_mp` indicates whether When using multiple processes, `total_process_num` indicates the number of processes when using multiple processes. For specific usage, please refer to [document](https://github.com/PaddlePaddle/PaddleOCR/blob/release%2F2.3/doc/doc_ch/inference.md#1-%E8%B6%85%E8%BD%BB%E9%87%8F%E4%B8%AD%E6%96%87ocr%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86).\n",
"\n",
"**2.7 Video memory explosion and memory leak during prediction?**\n",
"\n",
"**A**: If it is the prediction of the training model, the video memory is not enough because the model is too large or the input image is too large, you can refer to the code and add paddle.no_grad() before the main function runs to reduce the video memory usage. If the memory usage of the inference model is too high, you can add [config.enable_memory_optim()](https://github.com/PaddlePaddle/PaddleOCR/blob/8b656a3e13631dfb1ac21d2095d4d4a4993ef710/tools/infer/utility.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L267) to reduce the memory usage when configuring Config.\n",
"\n",
"In addition, regarding the memory leak when using Paddle to predict, it is recommended to install the latest version of paddle. The memory leak has been fixed."
]
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notebook/notebook_en/how_to_use_these_notebooks.ipynb 0 → 100644
View file @ cf613b13
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"# 1. Course Prerequisites\n",
"\n",
"The OCR model involved in this course is based on deep learning, so its related basic knowledge, environment configuration, project engineering and other materials will be introduced in this section, especially for readers who are not familiar with deep learning. content.\n",
"\n",
"### 1.1 Preliminary Knowledge\n",
"\n",
"The \"learning\" of deep learning has been developed from the content of neurons, perceptrons, and multilayer neural networks in machine learning. Therefore, understanding the basic machine learning algorithms is of great help to the understanding and application of deep learning. The \"deepness\" of deep learning is embodied in a series of vector-based mathematical operations such as convolution and pooling used in the process of processing a large amount of information. If you lack the theoretical foundation of the two, you can learn from teacher Li Hongyi's [Linear Algebra](https://aistudio.baidu.com/aistudio/course/introduce/2063) and [Machine Learning](https://aistudio.baidu.com/aistudio/course/introduce/1978) courses.\n",
"\n",
"For the understanding of deep learning itself, you can refer to the zero-based course of Bai Ran, an outstanding architect of Baidu: [Baidu architects take you hands-on with zero-based practice deep learning](https://aistudio.baidu.com/aistudio/course/introduce/1297), which covers the development history of deep learning and introduces the complete components of deep learning through a classic case. It is a set of practice-oriented deep learning courses.\n",
"\n",
"For the practice of theoretical knowledge, [Python basic knowledge](https://aistudio.baidu.com/aistudio/course/introduce/1224) is essential. At the same time, in order to quickly reproduce the deep learning model, the deep learning framework used in this course For: Flying PaddlePaddle. If you have used other frameworks, you can quickly learn how to use flying paddles through [Quick Start Document](https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/quick_start/hello_paddle.html).\n",
"\n",
"### 1.2 Basic Environment Preparation\n",
"\n",
"If you want to run the code of this course in a local environment and have not built a Python environment before, you can follow the [zero-base operating environment preparation](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/environment.md), install Anaconda or docker environment according to your operating system.\n",
"\n",
"If you don't have local resources, you can run the code through the AI Studio training platform. Each item in it is presented in a notebook, which is convenient for developers to learn. If you are not familiar with the related operations of Notebook, you can refer to [AI Studio Project Description](https://ai.baidu.com/ai-doc/AISTUDIO/0k3e2tfzm).\n",
"\n",
"### 1.3 Get and Run the Code\n",
"\n",
"This course relies on the formation of PaddleOCR's code repository. First, clone the complete project of PaddleOCR:\n",
"\n",
"```bash\n",
"# [recommend]\n",
"git clone https://github.com/PaddlePaddle/PaddleOCR\n",
"\n",
"# If you cannot pull successfully due to network problems, you can also choose to use the hosting on Code Cloud:\n",
"git clone https://gitee.com/paddlepaddle/PaddleOCR\n",
"```\n",
"\n",
"> Note: The code cloud hosted code may not be able to synchronize the update of this github project in real time, there is a delay of 3~5 days, please use the recommended method first.\n",
">\n",
"> If you are not familiar with git operations, you can download the compressed package directly from the `Code` on the homepage of PaddleOCR\n",
"\n",
"Then install third-party libraries:\n",
"\n",
"```bash\n",
"cd PaddleOCR\n",
"pip3 install -r requirements.txt\n",
"```\n",
"\n",
"\n",
"\n",
"### 1.4 Access to Information\n",
"\n",
"[PaddleOCR Usage Document](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/README.md) describes in detail how to use PaddleOCR to complete model application, training and deployment. The document is rich in content, most of the user’s questions are described in the document or FAQ, especially in [FAQ](https://github.com/PaddlePaddle/PaddleOCR/blob/release%2F2.3/doc/doc_en/FAQ_en.md), in accordance with the application process of deep learning, has precipitated the user's common questions, it is recommended that you read it carefully.\n",
"\n",
"### 1.5 Ask for Help\n",
"\n",
"If you encounter BUG, ease of use or documentation related issues while using PaddleOCR, you can contact the official via [Github issue](https://github.com/PaddlePaddle/PaddleOCR/issues), please follow the issue template Provide as much information as possible so that official personnel can quickly locate the problem. At the same time, the WeChat group is the daily communication position for the majority of PaddleOCR users, and it is more suitable for asking some consulting questions. In addition to the PaddleOCR team members, there will also be enthusiastic developers answering your questions."
]
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ppocr/losses/rec_ctc_loss.py
View file @ cf613b13
......@@ -31,7 +31,8 @@ class CTCLoss(nn.Layer):
predicts = predicts[-1]
predicts = predicts.transpose((1, 0, 2))
N, B, _ = predicts.shape
preds_lengths = paddle.to_tensor([N] * B, dtype='int64')
preds_lengths = paddle.to_tensor(
[N] * B, dtype='int64', place=paddle.CPUPlace())
labels = batch[1].astype("int32")
label_lengths = batch[2].astype('int64')
loss = self.loss_func(predicts, labels, preds_lengths, label_lengths)
......
ppocr/metrics/cls_metric.py
View file @ cf613b13
......@@ -16,6 +16,7 @@
class ClsMetric(object):
def __init__(self, main_indicator='acc', **kwargs):
self.main_indicator = main_indicator
self.eps = 1e-5
self.reset()
def __call__(self, pred_label, *args, **kwargs):
......@@ -28,7 +29,7 @@ class ClsMetric(object):
all_num += 1
self.correct_num += correct_num
self.all_num += all_num
return {'acc': correct_num / all_num, }
return {'acc': correct_num / (all_num + self.eps), }
def get_metric(self):
"""
......@@ -36,7 +37,7 @@ class ClsMetric(object):
'acc': 0
}
"""
acc = self.correct_num / self.all_num
acc = self.correct_num / (self.all_num + self.eps)
self.reset()
return {'acc': acc}
......
ppocr/metrics/rec_metric.py
View file @ cf613b13
......@@ -20,6 +20,7 @@ class RecMetric(object):
def __init__(self, main_indicator='acc', is_filter=False, **kwargs):
self.main_indicator = main_indicator
self.is_filter = is_filter
self.eps = 1e-5
self.reset()
def _normalize_text(self, text):
......@@ -47,8 +48,8 @@ class RecMetric(object):
self.all_num += all_num
self.norm_edit_dis += norm_edit_dis
return {
'acc': correct_num / all_num,
'norm_edit_dis': 1 - norm_edit_dis / (all_num + 1e-3)
'acc': correct_num / (all_num + self.eps),
'norm_edit_dis': 1 - norm_edit_dis / (all_num + self.eps)
}
def get_metric(self):
......@@ -58,8 +59,8 @@ class RecMetric(object):
'norm_edit_dis': 0,
}
"""
acc = 1.0 * self.correct_num / (self.all_num + 1e-3)
norm_edit_dis = 1 - self.norm_edit_dis / (self.all_num + 1e-3)
acc = 1.0 * self.correct_num / (self.all_num + self.eps)
norm_edit_dis = 1 - self.norm_edit_dis / (self.all_num + self.eps)
self.reset()
return {'acc': acc, 'norm_edit_dis': norm_edit_dis}
......
ppocr/metrics/table_metric.py
View file @ cf613b13
......@@ -12,9 +12,12 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
class TableMetric(object):
def __init__(self, main_indicator='acc', **kwargs):
self.main_indicator = main_indicator
self.eps = 1e-5
self.reset()
def __call__(self, pred, batch, *args, **kwargs):
......@@ -31,9 +34,7 @@ class TableMetric(object):
correct_num += 1
self.correct_num += correct_num
self.all_num += all_num
return {
'acc': correct_num * 1.0 / all_num,
}
return {'acc': correct_num * 1.0 / (all_num + self.eps), }
def get_metric(self):
"""
......@@ -41,7 +42,7 @@ class TableMetric(object):
'acc': 0,
}
"""
acc = 1.0 * self.correct_num / self.all_num
acc = 1.0 * self.correct_num / (self.all_num + self.eps)
self.reset()
return {'acc': acc}
......
ppocr/utils/utility.py
View file @ cf613b13
......@@ -105,3 +105,22 @@ def set_seed(seed=1024):
random.seed(seed)
np.random.seed(seed)
paddle.seed(seed)
class AverageMeter:
def __init__(self):
self.reset()
def reset(self):
"""reset"""
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
"""update"""
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
ppstructure/docs/quickstart.md
View file @ cf613b13
......@@ -133,16 +133,16 @@ cd ppstructure
# 下载模型
mkdir inference && cd inference
# 下载超轻量级中文OCR模型的检测模型并解压
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
# 下载超轻量级中文OCR模型的识别模型并解压
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
# 下载超轻量级英文表格英寸模型并解压
# 下载PP-OCRv2文本检测模型并解压
wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar && tar xf ch_PP-OCRv2_det_slim_quant_infer.tar
# 下载PP-OCRv2文本识别模型并解压
wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar && tar xf ch_PP-OCRv2_rec_slim_quant_infer.tar
# 下载超轻量级英文表格预测模型并解压
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
cd ..
python3 predict_system.py --det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer \
--rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer \
python3 predict_system.py --det_model_dir=inference/ch_PP-OCRv2_det_slim_quant_infer \
--rec_model_dir=inference/ch_PP-OCRv2_rec_slim_quant_infer \
--table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer \
--image_dir=../doc/table/1.png \
--rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt \
......
ppstructure/table/README.md
View file @ cf613b13
......@@ -41,7 +41,7 @@ wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_tab
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
cd ..
# run
python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_table_det_infer --rec_model_dir=inference/en_ppocr_mobile_v2.0_table_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_dict_path=../ppocr/utils/dict/en_dict.txt --det_limit_side_len=736 --det_limit_type=min --output ../output/table
python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_table_det_infer --rec_model_dir=inference/en_ppocr_mobile_v2.0_table_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --det_limit_side_len=736 --det_limit_type=min --output ../output/table
```
Note: The above model is trained on the PubLayNet dataset and only supports English scanning scenarios. If you need to identify other scenarios, you need to train the model yourself and replace the three fields `det_model_dir`, `rec_model_dir`, `table_model_dir`.
......
ppstructure/table/README_ch.md
View file @ cf613b13
......@@ -56,7 +56,7 @@ wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_tab
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
cd ..
# 执行预测
python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_table_det_infer --rec_model_dir=inference/en_ppocr_mobile_v2.0_table_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_dict_path=../ppocr/utils/dict/en_dict.txt --det_limit_side_len=736 --det_limit_type=min --output ../output/table
python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_table_det_infer --rec_model_dir=inference/en_ppocr_mobile_v2.0_table_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --det_limit_side_len=736 --det_limit_type=min --output ../output/table
```
运行完成后，每张图片的excel表格会保存到output字段指定的目录下
......
test_tipc/supplementary/readme.md
View file @ cf613b13
......@@ -3,7 +3,7 @@
Linux端基础训练预测功能测试的主程序为test_train_python.sh，可以测试基于Python的模型训练、评估等基本功能，包括裁剪、量化、蒸馏训练。
![](./tipc_train.png)
![](./test_tipc/tipc_train.png)
测试链条如上图所示，主要测试内容有带共享权重，自定义OP的模型的正常训练和slim相关功能训练流程是否正常。
......@@ -28,23 +28,23 @@ pip3 install -r requirements.txt
- 模式1：lite_train_lite_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度；
```
bash test_tipc/test_train_python.sh ./test_tipc/ch_ppocr_mobile_v2.0_det/train_infer_python.txt 'lite_train_lite_infer'
bash test_tipc/test_train_python.sh ./test_tipc/train_infer_python.txt 'lite_train_lite_infer'
```
- 模式2：whole_train_whole_infer，使用全量数据训练，用于快速验证训练到预测的走通流程，验证模型最终训练精度；
```
bash test_tipc/test_train_python.sh ./test_tipc/ch_ppocr_mobile_v2.0_det/train_infer_python.txt 'whole_train_whole_infer'
bash test_tipc/test_train_python.sh ./test_tipc/train_infer_python.txt 'whole_train_whole_infer'
```
如果是运行量化裁剪等训练方式，需要使用不同的配置文件。量化训练的测试指令如下：
```
bash test_tipc/test_train_python.sh ./test_tipc/ch_ppocr_mobile_v2.0_det/train_infer_python_PACT.txt 'lite_train_lite_infer'
bash test_tipc/test_train_python.sh ./test_tipc/train_infer_python_PACT.txt 'lite_train_lite_infer'
```
同理，FPGM裁剪的运行方式如下：
```
bash test_tipc/test_train_python.sh ./test_tipc/ch_ppocr_mobile_v2.0_det/train_infer_python_FPGM.txt 'lite_train_lite_infer'
bash test_tipc/test_train_python.sh ./test_tipc/train_infer_python_FPGM.txt 'lite_train_lite_infer'
```
运行相应指令后，在`test_tipc/output`文件夹下自动会保存运行日志。如'lite_train_lite_infer'模式运行后，在test_tipc/extra_output文件夹有以下文件：
......
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