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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 文本识别实战\n",
    "\n",
    "上一章理论部分，介绍了文本识别领域的主要方法，其中CRNN是较早被提出也是目前工业界应用较多的方法。本章将详细介绍如何基于PaddleOCR完成CRNN文本识别模型的搭建、训练、评估和预测。数据集采用 icdar 2015，其中训练集有4468张，测试集有2077张。\n",
    "\n",
    "\n",
    "通过本章的学习，你可以掌握：\n",
    "\n",
    "1. 如何使用paddleocr whl 包快速完成文本识别预测\n",
    "\n",
    "2. CRNN的基本原理和网络结构\n",
    "\n",
    "3. 模型训练的必须步骤和调参方式\n",
    "\n",
    "4. 使用自定义的数据集训练网络\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 1. 快速体验\n",
    "\n",
    "### 1.1 安装相关的依赖及whl包\n",
    "\n",
    "首先确认安装了 paddle 以及 paddleocr，如果已经安装过，忽略该步骤。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  Stored in directory: /home/aistudio/.cache/pip/wheels/a1/cb/b3/a25a8ce16c1a4ff102c1e40d6eaa4dfc9d5695b92d57331b36\n",
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      "Installing collected packages: tifffile, PyWavelets, shapely, scikit-image, pybind11, lxml, cssutils, cssselect, python-Levenshtein, pyclipper, premailer, opencv-contrib-python, lmdb, imgaug, fasttext, paddleocr\n",
      "Successfully installed PyWavelets-1.2.0 cssselect-1.1.0 cssutils-2.3.0 fasttext-0.9.1 imgaug-0.4.0 lmdb-1.2.1 lxml-4.7.1 opencv-contrib-python-4.4.0.46 paddleocr-2.3.0.2 premailer-3.10.0 pybind11-2.8.1 pyclipper-1.3.0.post2 python-Levenshtein-0.12.2 scikit-image-0.19.1 shapely-1.8.0 tifffile-2021.11.2\n"
     ]
    }
   ],
   "source": [
    "# 安装 PaddlePaddle GPU 版本\n",
    "!pip install paddlepaddle-gpu\n",
    "# 安装 paddleocr whl包\n",
    "! pip install -U pip\n",
    "! pip install paddleocr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 1.2 快速预测文字内容\n",
    "\n",
    "paddleocr whl包会自动下载ppocr轻量级模型作为默认模型\n",
    "\n",
    "下面展示如何使用whl包进行识别预测：\n",
    "\n",
    "测试图片：\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/531d9b3aff45449893b33bcb5dd13971057fcb4038f045578b3abd99fa3a96f2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2021/12/23 20:28:44] root WARNING: version 2.1 not support cls models, use version 2.0 instead\n",
      "download https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar to /home/aistudio/.paddleocr/2.2.1/ocr/det/ch/ch_PP-OCRv2_det_infer/ch_PP-OCRv2_det_infer.tar\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/skimage/morphology/_skeletonize.py:241: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  0, 1, 1, 0, 0, 1, 0, 0, 0], dtype=np.bool)\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/skimage/morphology/_skeletonize.py:256: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.bool)\n",
      "  0%|          | 0.00/3.19M [00:00<?, ?iB/s]100%|██████████| 3.19M/3.19M [00:00<00:00, 7.80MiB/s]\n",
      " 14%|█▎        | 1.20M/8.88M [00:00<00:00, 11.7MiB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "download https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar to /home/aistudio/.paddleocr/2.2.1/ocr/rec/ch/ch_PP-OCRv2_rec_infer/ch_PP-OCRv2_rec_infer.tar\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24%|██▍       | 2.15M/8.88M [00:00<00:00, 10.8MiB/s]100%|██████████| 8.88M/8.88M [00:01<00:00, 6.38MiB/s]\n",
      " 17%|█▋        | 249k/1.45M [00:00<00:00, 2.42MiB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "download https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar to /home/aistudio/.paddleocr/2.2.1/ocr/cls/ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v2.0_cls_infer.tar\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|█████████ | 1.31M/1.45M [00:00<00:00, 3.32MiB/s]100%|██████████| 1.45M/1.45M [00:00<00:00, 4.53MiB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Namespace(benchmark=False, cls_batch_num=6, cls_image_shape='3, 48, 192', cls_model_dir='/home/aistudio/.paddleocr/2.2.1/ocr/cls/ch_ppocr_mobile_v2.0_cls_infer', cls_thresh=0.9, cpu_threads=10, det=True, det_algorithm='DB', det_db_box_thresh=0.6, det_db_score_mode='fast', det_db_thresh=0.3, det_db_unclip_ratio=1.5, det_east_cover_thresh=0.1, det_east_nms_thresh=0.2, det_east_score_thresh=0.8, det_limit_side_len=960, det_limit_type='max', det_model_dir='/home/aistudio/.paddleocr/2.2.1/ocr/det/ch/ch_PP-OCRv2_det_infer', det_sast_nms_thresh=0.2, det_sast_polygon=False, det_sast_score_thresh=0.5, drop_score=0.5, e2e_algorithm='PGNet', e2e_char_dict_path='./ppocr/utils/ic15_dict.txt', e2e_limit_side_len=768, e2e_limit_type='max', e2e_model_dir=None, e2e_pgnet_mode='fast', e2e_pgnet_polygon=True, e2e_pgnet_score_thresh=0.5, e2e_pgnet_valid_set='totaltext', enable_mkldnn=False, gpu_mem=500, help='==SUPPRESS==', image_dir=None, ir_optim=True, label_list=['0', '180'], lang='ch', layout_path_model='lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config', max_batch_size=10, max_text_length=25, min_subgraph_size=15, output='./output/table', precision='fp32', process_id=0, rec=True, rec_algorithm='CRNN', rec_batch_num=6, rec_char_dict_path='/home/aistudio/PaddleOCR/ppocr/utils/ppocr_keys_v1.txt', rec_char_type='ch', rec_image_shape='3, 32, 320', rec_model_dir='/home/aistudio/.paddleocr/2.2.1/ocr/rec/ch/ch_PP-OCRv2_rec_infer', save_log_path='./log_output/', show_log=True, table_char_dict_path=None, table_char_type='en', table_max_len=488, table_model_dir=None, total_process_num=1, type='ocr', use_angle_cls=False, use_dilation=False, use_gpu=True, use_mp=False, use_pdserving=False, use_space_char=True, use_tensorrt=False, version='2.1', vis_font_path='./doc/fonts/simfang.ttf', warmup=True)\n",
      "[2021/12/23 20:28:48] root WARNING: Since the angle classifier is not initialized, the angle classifier will not be uesd during the forward process\n",
      "('SLOW', 0.9776376)\n"
     ]
    }
   ],
   "source": [
    "from paddleocr import PaddleOCR\n",
    "\n",
    "ocr = PaddleOCR()  # need to run only once to download and load model into memory\n",
    "img_path = '/home/aistudio/work/word_19.png'\n",
    "result = ocr.ocr(img_path, det=False)\n",
    "for line in result:\n",
    "    print(line)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "执行完上述代码块，将返回识别结果和识别置信度\n",
    "\n",
    "```\n",
    "('SLOW', 0.9776376)\n",
    "```\n",
    "\n",
    "至此，你掌握了如何使用 paddleocr whl 包进行预测。`./work/` 路径下有更多测试图片，可以尝试其他图片结果。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 2. 预测原理详解\n",
    "\n",
    "第一节中 paddleocr 加载训练好的 CRNN 识别模型进行预测，本节将详细介绍 CRNN 的原理及流程。\n",
    "\n",
    "### 2.1 所属类别\n",
    "\n",
    "CRNN 是基于CTC的算法，在理论部分介绍的分类图中，处在如下位置。可以看出CRNN主要用于解决规则文本，基于CTC的算法有较快的预测速度并且很好的适用长文本。因此CRNN是PPOCR选择的中文识别算法。\n",
    "<center><img src=https://ai-studio-static-online.cdn.bcebos.com/0e74d46918e5423684e06df8e6eb992cf9f7c485d6c142fb98cc263714559898 width=\"600\"></center>\n",
    "\n",
    "\n",
    "### 2.2 算法详解\n",
    "\n",
    "CRNN 的网络结构体系如下所示，从下往上分别为卷积层、递归层和转录层三部分：\n",
    "\n",
    "<center><img src=https://ai-studio-static-online.cdn.bcebos.com/f6fae3ff66bd413fa182d75782034a2af6aab1994fa148a08e6565f3fb75b18d width=\"600\"></center>\n",
    "\n",
    "1)backbone：\n",
    "\n",
    "卷积网络作为底层的骨干网络，用于从输入图像中提取特征序列。由于 `conv`、`max-pooling`、`elementwise` 和激活函数都作用在局部区域上，所以它们是平移不变的。因此，特征映射的每一列对应于原始图像的一个矩形区域(称为感受野)，并且这些矩形区域与它们在特征映射上对应的列从左到右的顺序相同。由于CNN需要将输入的图像缩放到固定的尺寸以满足其固定的输入维数，因此它不适合长度变化很大的序列对象。为了更好的支持变长序列，CRNN将backbone最后一层输出的特征向量送到了RNN层，转换为序列特征。\n",
    "\n",
    "<center><img src=https://ai-studio-static-online.cdn.bcebos.com/6694818123724b0d92d05b63dc9dfb08c7ced6c47c3b4f4d9b110ae9ccfe941d width=\"600\"></center>\n",
    "\n",
    "2)neck: \n",
    "\n",
    "递归层，在卷积网络的基础上，构建递归网络，将图像特征转换为序列特征，预测每个帧的标签分布。\n",
    "RNN具有很强的捕获序列上下文信息的能力。使用上下文线索进行基于图像的序列识别比单独处理每个像素更有效。以场景文本识别为例，宽字符可能需要几个连续的帧来充分描述。此外，有些歧义字符在观察其上下文时更容易区分。其次，RNN可以将误差差分反向传播回卷积层，使网络可以统一训练。第三，RNN能够对任意长度的序列进行操作，解决了文本图片变长的问题。CRNN使用双层LSTM作为递归层，解决了长序列训练过程中的梯度消失和梯度爆炸问题。\n",
    "\n",
    "<center><img src=https://ai-studio-static-online.cdn.bcebos.com/41cdb7fb08fb4b55923b0baf66b783e46fd063223d05416fa952369ad20ac83c width=\"600\"></center>\n",
    "\n",
    "\n",
    "3)head: \n",
    "\n",
    "转录层，通过全连接网络和softmax激活函数，将每帧的预测转换为最终的标签序列。最后使用 CTC Loss 在无需序列对齐的情况下，完成CNN和RNN的联合训练。CTC 有一套特别的合并序列机制，LSTM输出序列后，需要在时序上分类得到预测结果。可能存在多个时间步对应同一个类别，因此需要对相同结果进行合并。为避免合并本身存在的重复字符，CTC 引入了一个 `blank` 字符插入在重复字符之间。\n",
    "\n",
    "<center><img src=https://ai-studio-static-online.cdn.bcebos.com/bea6be2f5d9d4ac791118737c3f2f140a2a261e0d8a540a3b0ef239b6bcb2c43 width=\"600\"></center>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 2.3 代码实现\n",
    "\n",
    "整个网络结构非常简洁，代码实现也相对简单，可以跟随预测流程依次搭建模块。本节需要完成：数据输入、backbone搭建、neck搭建、head搭建。\n",
    "\n",
    "**【数据输入】**\n",
    "\n",
    "数据送入网络前需要缩放到统一尺寸(3,32,320)，并完成归一化处理。这里省略掉训练时需要的数据增强部分，以单张图为例展示预处理的必须步骤（[源码位置](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/ppocr/data/imaug/rec_img_aug.py#L126)）：\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import math\n",
    "import numpy as np\n",
    "\n",
    "def resize_norm_img(img):\n",
    "    \"\"\"\n",
    "    数据缩放和归一化\n",
    "    :param img: 输入图片\n",
    "    \"\"\"\n",
    "\n",
    "    # 默认输入尺寸\n",
    "    imgC = 3\n",
    "    imgH = 32\n",
    "    imgW = 320\n",
    "\n",
    "    # 图片的真实高宽\n",
    "    h, w = img.shape[:2]\n",
    "    # 图片真实长宽比\n",
    "    ratio = w / float(h)\n",
    "\n",
    "    # 按比例缩放\n",
    "    if math.ceil(imgH * ratio) > imgW:\n",
    "        # 如大于默认宽度，则宽度为imgW\n",
    "        resized_w = imgW\n",
    "    else:\n",
    "        # 如小于默认宽度则以图片真实宽为准\n",
    "        resized_w = int(math.ceil(imgH * ratio))\n",
    "    # 缩放\n",
    "    resized_image = cv2.resize(img, (resized_w, imgH))\n",
    "    resized_image = resized_image.astype('float32')\n",
    "    # 归一化\n",
    "    resized_image = resized_image.transpose((2, 0, 1)) / 255\n",
    "    resized_image -= 0.5\n",
    "    resized_image /= 0.5\n",
    "    # 对宽度不足的位置，补0\n",
    "    padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)\n",
    "    padding_im[:, :, 0:resized_w] = resized_image\n",
    "    # 转置 padding 后的图片用于可视化\n",
    "    draw_img = padding_im.transpose((1,2,0))\n",
    "    return padding_im, draw_img\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# 读图\n",
    "raw_img = cv2.imread(\"/home/aistudio/work/word_1.png\")\n",
    "plt.figure()\n",
    "plt.subplot(2,1,1)\n",
    "# 可视化原图\n",
    "plt.imshow(raw_img)\n",
    "# 缩放并归一化\n",
    "padding_im, draw_img = resize_norm_img(raw_img)\n",
    "plt.subplot(2,1,2)\n",
    "# 可视化网络输入图\n",
    "plt.imshow(draw_img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**【网络结构】**\n",
    "\n",
    "* backbone\n",
    "\n",
    "PaddleOCR 使用 MobileNetV3 作为骨干网络，组网顺序与网络结构一致，首先定义网络中的公共模块([源码位置](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/ppocr/modeling/backbones/rec_mobilenet_v3.py))：ConvBNLayer、ResidualUnit、make_divisible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import paddle\n",
    "import paddle.nn as nn\n",
    "import paddle.nn.functional as F\n",
    "\n",
    "class ConvBNLayer(nn.Layer):\n",
    "    def __init__(self,\n",
    "                 in_channels,\n",
    "                 out_channels,\n",
    "                 kernel_size,\n",
    "                 stride,\n",
    "                 padding,\n",
    "                 groups=1,\n",
    "                 if_act=True,\n",
    "                 act=None):\n",
    "        \"\"\"\n",
    "        卷积BN层\n",
    "        :param in_channels: 输入通道数\n",
    "        :param out_channels: 输出通道数\n",
    "        :param kernel_size: 卷积核尺寸\n",
    "        :parma stride: 步长大小\n",
    "        :param padding: 填充大小\n",
    "        :param groups: 二维卷积层的组数\n",
    "        :param if_act: 是否添加激活函数\n",
    "        :param act: 激活函数\n",
    "        \"\"\"\n",
    "        super(ConvBNLayer, self).__init__()\n",
    "        self.if_act = if_act\n",
    "        self.act = act\n",
    "        self.conv = nn.Conv2D(\n",
    "            in_channels=in_channels,\n",
    "            out_channels=out_channels,\n",
    "            kernel_size=kernel_size,\n",
    "            stride=stride,\n",
    "            padding=padding,\n",
    "            groups=groups,\n",
    "            bias_attr=False)\n",
    "\n",
    "        self.bn = nn.BatchNorm(num_channels=out_channels, act=None)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # conv层\n",
    "        x = self.conv(x)\n",
    "        # batchnorm层\n",
    "        x = self.bn(x)\n",
    "        # 是否使用激活函数\n",
    "        if self.if_act:\n",
    "            if self.act == \"relu\":\n",
    "                x = F.relu(x)\n",
    "            elif self.act == \"hardswish\":\n",
    "                x = F.hardswish(x)\n",
    "            else:\n",
    "                print(\"The activation function({}) is selected incorrectly.\".\n",
    "                      format(self.act))\n",
    "                exit()\n",
    "        return x\n",
    "\n",
    "class SEModule(nn.Layer):\n",
    "    def __init__(self, in_channels, reduction=4):\n",
    "        \"\"\"\n",
    "        SE模块\n",
    "        :param in_channels: 输入通道数\n",
    "        :param reduction: 通道缩放率\n",
    "        \"\"\"        \n",
    "        super(SEModule, self).__init__()\n",
    "        self.avg_pool = nn.AdaptiveAvgPool2D(1)\n",
    "        self.conv1 = nn.Conv2D(\n",
    "            in_channels=in_channels,\n",
    "            out_channels=in_channels // reduction,\n",
    "            kernel_size=1,\n",
    "            stride=1,\n",
    "            padding=0)\n",
    "        self.conv2 = nn.Conv2D(\n",
    "            in_channels=in_channels // reduction,\n",
    "            out_channels=in_channels,\n",
    "            kernel_size=1,\n",
    "            stride=1,\n",
    "            padding=0)\n",
    "\n",
    "    def forward(self, inputs):\n",
    "        # 平均池化\n",
    "        outputs = self.avg_pool(inputs)\n",
    "        # 第一个卷积层\n",
    "        outputs = self.conv1(outputs)\n",
    "        # relu激活函数\n",
    "        outputs = F.relu(outputs)\n",
    "        # 第二个卷积层\n",
    "        outputs = self.conv2(outputs)\n",
    "        # hardsigmoid 激活函数\n",
    "        outputs = F.hardsigmoid(outputs, slope=0.2, offset=0.5)\n",
    "        return inputs * outputs\n",
    "\n",
    "\n",
    "class ResidualUnit(nn.Layer):\n",
    "    def __init__(self,\n",
    "                 in_channels,\n",
    "                 mid_channels,\n",
    "                 out_channels,\n",
    "                 kernel_size,\n",
    "                 stride,\n",
    "                 use_se,\n",
    "                 act=None):\n",
    "        \"\"\"\n",
    "        残差层\n",
    "        :param in_channels: 输入通道数\n",
    "        :param mid_channels: 中间通道数\n",
    "        :param out_channels: 输出通道数\n",
    "        :param kernel_size: 卷积核尺寸\n",
    "        :parma stride: 步长大小\n",
    "        :param use_se: 是否使用se模块\n",
    "        :param act: 激活函数\n",
    "        \"\"\" \n",
    "        super(ResidualUnit, self).__init__()\n",
    "        self.if_shortcut = stride == 1 and in_channels == out_channels\n",
    "        self.if_se = use_se\n",
    "\n",
    "        self.expand_conv = ConvBNLayer(\n",
    "            in_channels=in_channels,\n",
    "            out_channels=mid_channels,\n",
    "            kernel_size=1,\n",
    "            stride=1,\n",
    "            padding=0,\n",
    "            if_act=True,\n",
    "            act=act)\n",
    "        self.bottleneck_conv = ConvBNLayer(\n",
    "            in_channels=mid_channels,\n",
    "            out_channels=mid_channels,\n",
    "            kernel_size=kernel_size,\n",
    "            stride=stride,\n",
    "            padding=int((kernel_size - 1) // 2),\n",
    "            groups=mid_channels,\n",
    "            if_act=True,\n",
    "            act=act)\n",
    "        if self.if_se:\n",
    "            self.mid_se = SEModule(mid_channels)\n",
    "        self.linear_conv = ConvBNLayer(\n",
    "            in_channels=mid_channels,\n",
    "            out_channels=out_channels,\n",
    "            kernel_size=1,\n",
    "            stride=1,\n",
    "            padding=0,\n",
    "            if_act=False,\n",
    "            act=None)\n",
    "\n",
    "    def forward(self, inputs):\n",
    "        x = self.expand_conv(inputs)\n",
    "        x = self.bottleneck_conv(x)\n",
    "        if self.if_se:\n",
    "            x = self.mid_se(x)\n",
    "        x = self.linear_conv(x)\n",
    "        if self.if_shortcut:\n",
    "            x = paddle.add(inputs, x)\n",
    "        return x\n",
    "\n",
    "\n",
    "def make_divisible(v, divisor=8, min_value=None):\n",
    "    \"\"\"\n",
    "    确保被8整除\n",
    "    \"\"\" \n",
    "    if min_value is None:\n",
    "        min_value = divisor\n",
    "    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)\n",
    "    if new_v < 0.9 * v:\n",
    "        new_v += divisor\n",
    "    return new_v\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "利用公共模块搭建骨干网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class MobileNetV3(nn.Layer):\n",
    "    def __init__(self,\n",
    "                 in_channels=3,\n",
    "                 model_name='small',\n",
    "                 scale=0.5,\n",
    "                 small_stride=None,\n",
    "                 disable_se=False,\n",
    "                 **kwargs):\n",
    "        super(MobileNetV3, self).__init__()\n",
    "        self.disable_se = disable_se\n",
    "        \n",
    "        small_stride = [1, 2, 2, 2]\n",
    "\n",
    "        if model_name == \"small\":\n",
    "            cfg = [\n",
    "                # k, exp, c,  se,     nl,  s,\n",
    "                [3, 16, 16, True, 'relu', (small_stride[0], 1)],\n",
    "                [3, 72, 24, False, 'relu', (small_stride[1], 1)],\n",
    "                [3, 88, 24, False, 'relu', 1],\n",
    "                [5, 96, 40, True, 'hardswish', (small_stride[2], 1)],\n",
    "                [5, 240, 40, True, 'hardswish', 1],\n",
    "                [5, 240, 40, True, 'hardswish', 1],\n",
    "                [5, 120, 48, True, 'hardswish', 1],\n",
    "                [5, 144, 48, True, 'hardswish', 1],\n",
    "                [5, 288, 96, True, 'hardswish', (small_stride[3], 1)],\n",
    "                [5, 576, 96, True, 'hardswish', 1],\n",
    "                [5, 576, 96, True, 'hardswish', 1],\n",
    "            ]\n",
    "            cls_ch_squeeze = 576\n",
    "        else:\n",
    "            raise NotImplementedError(\"mode[\" + model_name +\n",
    "                                      \"_model] is not implemented!\")\n",
    "\n",
    "        supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25]\n",
    "        assert scale in supported_scale, \\\n",
    "            \"supported scales are {} but input scale is {}\".format(supported_scale, scale)\n",
    "\n",
    "        inplanes = 16\n",
    "        # conv1\n",
    "        self.conv1 = ConvBNLayer(\n",
    "            in_channels=in_channels,\n",
    "            out_channels=make_divisible(inplanes * scale),\n",
    "            kernel_size=3,\n",
    "            stride=2,\n",
    "            padding=1,\n",
    "            groups=1,\n",
    "            if_act=True,\n",
    "            act='hardswish')\n",
    "        i = 0\n",
    "        block_list = []\n",
    "        inplanes = make_divisible(inplanes * scale)\n",
    "        for (k, exp, c, se, nl, s) in cfg:\n",
    "            se = se and not self.disable_se\n",
    "            block_list.append(\n",
    "                ResidualUnit(\n",
    "                    in_channels=inplanes,\n",
    "                    mid_channels=make_divisible(scale * exp),\n",
    "                    out_channels=make_divisible(scale * c),\n",
    "                    kernel_size=k,\n",
    "                    stride=s,\n",
    "                    use_se=se,\n",
    "                    act=nl))\n",
    "            inplanes = make_divisible(scale * c)\n",
    "            i += 1\n",
    "        self.blocks = nn.Sequential(*block_list)\n",
    "\n",
    "        self.conv2 = ConvBNLayer(\n",
    "            in_channels=inplanes,\n",
    "            out_channels=make_divisible(scale * cls_ch_squeeze),\n",
    "            kernel_size=1,\n",
    "            stride=1,\n",
    "            padding=0,\n",
    "            groups=1,\n",
    "            if_act=True,\n",
    "            act='hardswish')\n",
    "\n",
    "        self.pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)\n",
    "        self.out_channels = make_divisible(scale * cls_ch_squeeze)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.blocks(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.pool(x)\n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "至此就完成了骨干网络的定义，可通过 paddle.summary 结构可视化整个网络结构："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------\n",
      "   Layer (type)         Input Shape          Output Shape         Param #    \n",
      "===============================================================================\n",
      "     Conv2D-1        [[1, 3, 32, 320]]     [1, 8, 16, 160]          216      \n",
      "    BatchNorm-1      [[1, 8, 16, 160]]     [1, 8, 16, 160]          32       \n",
      "   ConvBNLayer-1     [[1, 3, 32, 320]]     [1, 8, 16, 160]           0       \n",
      "     Conv2D-2        [[1, 8, 16, 160]]     [1, 8, 16, 160]          64       \n",
      "    BatchNorm-2      [[1, 8, 16, 160]]     [1, 8, 16, 160]          32       \n",
      "   ConvBNLayer-2     [[1, 8, 16, 160]]     [1, 8, 16, 160]           0       \n",
      "     Conv2D-3        [[1, 8, 16, 160]]     [1, 8, 16, 160]          72       \n",
      "    BatchNorm-3      [[1, 8, 16, 160]]     [1, 8, 16, 160]          32       \n",
      "   ConvBNLayer-3     [[1, 8, 16, 160]]     [1, 8, 16, 160]           0       \n",
      "AdaptiveAvgPool2D-1  [[1, 8, 16, 160]]       [1, 8, 1, 1]            0       \n",
      "     Conv2D-4          [[1, 8, 1, 1]]        [1, 2, 1, 1]           18       \n",
      "     Conv2D-5          [[1, 2, 1, 1]]        [1, 8, 1, 1]           24       \n",
      "    SEModule-1       [[1, 8, 16, 160]]     [1, 8, 16, 160]           0       \n",
      "     Conv2D-6        [[1, 8, 16, 160]]     [1, 8, 16, 160]          64       \n",
      "    BatchNorm-4      [[1, 8, 16, 160]]     [1, 8, 16, 160]          32       \n",
      "   ConvBNLayer-4     [[1, 8, 16, 160]]     [1, 8, 16, 160]           0       \n",
      "  ResidualUnit-1     [[1, 8, 16, 160]]     [1, 8, 16, 160]           0       \n",
      "     Conv2D-7        [[1, 8, 16, 160]]     [1, 40, 16, 160]         320      \n",
      "    BatchNorm-5      [[1, 40, 16, 160]]    [1, 40, 16, 160]         160      \n",
      "   ConvBNLayer-5     [[1, 8, 16, 160]]     [1, 40, 16, 160]          0       \n",
      "     Conv2D-8        [[1, 40, 16, 160]]    [1, 40, 8, 160]          360      \n",
      "    BatchNorm-6      [[1, 40, 8, 160]]     [1, 40, 8, 160]          160      \n",
      "   ConvBNLayer-6     [[1, 40, 16, 160]]    [1, 40, 8, 160]           0       \n",
      "     Conv2D-9        [[1, 40, 8, 160]]     [1, 16, 8, 160]          640      \n",
      "    BatchNorm-7      [[1, 16, 8, 160]]     [1, 16, 8, 160]          64       \n",
      "   ConvBNLayer-7     [[1, 40, 8, 160]]     [1, 16, 8, 160]           0       \n",
      "  ResidualUnit-2     [[1, 8, 16, 160]]     [1, 16, 8, 160]           0       \n",
      "     Conv2D-10       [[1, 16, 8, 160]]     [1, 48, 8, 160]          768      \n",
      "    BatchNorm-8      [[1, 48, 8, 160]]     [1, 48, 8, 160]          192      \n",
      "   ConvBNLayer-8     [[1, 16, 8, 160]]     [1, 48, 8, 160]           0       \n",
      "     Conv2D-11       [[1, 48, 8, 160]]     [1, 48, 8, 160]          432      \n",
      "    BatchNorm-9      [[1, 48, 8, 160]]     [1, 48, 8, 160]          192      \n",
      "   ConvBNLayer-9     [[1, 48, 8, 160]]     [1, 48, 8, 160]           0       \n",
      "     Conv2D-12       [[1, 48, 8, 160]]     [1, 16, 8, 160]          768      \n",
      "   BatchNorm-10      [[1, 16, 8, 160]]     [1, 16, 8, 160]          64       \n",
      "  ConvBNLayer-10     [[1, 48, 8, 160]]     [1, 16, 8, 160]           0       \n",
      "  ResidualUnit-3     [[1, 16, 8, 160]]     [1, 16, 8, 160]           0       \n",
      "     Conv2D-13       [[1, 16, 8, 160]]     [1, 48, 8, 160]          768      \n",
      "   BatchNorm-11      [[1, 48, 8, 160]]     [1, 48, 8, 160]          192      \n",
      "  ConvBNLayer-11     [[1, 16, 8, 160]]     [1, 48, 8, 160]           0       \n",
      "     Conv2D-14       [[1, 48, 8, 160]]     [1, 48, 4, 160]         1,200     \n",
      "   BatchNorm-12      [[1, 48, 4, 160]]     [1, 48, 4, 160]          192      \n",
      "  ConvBNLayer-12     [[1, 48, 8, 160]]     [1, 48, 4, 160]           0       \n",
      "AdaptiveAvgPool2D-2  [[1, 48, 4, 160]]      [1, 48, 1, 1]            0       \n",
      "     Conv2D-15        [[1, 48, 1, 1]]       [1, 12, 1, 1]           588      \n",
      "     Conv2D-16        [[1, 12, 1, 1]]       [1, 48, 1, 1]           624      \n",
      "    SEModule-2       [[1, 48, 4, 160]]     [1, 48, 4, 160]           0       \n",
      "     Conv2D-17       [[1, 48, 4, 160]]     [1, 24, 4, 160]         1,152     \n",
      "   BatchNorm-13      [[1, 24, 4, 160]]     [1, 24, 4, 160]          96       \n",
      "  ConvBNLayer-13     [[1, 48, 4, 160]]     [1, 24, 4, 160]           0       \n",
      "  ResidualUnit-4     [[1, 16, 8, 160]]     [1, 24, 4, 160]           0       \n",
      "     Conv2D-18       [[1, 24, 4, 160]]     [1, 120, 4, 160]        2,880     \n",
      "   BatchNorm-14      [[1, 120, 4, 160]]    [1, 120, 4, 160]         480      \n",
      "  ConvBNLayer-14     [[1, 24, 4, 160]]     [1, 120, 4, 160]          0       \n",
      "     Conv2D-19       [[1, 120, 4, 160]]    [1, 120, 4, 160]        3,000     \n",
      "   BatchNorm-15      [[1, 120, 4, 160]]    [1, 120, 4, 160]         480      \n",
      "  ConvBNLayer-15     [[1, 120, 4, 160]]    [1, 120, 4, 160]          0       \n",
      "AdaptiveAvgPool2D-3  [[1, 120, 4, 160]]     [1, 120, 1, 1]           0       \n",
      "     Conv2D-20        [[1, 120, 1, 1]]      [1, 30, 1, 1]          3,630     \n",
      "     Conv2D-21        [[1, 30, 1, 1]]       [1, 120, 1, 1]         3,720     \n",
      "    SEModule-3       [[1, 120, 4, 160]]    [1, 120, 4, 160]          0       \n",
      "     Conv2D-22       [[1, 120, 4, 160]]    [1, 24, 4, 160]         2,880     \n",
      "   BatchNorm-16      [[1, 24, 4, 160]]     [1, 24, 4, 160]          96       \n",
      "  ConvBNLayer-16     [[1, 120, 4, 160]]    [1, 24, 4, 160]           0       \n",
      "  ResidualUnit-5     [[1, 24, 4, 160]]     [1, 24, 4, 160]           0       \n",
      "     Conv2D-23       [[1, 24, 4, 160]]     [1, 120, 4, 160]        2,880     \n",
      "   BatchNorm-17      [[1, 120, 4, 160]]    [1, 120, 4, 160]         480      \n",
      "  ConvBNLayer-17     [[1, 24, 4, 160]]     [1, 120, 4, 160]          0       \n",
      "     Conv2D-24       [[1, 120, 4, 160]]    [1, 120, 4, 160]        3,000     \n",
      "   BatchNorm-18      [[1, 120, 4, 160]]    [1, 120, 4, 160]         480      \n",
      "  ConvBNLayer-18     [[1, 120, 4, 160]]    [1, 120, 4, 160]          0       \n",
      "AdaptiveAvgPool2D-4  [[1, 120, 4, 160]]     [1, 120, 1, 1]           0       \n",
      "     Conv2D-25        [[1, 120, 1, 1]]      [1, 30, 1, 1]          3,630     \n",
      "     Conv2D-26        [[1, 30, 1, 1]]       [1, 120, 1, 1]         3,720     \n",
      "    SEModule-4       [[1, 120, 4, 160]]    [1, 120, 4, 160]          0       \n",
      "     Conv2D-27       [[1, 120, 4, 160]]    [1, 24, 4, 160]         2,880     \n",
      "   BatchNorm-19      [[1, 24, 4, 160]]     [1, 24, 4, 160]          96       \n",
      "  ConvBNLayer-19     [[1, 120, 4, 160]]    [1, 24, 4, 160]           0       \n",
      "  ResidualUnit-6     [[1, 24, 4, 160]]     [1, 24, 4, 160]           0       \n",
      "     Conv2D-28       [[1, 24, 4, 160]]     [1, 64, 4, 160]         1,536     \n",
      "   BatchNorm-20      [[1, 64, 4, 160]]     [1, 64, 4, 160]          256      \n",
      "  ConvBNLayer-20     [[1, 24, 4, 160]]     [1, 64, 4, 160]           0       \n",
      "     Conv2D-29       [[1, 64, 4, 160]]     [1, 64, 4, 160]         1,600     \n",
      "   BatchNorm-21      [[1, 64, 4, 160]]     [1, 64, 4, 160]          256      \n",
      "  ConvBNLayer-21     [[1, 64, 4, 160]]     [1, 64, 4, 160]           0       \n",
      "AdaptiveAvgPool2D-5  [[1, 64, 4, 160]]      [1, 64, 1, 1]            0       \n",
      "     Conv2D-30        [[1, 64, 1, 1]]       [1, 16, 1, 1]          1,040     \n",
      "     Conv2D-31        [[1, 16, 1, 1]]       [1, 64, 1, 1]          1,088     \n",
      "    SEModule-5       [[1, 64, 4, 160]]     [1, 64, 4, 160]           0       \n",
      "     Conv2D-32       [[1, 64, 4, 160]]     [1, 24, 4, 160]         1,536     \n",
      "   BatchNorm-22      [[1, 24, 4, 160]]     [1, 24, 4, 160]          96       \n",
      "  ConvBNLayer-22     [[1, 64, 4, 160]]     [1, 24, 4, 160]           0       \n",
      "  ResidualUnit-7     [[1, 24, 4, 160]]     [1, 24, 4, 160]           0       \n",
      "     Conv2D-33       [[1, 24, 4, 160]]     [1, 72, 4, 160]         1,728     \n",
      "   BatchNorm-23      [[1, 72, 4, 160]]     [1, 72, 4, 160]          288      \n",
      "  ConvBNLayer-23     [[1, 24, 4, 160]]     [1, 72, 4, 160]           0       \n",
      "     Conv2D-34       [[1, 72, 4, 160]]     [1, 72, 4, 160]         1,800     \n",
      "   BatchNorm-24      [[1, 72, 4, 160]]     [1, 72, 4, 160]          288      \n",
      "  ConvBNLayer-24     [[1, 72, 4, 160]]     [1, 72, 4, 160]           0       \n",
      "AdaptiveAvgPool2D-6  [[1, 72, 4, 160]]      [1, 72, 1, 1]            0       \n",
      "     Conv2D-35        [[1, 72, 1, 1]]       [1, 18, 1, 1]          1,314     \n",
      "     Conv2D-36        [[1, 18, 1, 1]]       [1, 72, 1, 1]          1,368     \n",
      "    SEModule-6       [[1, 72, 4, 160]]     [1, 72, 4, 160]           0       \n",
      "     Conv2D-37       [[1, 72, 4, 160]]     [1, 24, 4, 160]         1,728     \n",
      "   BatchNorm-25      [[1, 24, 4, 160]]     [1, 24, 4, 160]          96       \n",
      "  ConvBNLayer-25     [[1, 72, 4, 160]]     [1, 24, 4, 160]           0       \n",
      "  ResidualUnit-8     [[1, 24, 4, 160]]     [1, 24, 4, 160]           0       \n",
      "     Conv2D-38       [[1, 24, 4, 160]]     [1, 144, 4, 160]        3,456     \n",
      "   BatchNorm-26      [[1, 144, 4, 160]]    [1, 144, 4, 160]         576      \n",
      "  ConvBNLayer-26     [[1, 24, 4, 160]]     [1, 144, 4, 160]          0       \n",
      "     Conv2D-39       [[1, 144, 4, 160]]    [1, 144, 2, 160]        3,600     \n",
      "   BatchNorm-27      [[1, 144, 2, 160]]    [1, 144, 2, 160]         576      \n",
      "  ConvBNLayer-27     [[1, 144, 4, 160]]    [1, 144, 2, 160]          0       \n",
      "AdaptiveAvgPool2D-7  [[1, 144, 2, 160]]     [1, 144, 1, 1]           0       \n",
      "     Conv2D-40        [[1, 144, 1, 1]]      [1, 36, 1, 1]          5,220     \n",
      "     Conv2D-41        [[1, 36, 1, 1]]       [1, 144, 1, 1]         5,328     \n",
      "    SEModule-7       [[1, 144, 2, 160]]    [1, 144, 2, 160]          0       \n",
      "     Conv2D-42       [[1, 144, 2, 160]]    [1, 48, 2, 160]         6,912     \n",
      "   BatchNorm-28      [[1, 48, 2, 160]]     [1, 48, 2, 160]          192      \n",
      "  ConvBNLayer-28     [[1, 144, 2, 160]]    [1, 48, 2, 160]           0       \n",
      "  ResidualUnit-9     [[1, 24, 4, 160]]     [1, 48, 2, 160]           0       \n",
      "     Conv2D-43       [[1, 48, 2, 160]]     [1, 288, 2, 160]       13,824     \n",
      "   BatchNorm-29      [[1, 288, 2, 160]]    [1, 288, 2, 160]        1,152     \n",
      "  ConvBNLayer-29     [[1, 48, 2, 160]]     [1, 288, 2, 160]          0       \n",
      "     Conv2D-44       [[1, 288, 2, 160]]    [1, 288, 2, 160]        7,200     \n",
      "   BatchNorm-30      [[1, 288, 2, 160]]    [1, 288, 2, 160]        1,152     \n",
      "  ConvBNLayer-30     [[1, 288, 2, 160]]    [1, 288, 2, 160]          0       \n",
      "AdaptiveAvgPool2D-8  [[1, 288, 2, 160]]     [1, 288, 1, 1]           0       \n",
      "     Conv2D-45        [[1, 288, 1, 1]]      [1, 72, 1, 1]         20,808     \n",
      "     Conv2D-46        [[1, 72, 1, 1]]       [1, 288, 1, 1]        21,024     \n",
      "    SEModule-8       [[1, 288, 2, 160]]    [1, 288, 2, 160]          0       \n",
      "     Conv2D-47       [[1, 288, 2, 160]]    [1, 48, 2, 160]        13,824     \n",
      "   BatchNorm-31      [[1, 48, 2, 160]]     [1, 48, 2, 160]          192      \n",
      "  ConvBNLayer-31     [[1, 288, 2, 160]]    [1, 48, 2, 160]           0       \n",
      "  ResidualUnit-10    [[1, 48, 2, 160]]     [1, 48, 2, 160]           0       \n",
      "     Conv2D-48       [[1, 48, 2, 160]]     [1, 288, 2, 160]       13,824     \n",
      "   BatchNorm-32      [[1, 288, 2, 160]]    [1, 288, 2, 160]        1,152     \n",
      "  ConvBNLayer-32     [[1, 48, 2, 160]]     [1, 288, 2, 160]          0       \n",
      "     Conv2D-49       [[1, 288, 2, 160]]    [1, 288, 2, 160]        7,200     \n",
      "   BatchNorm-33      [[1, 288, 2, 160]]    [1, 288, 2, 160]        1,152     \n",
      "  ConvBNLayer-33     [[1, 288, 2, 160]]    [1, 288, 2, 160]          0       \n",
      "AdaptiveAvgPool2D-9  [[1, 288, 2, 160]]     [1, 288, 1, 1]           0       \n",
      "     Conv2D-50        [[1, 288, 1, 1]]      [1, 72, 1, 1]         20,808     \n",
      "     Conv2D-51        [[1, 72, 1, 1]]       [1, 288, 1, 1]        21,024     \n",
      "    SEModule-9       [[1, 288, 2, 160]]    [1, 288, 2, 160]          0       \n",
      "     Conv2D-52       [[1, 288, 2, 160]]    [1, 48, 2, 160]        13,824     \n",
      "   BatchNorm-34      [[1, 48, 2, 160]]     [1, 48, 2, 160]          192      \n",
      "  ConvBNLayer-34     [[1, 288, 2, 160]]    [1, 48, 2, 160]           0       \n",
      "  ResidualUnit-11    [[1, 48, 2, 160]]     [1, 48, 2, 160]           0       \n",
      "     Conv2D-53       [[1, 48, 2, 160]]     [1, 288, 2, 160]       13,824     \n",
      "   BatchNorm-35      [[1, 288, 2, 160]]    [1, 288, 2, 160]        1,152     \n",
      "  ConvBNLayer-35     [[1, 48, 2, 160]]     [1, 288, 2, 160]          0       \n",
      "    MaxPool2D-1      [[1, 288, 2, 160]]    [1, 288, 1, 80]           0       \n",
      "===============================================================================\n",
      "Total params: 259,056\n",
      "Trainable params: 246,736\n",
      "Non-trainable params: 12,320\n",
      "-------------------------------------------------------------------------------\n",
      "Input size (MB): 0.12\n",
      "Forward/backward pass size (MB): 44.38\n",
      "Params size (MB): 0.99\n",
      "Estimated Total Size (MB): 45.48\n",
      "-------------------------------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'total_params': 259056, 'trainable_params': 246736}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义网络输入shape\n",
    "IMAGE_SHAPE_C = 3\n",
    "IMAGE_SHAPE_H = 32\n",
    "IMAGE_SHAPE_W = 320\n",
    "\n",
    "\n",
    "# 可视化网络结构\n",
    "paddle.summary(MobileNetV3(),[(1, IMAGE_SHAPE_C, IMAGE_SHAPE_H, IMAGE_SHAPE_W)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "backbone output: [1, 288, 1, 80]\n"
     ]
    }
   ],
   "source": [
    "# 图片输入骨干网络\n",
    "backbone = MobileNetV3()\n",
    "# 将numpy数据转换为Tensor\n",
    "input_data = paddle.to_tensor([padding_im])\n",
    "# 骨干网络输出\n",
    "feature = backbone(input_data)\n",
    "# 查看feature map的纬度\n",
    "print(\"backbone output:\", feature.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "* neck\n",
    "\n",
    "neck 部分将backbone输出的视觉特征图转换为1维向量输入送到 LSTM 网络中，输出序列特征（ [源码位置](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/ppocr/modeling/necks/rnn.py) ）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class Im2Seq(nn.Layer):\n",
    "    def __init__(self, in_channels, **kwargs):\n",
    "        \"\"\"\n",
    "        图像特征转换为序列特征\n",
    "        :param in_channels: 输入通道数\n",
    "        \"\"\" \n",
    "        super().__init__()\n",
    "        self.out_channels = in_channels\n",
    "\n",
    "    def forward(self, x):\n",
    "        B, C, H, W = x.shape\n",
    "        assert H == 1\n",
    "        x = x.squeeze(axis=2)\n",
    "        x = x.transpose([0, 2, 1])  # (NWC)(batch, width, channels)\n",
    "        return x\n",
    "\n",
    "class EncoderWithRNN(nn.Layer):\n",
    "    def __init__(self, in_channels, hidden_size):\n",
    "        super(EncoderWithRNN, self).__init__()\n",
    "        self.out_channels = hidden_size * 2\n",
    "        self.lstm = nn.LSTM(\n",
    "            in_channels, hidden_size, direction='bidirectional', num_layers=2)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x, _ = self.lstm(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "class SequenceEncoder(nn.Layer):\n",
    "    def __init__(self, in_channels, hidden_size=48, **kwargs):\n",
    "        \"\"\"\n",
    "        序列编码\n",
    "        :param in_channels: 输入通道数\n",
    "        :param hidden_size: 隐藏层size\n",
    "        \"\"\" \n",
    "        super(SequenceEncoder, self).__init__()\n",
    "        self.encoder_reshape = Im2Seq(in_channels)\n",
    "\n",
    "        self.encoder = EncoderWithRNN(\n",
    "            self.encoder_reshape.out_channels, hidden_size)\n",
    "        self.out_channels = self.encoder.out_channels\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.encoder_reshape(x)\n",
    "        x = self.encoder(x)\n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sequence shape: [1, 80, 96]\n"
     ]
    }
   ],
   "source": [
    "neck = SequenceEncoder(in_channels=288)\n",
    "sequence = neck(feature)\n",
    "print(\"sequence shape:\", sequence.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "* head\n",
    "\n",
    "预测头部分由全连接层和softmax组成，用于计算序列特征时间步上的标签概率分布，本示例仅支持模型识别小写英文字母和数字（26+10）36个类别（[源码位置](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/ppocr/modeling/heads/rec_ctc_head.py)）:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class CTCHead(nn.Layer):\n",
    "    def __init__(self,\n",
    "                 in_channels,\n",
    "                 out_channels,\n",
    "                 **kwargs):\n",
    "        \"\"\"\n",
    "        CTC 预测层\n",
    "        :param in_channels: 输入通道数\n",
    "        :param out_channels: 输出通道数\n",
    "        \"\"\" \n",
    "        super(CTCHead, self).__init__()\n",
    "        self.fc = nn.Linear(\n",
    "            in_channels,\n",
    "            out_channels)\n",
    "        \n",
    "        # 思考：out_channels 应该等于多少？\n",
    "        self.out_channels = out_channels\n",
    "\n",
    "    def forward(self, x):\n",
    "        predicts = self.fc(x)\n",
    "        result = predicts\n",
    "\n",
    "        if not self.training:\n",
    "            predicts = F.softmax(predicts, axis=2)\n",
    "            result = predicts\n",
    "\n",
    "        return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "在网络随机初始化的情况下，输出结果是无序的，经过SoftMax之后，可以得到各时间步上的概率最大的预测结果，其中：`pred_id` 代表预测的标签ID，`pre_scores` 代表预测结果的置信度："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predict shape: [1, 80, 37]\n",
      "pred_id: Tensor(shape=[1, 80], dtype=int64, place=CUDAPlace(0), stop_gradient=False,\n",
      "       [[23, 28, 23, 23, 23, 23, 23, 23, 23, 23, 23, 30, 30, 30, 31, 23, 23, 23, 23, 23, 23, 23, 31, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 5 ]])\n",
      "pred_scores: Tensor(shape=[1, 80], dtype=float32, place=CUDAPlace(0), stop_gradient=False,\n",
      "       [[0.03683758, 0.03368053, 0.03604801, 0.03504696, 0.03696444, 0.03597261, 0.03925638, 0.03650934, 0.03873367, 0.03572492, 0.03543066, 0.03618268, 0.03805700, 0.03496549, 0.03329032, 0.03565763, 0.03846950, 0.03922413, 0.03970327, 0.03638541, 0.03572393, 0.03618102, 0.03565401, 0.03636984, 0.03691722, 0.03718850, 0.03623354, 0.03877943, 0.03731697, 0.03563465, 0.03447339, 0.03365586, 0.03312979, 0.03285240, 0.03273271, 0.03269565, 0.03269779, 0.03271412, 0.03273287, 0.03274929, 0.03276210, 0.03277146, 0.03277802, 0.03278249, 0.03278547, 0.03278742, 0.03278869, 0.03278949, 0.03279000, 0.03279032, 0.03279052, 0.03279064, 0.03279071, 0.03279077, 0.03279081, 0.03279087, 0.03279094, 0.03279106, 0.03279124, 0.03279152, 0.03279196, 0.03279264, 0.03279363, 0.03279509, 0.03279718, 0.03280006, 0.03280392, 0.03280888, 0.03281487, 0.03282148, 0.03282760, 0.03283087, 0.03282646, 0.03280647, 0.03275031, 0.03263619, 0.03242587, 0.03194289, 0.03122442, 0.02986610]])\n"
     ]
    }
   ],
   "source": [
    "ctc_head = CTCHead(in_channels=96, out_channels=37)\n",
    "predict = ctc_head(sequence)\n",
    "print(\"predict shape:\", predict.shape)\n",
    "result = F.softmax(predict, axis=2)\n",
    "pred_id = paddle.argmax(result, axis=2)\n",
    "pred_socres = paddle.max(result, axis=2)\n",
    "print(\"pred_id:\", pred_id)\n",
    "print(\"pred_scores:\", pred_socres)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "* 后处理\n",
    "\n",
    "识别网络最终返回的结果是各个时间步上的最大索引值，最终期望的输出是对应的文字结果，因此CRNN的后处理是一个解码过程，主要逻辑如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def decode(text_index, text_prob=None, is_remove_duplicate=False):\n",
    "    \"\"\" convert text-index into text-label. \"\"\"\n",
    "    character = \"-0123456789abcdefghijklmnopqrstuvwxyz\"\n",
    "    result_list = []\n",
    "    # 忽略tokens [0] 代表ctc中的blank位\n",
    "    ignored_tokens = [0]\n",
    "    batch_size = len(text_index)\n",
    "    for batch_idx in range(batch_size):\n",
    "        char_list = []\n",
    "        conf_list = []\n",
    "        for idx in range(len(text_index[batch_idx])):\n",
    "            if text_index[batch_idx][idx] in ignored_tokens:\n",
    "                continue\n",
    "            # 合并blank之间相同的字符\n",
    "            if is_remove_duplicate:\n",
    "                # only for predict\n",
    "                if idx > 0 and text_index[batch_idx][idx - 1] == text_index[\n",
    "                        batch_idx][idx]:\n",
    "                    continue\n",
    "            # 将解码结果存在char_list内\n",
    "            char_list.append(character[int(text_index[batch_idx][\n",
    "                idx])])\n",
    "            # 记录置信度\n",
    "            if text_prob is not None:\n",
    "                conf_list.append(text_prob[batch_idx][idx])\n",
    "            else:\n",
    "                conf_list.append(1)\n",
    "        text = ''.join(char_list)\n",
    "        # 输出结果\n",
    "        result_list.append((text, np.mean(conf_list)))\n",
    "    return result_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "以 head 部分随机初始化预测出的结果为例，进行解码得到："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(shape=[1, 80], dtype=int64, place=CUDAPlace(0), stop_gradient=False,\n",
      "       [[23, 28, 23, 23, 23, 23, 23, 23, 23, 23, 23, 30, 30, 30, 31, 23, 23, 23, 23, 23, 23, 23, 31, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 5 ]])\n",
      "decode out: [('mrmmmmmmmmmtttummmmmmmummmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmm4', 0.034180813)]\n"
     ]
    }
   ],
   "source": [
    "pred_id = paddle.argmax(result, axis=2)\n",
    "pred_socres = paddle.max(result, axis=2)\n",
    "print(pred_id)\n",
    "decode_out = decode(pred_id, pred_socres)\n",
    "print(\"decode out:\", decode_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**小测试：** 如果输入模型训练好的index，解码结果是否正确呢？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "out: [('pain', 1.0)]\n"
     ]
    }
   ],
   "source": [
    "# 替换模型预测好的结果\n",
    "right_pred_id = paddle.to_tensor([['xxxxxxxxxxxxx']])\n",
    "tmp_scores = paddle.ones(shape=right_pred_id.shape)\n",
    "out = decode(right_pred_id, tmp_scores)\n",
    "print(\"out:\",out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "上述步骤完成了网络的搭建，也实现了一个简单的前向预测过程。\n",
    "\n",
    "没有经过训练的网络无法正确预测结果，因此需要定义损失函数、优化策略，将整个网络run起来，下面将详细介绍网络训练原理。\n",
    "\n",
    "\n",
    "## 3. 训练原理详解\n",
    "### 3.1 准备训练数据\n",
    "PaddleOCR 支持两种数据格式:\n",
    " - `lmdb` 用于训练以lmdb格式存储的数据集(LMDBDataSet);\n",
    " - `通用数据` 用于训练以文本文件存储的数据集(SimpleDataSet);\n",
    " \n",
    " 本次只介绍通用数据格式读取\n",
    "\n",
    "训练数据的默认存储路径是 `./train_data`, 执行以下命令解压数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!cd /home/aistudio/work/train_data/ && tar xf ic15_data.tar "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "解压完成后，训练图片都在同一个文件夹内，并有一个txt文件（rec_gt_train.txt）记录图片路径和标签，txt文件里的内容如下:\n",
    "\n",
    "```\n",
    "\" 图像文件名         图像标注信息 \"\n",
    "\n",
    "train/word_1.png\tGenaxis Theatre\n",
    "train/word_2.png\t[06]\n",
    "...\n",
    "```\n",
    "\n",
    "**注意：** txt文件中默认将图片路径和图片标签用 \\t 分割，如用其他方式分割将造成训练报错。\n",
    "\n",
    "\n",
    "数据集应有如下文件结构：\n",
    "```\n",
    "|-train_data\n",
    "  |-ic15_data\n",
    "    |- rec_gt_train.txt\n",
    "    |- train\n",
    "        |- word_001.png\n",
    "        |- word_002.jpg\n",
    "        |- word_003.jpg\n",
    "        | ...\n",
    "    |- rec_gt_test.txt\n",
    "    |- test\n",
    "        |- word_001.png\n",
    "        |- word_002.jpg\n",
    "        |- word_003.jpg\n",
    "        | ...\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "确认配置文件中的数据路径是否正确，以 [rec_icdar15_train.yml](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/configs/rec/rec_icdar15_train.yml)为例：\n",
    "\n",
    "```\n",
    "Train:\n",
    "  dataset:\n",
    "    name: SimpleDataSet\n",
    "    # 训练数据根目录\n",
    "    data_dir: ./train_data/ic15_data/\n",
    "    # 训练数据标签\n",
    "    label_file_list: [\"./train_data/ic15_data/rec_gt_train.txt\"]\n",
    "    transforms:\n",
    "      - DecodeImage: # load image\n",
    "          img_mode: BGR\n",
    "          channel_first: False\n",
    "      - CTCLabelEncode: # Class handling label\n",
    "      - RecResizeImg:\n",
    "          image_shape: [3, 32, 100]  # [3,32,320]\n",
    "      - KeepKeys:\n",
    "          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order\n",
    "  loader:\n",
    "    shuffle: True\n",
    "    batch_size_per_card: 256\n",
    "    drop_last: True\n",
    "    num_workers: 8\n",
    "    use_shared_memory: False\n",
    "\n",
    "Eval:\n",
    "  dataset:\n",
    "    name: SimpleDataSet\n",
    "    # 评估数据根目录\n",
    "    data_dir: ./train_data/ic15_data\n",
    "    # 评估数据标签\n",
    "    label_file_list: [\"./train_data/ic15_data/rec_gt_test.txt\"]\n",
    "    transforms:\n",
    "      - DecodeImage: # load image\n",
    "          img_mode: BGR\n",
    "          channel_first: False\n",
    "      - CTCLabelEncode: # Class handling label\n",
    "      - RecResizeImg:\n",
    "          image_shape: [3, 32, 100]\n",
    "      - KeepKeys:\n",
    "          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order\n",
    "  loader:\n",
    "    shuffle: False\n",
    "    drop_last: False\n",
    "    batch_size_per_card: 256\n",
    "    num_workers: 4\n",
    "    use_shared_memory: False\n",
    "    ```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 3.2 数据预处理\n",
    "\n",
    "送入网络的训练数据，需要保证一个batch内维度一致，同时为了不同维度之间的特征在数值上有一定的比较性，需要对数据做统一尺度**缩放**和**归一化**。\n",
    "\n",
    "为了增加模型的鲁棒性，抑制过拟合提升泛化性能，需要实现一定的**数据增广**。\n",
    "\n",
    "* 缩放和归一化\n",
    "\n",
    "第二节中已经介绍了相关内容，这是图片送入网络之前的最后一步操作。调用 `resize_norm_img` 完成图片缩放、padding和归一化。\n",
    "\n",
    "* 数据增广\n",
    "\n",
    "PaddleOCR中实现了多种数据增广方式，如：颜色反转、随机切割、仿射变化、随机噪声等等，这里以简单的随机切割为例，更多增广方式可参考：[rec_img_aug.py](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/ppocr/data/imaug/rec_img_aug.py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_crop(image):\n",
    "    \"\"\"\n",
    "    random crop\n",
    "    \"\"\"\n",
    "    import random\n",
    "    h, w, _ = image.shape\n",
    "    top_min = 1\n",
    "    top_max = 8\n",
    "    top_crop = int(random.randint(top_min, top_max))\n",
    "    top_crop = min(top_crop, h - 1)\n",
    "    crop_img = image.copy()\n",
    "    ratio = random.randint(0, 1)\n",
    "    if ratio:\n",
    "        crop_img = crop_img[top_crop:h, :, :]\n",
    "    else:\n",
    "        crop_img = crop_img[0:h - top_crop, :, :]\n",
    "    return crop_img\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 读图\n",
    "raw_img = cv2.imread(\"/home/aistudio/work/word_1.png\")\n",
    "plt.figure()\n",
    "plt.subplot(2,1,1)\n",
    "# 可视化原图\n",
    "plt.imshow(raw_img)\n",
    "# 随机切割\n",
    "crop_img = get_crop(raw_img)\n",
    "plt.subplot(2,1,2)\n",
    "# 可视化增广图\n",
    "plt.imshow(crop_img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 3.3 训练主程序\n",
    "\n",
    "模型训练的入口代码是 [train.py](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/tools/train.py)，它展示了训练中所需的各个模块： `build dataloader`, `build post process`, `build model` , `build loss`, `build optim`, `build metric`，将各部分串联后即可开始训练：\n",
    "\n",
    "* 构建 dataloader\n",
    "\n",
    "训练模型需要将数据组成指定数目的 batch ，并在训练过程中依次 yield 出来，本例中调用了 PaddleOCR 中实现的 [SimpleDataSet](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/ppocr/data/simple_dataset.py)\n",
    "\n",
    "基于原始代码稍作修改，其返回单条数据的主要逻辑如下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def __getitem__(data_line, data_dir):\n",
    "    import os\n",
    "    mode = \"train\"\n",
    "    delimiter = '\\t'\n",
    "    try:\n",
    "        substr = data_line.strip(\"\\n\").split(delimiter)\n",
    "        file_name = substr[0]\n",
    "        label = substr[1]\n",
    "        img_path = os.path.join(data_dir, file_name)\n",
    "        data = {'img_path': img_path, 'label': label}\n",
    "        if not os.path.exists(img_path):\n",
    "            raise Exception(\"{} does not exist!\".format(img_path))\n",
    "        with open(data['img_path'], 'rb') as f:\n",
    "            img = f.read()\n",
    "            data['image'] = img\n",
    "        # 预处理操作，先注释掉\n",
    "        # outs = transform(data, self.ops)\n",
    "        outs = data\n",
    "    except Exception as e:\n",
    "        print(\"When parsing line {}, error happened with msg: {}\".format(\n",
    "                data_line, e))\n",
    "        outs = None\n",
    "    return outs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "假设当前输入的标签为 `train/word_1.png\tGenaxis Theatre`, 训练数据的路径为 `/home/aistudio/work/train_data/ic15_data/`, 解析出的结果是一个字典，里面包含 `img_path` `label` `image` 三个字段："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'img_path': '/home/aistudio/work/train_data/ic15_data/train/word_1.png', 'label': 'Genaxis Theatre', 'image': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\x00Y\\x00\\x00\\x00\\x0e\\x08\\x02\\x00\\x00\\x00\\xcb\\xe2\\'\\xb7\\x00\\x00\\x00\\x01sRGB\\x00\\xae\\xce\\x1c\\xe9\\x00\\x00\\x00\\x04gAMA\\x00\\x00\\xb1\\x8f\\x0b\\xfca\\x05\\x00\\x00\\x00 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     ]
    }
   ],
   "source": [
    "data_line = \"train/word_1.png\tGenaxis Theatre\"\n",
    "data_dir = \"/home/aistudio/work/train_data/ic15_data/\"\n",
    "\n",
    "item = __getitem__(data_line, data_dir)\n",
    "print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "实现完单条数据返回逻辑后，调用 `padde.io.Dataloader` 即可把数据组合成batch，具体可参考 [build_dataloader]()\n",
    "\n",
    "\n",
    "* build model\n",
    "\n",
    "  build model 即搭建主要网络结构，具体细节如《2.3 代码实现》所述，本节不做过多介绍，各模块代码可参考[modeling](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.3/ppocr/modeling)\n",
    "\n",
    "* build loss\n",
    "  \n",
    "  CRNN 模型的损失函数为 CTC loss, 飞桨集成了常用的 Loss 函数，只需调用实现即可："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import paddle.nn as nn\n",
    "class CTCLoss(nn.Layer):\n",
    "    def __init__(self, use_focal_loss=False, **kwargs):\n",
    "        super(CTCLoss, self).__init__()\n",
    "        # blank 是 ctc 的无意义连接符\n",
    "        self.loss_func = nn.CTCLoss(blank=0, reduction='none')\n",
    "\n",
    "    def forward(self, predicts, batch):\n",
    "        if isinstance(predicts, (list, tuple)):\n",
    "            predicts = predicts[-1]\n",
    "        # 转置模型 head 层的预测结果，沿channel层排列\n",
    "        predicts = predicts.transpose((1, 0, 2)) #[80,1,37]\n",
    "        N, B, _ = predicts.shape\n",
    "        preds_lengths = paddle.to_tensor([N] * B, dtype='int64')\n",
    "        labels = batch[1].astype(\"int32\")\n",
    "        label_lengths = batch[2].astype('int64')\n",
    "        # 计算损失函数\n",
    "        loss = self.loss_func(predicts, labels, preds_lengths, label_lengths)\n",
    "        loss = loss.mean()\n",
    "        return {'loss': loss}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "* build post process\n",
    "\n",
    " 具体细节同样在《2.3 代码实现》有详细介绍，实现逻辑与之前一致。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "* build optim\n",
    "\n",
    "优化器使用 `Adam` , 同样调用飞桨API： `paddle.optimizer.Adam`\n",
    "\n",
    "* build metric\n",
    "\n",
    "metric 部分用于计算模型指标，PaddleOCR的文本识别中，将整句预测正确判断为预测正确，因此准确率计算主要逻辑如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def metric(preds, labels):    \n",
    "    correct_num = 0\n",
    "    all_num = 0\n",
    "    norm_edit_dis = 0.0\n",
    "    for (pred), (target) in zip(preds, labels):\n",
    "        pred = pred.replace(\" \", \"\")\n",
    "        target = target.replace(\" \", \"\")\n",
    "        if pred == target:\n",
    "            correct_num += 1\n",
    "        all_num += 1\n",
    "    correct_num += correct_num\n",
    "    all_num += all_num\n",
    "    return {\n",
    "        'acc': correct_num / all_num,\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: {'acc': 0.6}\n"
     ]
    }
   ],
   "source": [
    "preds = [\"aaa\", \"bbb\", \"ccc\", \"123\", \"456\"]\n",
    "labels = [\"aaa\", \"bbb\", \"ddd\", \"123\", \"444\"]\n",
    "acc = metric(preds, labels)\n",
    "print(\"acc:\", acc)\n",
    "# 五个预测结果中,完全正确的有3个，因此准确率应为0.6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "将以上各部分组合起来，即是完整的训练流程："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "def main(config, device, logger, vdl_writer):\n",
    "    # init dist environment\n",
    "    if config['Global']['distributed']:\n",
    "        dist.init_parallel_env()\n",
    "\n",
    "    global_config = config['Global']\n",
    "\n",
    "    # build dataloader\n",
    "    train_dataloader = build_dataloader(config, 'Train', device, logger)\n",
    "    if len(train_dataloader) == 0:\n",
    "        logger.error(\n",
    "            \"No Images in train dataset, please ensure\\n\" +\n",
    "            \"\\t1. The images num in the train label_file_list should be larger than or equal with batch size.\\n\"\n",
    "            +\n",
    "            \"\\t2. The annotation file and path in the configuration file are provided normally.\"\n",
    "        )\n",
    "        return\n",
    "\n",
    "    if config['Eval']:\n",
    "        valid_dataloader = build_dataloader(config, 'Eval', device, logger)\n",
    "    else:\n",
    "        valid_dataloader = None\n",
    "\n",
    "    # build post process\n",
    "    post_process_class = build_post_process(config['PostProcess'],\n",
    "                                            global_config)\n",
    "\n",
    "    # build model\n",
    "    # for rec algorithm\n",
    "    if hasattr(post_process_class, 'character'):\n",
    "        char_num = len(getattr(post_process_class, 'character'))\n",
    "        if config['Architecture'][\"algorithm\"] in [\"Distillation\",\n",
    "                                                   ]:  # distillation model\n",
    "            for key in config['Architecture'][\"Models\"]:\n",
    "                config['Architecture'][\"Models\"][key][\"Head\"][\n",
    "                    'out_channels'] = char_num\n",
    "        else:  # base rec model\n",
    "            config['Architecture'][\"Head\"]['out_channels'] = char_num\n",
    "\n",
    "    model = build_model(config['Architecture'])\n",
    "    if config['Global']['distributed']:\n",
    "        model = paddle.DataParallel(model)\n",
    "\n",
    "    # build loss\n",
    "    loss_class = build_loss(config['Loss'])\n",
    "\n",
    "    # build optim\n",
    "    optimizer, lr_scheduler = build_optimizer(\n",
    "        config['Optimizer'],\n",
    "        epochs=config['Global']['epoch_num'],\n",
    "        step_each_epoch=len(train_dataloader),\n",
    "        parameters=model.parameters())\n",
    "\n",
    "    # build metric\n",
    "    eval_class = build_metric(config['Metric'])\n",
    "    # load pretrain model\n",
    "    pre_best_model_dict = load_model(config, model, optimizer)\n",
    "    logger.info('train dataloader has {} iters'.format(len(train_dataloader)))\n",
    "    if valid_dataloader is not None:\n",
    "        logger.info('valid dataloader has {} iters'.format(\n",
    "            len(valid_dataloader)))\n",
    "\n",
    "    use_amp = config[\"Global\"].get(\"use_amp\", False)\n",
    "    if use_amp:\n",
    "        AMP_RELATED_FLAGS_SETTING = {\n",
    "            'FLAGS_cudnn_batchnorm_spatial_persistent': 1,\n",
    "            'FLAGS_max_inplace_grad_add': 8,\n",
    "        }\n",
    "        paddle.fluid.set_flags(AMP_RELATED_FLAGS_SETTING)\n",
    "        scale_loss = config[\"Global\"].get(\"scale_loss\", 1.0)\n",
    "        use_dynamic_loss_scaling = config[\"Global\"].get(\n",
    "            \"use_dynamic_loss_scaling\", False)\n",
    "        scaler = paddle.amp.GradScaler(\n",
    "            init_loss_scaling=scale_loss,\n",
    "            use_dynamic_loss_scaling=use_dynamic_loss_scaling)\n",
    "    else:\n",
    "        scaler = None\n",
    "\n",
    "    # start train\n",
    "    program.train(config, train_dataloader, valid_dataloader, device, model,\n",
    "                  loss_class, optimizer, lr_scheduler, post_process_class,\n",
    "                  eval_class, pre_best_model_dict, logger, vdl_writer, scaler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 4. 完整训练任务\n",
    "\n",
    "### 4.1 启动训练\n",
    "\n",
    "PaddleOCR 识别任务与检测任务类似，是通过配置文件传输参数的。\n",
    "\n",
    "要进行完整的模型训练，首先需要下载整个项目并安装相关依赖："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: shapely in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 1)) (1.8.0)\n",
      "Collecting scikit-image==0.17.2\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d7/ee/753ea56fda5bc2a5516a1becb631bf5ada593a2dd44f21971a13a762d4db/scikit_image-0.17.2-cp37-cp37m-manylinux1_x86_64.whl (12.5 MB)\n",
      "     |████████████████████████████████| 12.5 MB 8.4 MB/s            \n",
      "\u001b[?25hRequirement already satisfied: imgaug==0.4.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 3)) (0.4.0)\n",
      "Requirement already satisfied: pyclipper in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 4)) (1.3.0.post2)\n",
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      "Requirement already satisfied: tqdm in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 6)) (4.36.1)\n",
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      "Requirement already satisfied: visualdl in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 8)) (2.2.0)\n",
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      "Requirement already satisfied: opencv-contrib-python==4.4.0.46 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 10)) (4.4.0.46)\n",
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      "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,>=4.3.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-image==0.17.2->-r requirements.txt (line 2)) (7.1.2)\n",
      "Requirement already satisfied: networkx>=2.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-image==0.17.2->-r requirements.txt (line 2)) (2.4)\n",
      "Requirement already satisfied: scipy>=1.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-image==0.17.2->-r requirements.txt (line 2)) (1.6.3)\n",
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      "Requirement already satisfied: et-xmlfile in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from openpyxl->-r requirements.txt (line 13)) (1.0.1)\n",
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      "Requirement already satisfied: importlib-metadata in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from flake8>=3.7.9->visualdl->-r requirements.txt (line 8)) (0.23)\n",
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      "Requirement already satisfied: Babel>=2.3 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from Flask-Babel>=1.0.0->visualdl->-r requirements.txt (line 8)) (2.8.0)\n",
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      "Requirement already satisfied: aspy.yaml in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pre-commit->visualdl->-r requirements.txt (line 8)) (1.3.0)\n",
      "Requirement already satisfied: identify>=1.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from pre-commit->visualdl->-r requirements.txt (line 8)) (1.4.10)\n",
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      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests->visualdl->-r requirements.txt (line 8)) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests->visualdl->-r requirements.txt (line 8)) (2019.9.11)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests->visualdl->-r requirements.txt (line 8)) (1.25.6)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from Jinja2>=2.10.1->flask>=1.1.1->visualdl->-r requirements.txt (line 8)) (1.1.1)\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from importlib-metadata->flake8>=3.7.9->visualdl->-r requirements.txt (line 8)) (3.6.0)\n",
      "Installing collected packages: scikit-image\n",
      "  Attempting uninstall: scikit-image\n",
      "    Found existing installation: scikit-image 0.19.1\n",
      "    Uninstalling scikit-image-0.19.1:\n",
      "      Successfully uninstalled scikit-image-0.19.1\n",
      "Successfully installed scikit-image-0.17.2\n"
     ]
    }
   ],
   "source": [
    "# 克隆PaddleOCR代码\n",
    "#!git clone https://gitee.com/paddlepaddle/PaddleOCR\n",
    "# 修改代码运行的默认目录为 /home/aistudio/PaddleOCR\n",
    "import os\n",
    "os.chdir(\"/home/aistudio/PaddleOCR\")\n",
    "# 安装PaddleOCR第三方依赖\n",
    "!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "创建软链，将训练数据放在PaddleOCR项目下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!ln -s /home/aistudio/work/train_data/ /home/aistudio/PaddleOCR/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "下载预训练模型：\n",
    "\n",
    "为了加快收敛速度，建议下载训练好的模型在 icdar2015 数据上进行 finetune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-12-22 15:39:39--  https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar\n",
      "Resolving paddleocr.bj.bcebos.com (paddleocr.bj.bcebos.com)... 182.61.200.195, 182.61.200.229, 2409:8c04:1001:1002:0:ff:b001:368a\n",
      "Connecting to paddleocr.bj.bcebos.com (paddleocr.bj.bcebos.com)|182.61.200.195|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 51200000 (49M) [application/x-tar]\n",
      "Saving to: ‘./pretrain_models/rec_mv3_none_bilstm_ctc_v2.0_train.tar’\n",
      "\n",
      "rec_mv3_none_bilstm 100%[===================>]  48.83M  15.5MB/s    in 3.6s    \n",
      "\n",
      "2021-12-22 15:39:42 (13.7 MB/s) - ‘./pretrain_models/rec_mv3_none_bilstm_ctc_v2.0_train.tar’ saved [51200000/51200000]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!cd PaddleOCR/\n",
    "# 下载MobileNetV3的预训练模型\n",
    "!wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar\n",
    "# 解压模型参数\n",
    "!tar -xf pretrain_models/rec_mv3_none_bilstm_ctc_v2.0_train.tar && rm -rf pretrain_models/rec_mv3_none_bilstm_ctc_v2.0_train.tar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "启动训练命令很简单，指定好配置文件即可。另外在命令行中可以通过 `-o` 修改配置文件中的参数值。启动训练命令如下所示\n",
    "\n",
    "其中：\n",
    "\n",
    "* `Global.pretrained_model`: 加载的预训练模型路径\n",
    "* `Global.character_dict_path` ： 字典路径（这里只支持26个小写字母+数字）\n",
    "* `Global.eval_batch_step` ： 评估频率\n",
    "* `Global.epoch_num`： 总训练轮数\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/skimage/morphology/_skeletonize.py:241: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  0, 1, 1, 0, 0, 1, 0, 0, 0], dtype=np.bool)\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/skimage/morphology/_skeletonize.py:256: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.bool)\n",
      "[2021/12/23 20:28:15] root INFO: Architecture : \n",
      "[2021/12/23 20:28:15] root INFO:     Backbone : \n",
      "[2021/12/23 20:28:15] root INFO:         model_name : large\n",
      "[2021/12/23 20:28:15] root INFO:         name : MobileNetV3\n",
      "[2021/12/23 20:28:15] root INFO:         scale : 0.5\n",
      "[2021/12/23 20:28:15] root INFO:     Head : \n",
      "[2021/12/23 20:28:15] root INFO:         fc_decay : 0\n",
      "[2021/12/23 20:28:15] root INFO:         name : CTCHead\n",
      "[2021/12/23 20:28:15] root INFO:     Neck : \n",
      "[2021/12/23 20:28:15] root INFO:         encoder_type : rnn\n",
      "[2021/12/23 20:28:15] root INFO:         hidden_size : 96\n",
      "[2021/12/23 20:28:15] root INFO:         name : SequenceEncoder\n",
      "[2021/12/23 20:28:15] root INFO:     Transform : None\n",
      "[2021/12/23 20:28:15] root INFO:     algorithm : CRNN\n",
      "[2021/12/23 20:28:15] root INFO:     model_type : rec\n",
      "[2021/12/23 20:28:15] root INFO: Eval : \n",
      "[2021/12/23 20:28:15] root INFO:     dataset : \n",
      "[2021/12/23 20:28:15] root INFO:         data_dir : ./train_data/ic15_data\n",
      "[2021/12/23 20:28:15] root INFO:         label_file_list : ['./train_data/ic15_data/rec_gt_test.txt']\n",
      "[2021/12/23 20:28:15] root INFO:         name : SimpleDataSet\n",
      "[2021/12/23 20:28:15] root INFO:         transforms : \n",
      "[2021/12/23 20:28:15] root INFO:             DecodeImage : \n",
      "[2021/12/23 20:28:15] root INFO:                 channel_first : False\n",
      "[2021/12/23 20:28:15] root INFO:                 img_mode : BGR\n",
      "[2021/12/23 20:28:15] root INFO:             CTCLabelEncode : None\n",
      "[2021/12/23 20:28:15] root INFO:             RecResizeImg : \n",
      "[2021/12/23 20:28:15] root INFO:                 image_shape : [3, 32, 100]\n",
      "[2021/12/23 20:28:15] root INFO:             KeepKeys : \n",
      "[2021/12/23 20:28:15] root INFO:                 keep_keys : ['image', 'label', 'length']\n",
      "[2021/12/23 20:28:15] root INFO:     loader : \n",
      "[2021/12/23 20:28:15] root INFO:         batch_size_per_card : 256\n",
      "[2021/12/23 20:28:15] root INFO:         drop_last : False\n",
      "[2021/12/23 20:28:15] root INFO:         num_workers : 4\n",
      "[2021/12/23 20:28:15] root INFO:         shuffle : False\n",
      "[2021/12/23 20:28:15] root INFO:         use_shared_memory : False\n",
      "[2021/12/23 20:28:15] root INFO: Global : \n",
      "[2021/12/23 20:28:15] root INFO:     cal_metric_during_train : True\n",
      "[2021/12/23 20:28:15] root INFO:     character_dict_path : ppocr/utils/ic15_dict.txt\n",
      "[2021/12/23 20:28:15] root INFO:     character_type : EN\n",
      "[2021/12/23 20:28:15] root INFO:     checkpoints : None\n",
      "[2021/12/23 20:28:15] root INFO:     debug : False\n",
      "[2021/12/23 20:28:15] root INFO:     distributed : False\n",
      "[2021/12/23 20:28:15] root INFO:     epoch_num : 40\n",
      "[2021/12/23 20:28:15] root INFO:     eval_batch_step : [0, 200]\n",
      "[2021/12/23 20:28:15] root INFO:     infer_img : doc/imgs_words_en/word_19.png\n",
      "[2021/12/23 20:28:15] root INFO:     infer_mode : False\n",
      "[2021/12/23 20:28:15] root INFO:     log_smooth_window : 20\n",
      "[2021/12/23 20:28:15] root INFO:     max_text_length : 25\n",
      "[2021/12/23 20:28:15] root INFO:     pretrained_model : rec_mv3_none_bilstm_ctc_v2.0_train/best_accuracy\n",
      "[2021/12/23 20:28:15] root INFO:     print_batch_step : 10\n",
      "[2021/12/23 20:28:15] root INFO:     save_epoch_step : 3\n",
      "[2021/12/23 20:28:15] root INFO:     save_inference_dir : ./\n",
      "[2021/12/23 20:28:15] root INFO:     save_model_dir : ./output/rec/ic15/\n",
      "[2021/12/23 20:28:15] root INFO:     save_res_path : ./output/rec/predicts_ic15.txt\n",
      "[2021/12/23 20:28:15] root INFO:     use_gpu : True\n",
      "[2021/12/23 20:28:15] root INFO:     use_space_char : False\n",
      "[2021/12/23 20:28:15] root INFO:     use_visualdl : False\n",
      "[2021/12/23 20:28:15] root INFO: Loss : \n",
      "[2021/12/23 20:28:15] root INFO:     name : CTCLoss\n",
      "[2021/12/23 20:28:15] root INFO: Metric : \n",
      "[2021/12/23 20:28:15] root INFO:     main_indicator : acc\n",
      "[2021/12/23 20:28:15] root INFO:     name : RecMetric\n",
      "[2021/12/23 20:28:15] root INFO: Optimizer : \n",
      "[2021/12/23 20:28:15] root INFO:     beta1 : 0.9\n",
      "[2021/12/23 20:28:15] root INFO:     beta2 : 0.999\n",
      "[2021/12/23 20:28:15] root INFO:     lr : \n",
      "[2021/12/23 20:28:15] root INFO:         learning_rate : 0.0005\n",
      "[2021/12/23 20:28:15] root INFO:     name : Adam\n",
      "[2021/12/23 20:28:15] root INFO:     regularizer : \n",
      "[2021/12/23 20:28:15] root INFO:         factor : 0\n",
      "[2021/12/23 20:28:15] root INFO:         name : L2\n",
      "[2021/12/23 20:28:15] root INFO: PostProcess : \n",
      "[2021/12/23 20:28:15] root INFO:     name : CTCLabelDecode\n",
      "[2021/12/23 20:28:15] root INFO: Train : \n",
      "[2021/12/23 20:28:15] root INFO:     dataset : \n",
      "[2021/12/23 20:28:15] root INFO:         data_dir : ./train_data/ic15_data/\n",
      "[2021/12/23 20:28:15] root INFO:         label_file_list : ['./train_data/ic15_data/rec_gt_train.txt']\n",
      "[2021/12/23 20:28:15] root INFO:         name : SimpleDataSet\n",
      "[2021/12/23 20:28:15] root INFO:         transforms : \n",
      "[2021/12/23 20:28:15] root INFO:             DecodeImage : \n",
      "[2021/12/23 20:28:15] root INFO:                 channel_first : False\n",
      "[2021/12/23 20:28:15] root INFO:                 img_mode : BGR\n",
      "[2021/12/23 20:28:15] root INFO:             CTCLabelEncode : None\n",
      "[2021/12/23 20:28:15] root INFO:             RecResizeImg : \n",
      "[2021/12/23 20:28:15] root INFO:                 image_shape : [3, 32, 100]\n",
      "[2021/12/23 20:28:15] root INFO:             KeepKeys : \n",
      "[2021/12/23 20:28:15] root INFO:                 keep_keys : ['image', 'label', 'length']\n",
      "[2021/12/23 20:28:15] root INFO:     loader : \n",
      "[2021/12/23 20:28:15] root INFO:         batch_size_per_card : 256\n",
      "[2021/12/23 20:28:15] root INFO:         drop_last : True\n",
      "[2021/12/23 20:28:15] root INFO:         num_workers : 8\n",
      "[2021/12/23 20:28:15] root INFO:         shuffle : True\n",
      "[2021/12/23 20:28:15] root INFO:         use_shared_memory : False\n",
      "[2021/12/23 20:28:15] root INFO: train with paddle 2.1.2 and device CUDAPlace(0)\n",
      "[2021/12/23 20:28:15] root INFO: Initialize indexs of datasets:['./train_data/ic15_data/rec_gt_train.txt']\n",
      "[2021/12/23 20:28:15] root INFO: Initialize indexs of datasets:['./train_data/ic15_data/rec_gt_test.txt']\n",
      "W1223 20:28:15.851713   306 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1\n",
      "W1223 20:28:15.857080   306 device_context.cc:422] device: 0, cuDNN Version: 7.6.\n",
      "[2021/12/23 20:28:19] root INFO: loaded pretrained_model successful from rec_mv3_none_bilstm_ctc_v2.0_train/best_accuracy.pdparams\n",
      "[2021/12/23 20:28:19] root INFO: train dataloader has 17 iters\n",
      "[2021/12/23 20:28:19] root INFO: valid dataloader has 9 iters\n",
      "[2021/12/23 20:28:19] root INFO: During the training process, after the 0th iteration, an evaluation is run every 200 iterations\n",
      "[2021/12/23 20:28:19] root INFO: Initialize indexs of datasets:['./train_data/ic15_data/rec_gt_train.txt']\n",
      "[2021/12/23 20:28:23] root INFO: epoch: [1/40], iter: 10, lr: 0.000500, loss: 9.336592, acc: 0.203125, norm_edit_dis: 0.674909, reader_cost: 0.27284 s, batch_cost: 0.40185 s, samples: 2816, ips: 700.75290\n",
      "[2021/12/23 20:28:24] root INFO: epoch: [1/40], iter: 16, lr: 0.000500, loss: 6.955496, acc: 0.210938, norm_edit_dis: 0.678930, reader_cost: 0.00008 s, batch_cost: 0.05430 s, samples: 1536, ips: 2828.80514\n",
      "[2021/12/23 20:28:24] root INFO: save model in ./output/rec/ic15/latest\n",
      "[2021/12/23 20:28:24] root INFO: Initialize indexs of datasets:['./train_data/ic15_data/rec_gt_train.txt']\n",
      "[2021/12/23 20:28:28] root INFO: epoch: [2/40], iter: 20, lr: 0.000500, loss: 6.402417, acc: 0.246094, norm_edit_dis: 0.695874, reader_cost: 0.24180 s, batch_cost: 0.34361 s, samples: 1024, ips: 298.00945\n",
      "[2021/12/23 20:28:29] root INFO: epoch: [2/40], iter: 30, lr: 0.000500, loss: 4.007382, acc: 0.412109, norm_edit_dis: 0.743064, reader_cost: 0.00013 s, batch_cost: 0.08982 s, samples: 2560, ips: 2849.98954\n",
      "[2021/12/23 20:28:29] root INFO: epoch: [2/40], iter: 33, lr: 0.000500, loss: 3.906031, acc: 0.458984, norm_edit_dis: 0.770415, reader_cost: 0.00004 s, batch_cost: 0.02684 s, samples: 768, ips: 2861.80304\n",
      "^C\n",
      "main proc 306 exit, kill process group 306\n"
     ]
    }
   ],
   "source": [
    "!python3 tools/train.py -c configs/rec/rec_icdar15_train.yml \\\n",
    "   -o Global.pretrained_model=rec_mv3_none_bilstm_ctc_v2.0_train/best_accuracy \\\n",
    "   Global.character_dict_path=ppocr/utils/ic15_dict.txt \\\n",
    "   Global.eval_batch_step=[0,200] \\\n",
    "   Global.epoch_num=40"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "根据配置文件中设置的的 `save_model_dir` 字段，会有以下几种参数被保存下来：\n",
    "\n",
    "```\n",
    "output/rec/ic15\n",
    "├── best_accuracy.pdopt  \n",
    "├── best_accuracy.pdparams  \n",
    "├── best_accuracy.states  \n",
    "├── config.yml  \n",
    "├── iter_epoch_3.pdopt  \n",
    "├── iter_epoch_3.pdparams  \n",
    "├── iter_epoch_3.states  \n",
    "├── latest.pdopt  \n",
    "├── latest.pdparams  \n",
    "├── latest.states  \n",
    "└── train.log\n",
    "```\n",
    "其中 best_accuracy.* 是评估集上的最优模型；iter_epoch_x.* 是以 `save_epoch_step` 为间隔保存下来的模型；latest.* 是最后一个epoch的模型。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**总结：**\n",
    "\n",
    "如果需要训练自己的数据需要修改：\n",
    "\n",
    "1. 训练和评估数据路径（必须）\n",
    "2. 字典路径（必须）\n",
    "3. 预训练模型 （可选）\n",
    "4. 学习率、image shape、网络结构（可选）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 4.2 模型评估\n",
    "\n",
    "\n",
    "评估数据集可以通过 `configs/rec/rec_icdar15_train.yml`  修改Eval中的 `label_file_path` 设置。\n",
    "\n",
    "这里默认使用 icdar2015 的评估集，加载刚刚训练好的模型权重："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2021/12/23 14:27:51] root INFO: Architecture : \n",
      "[2021/12/23 14:27:51] root INFO:     Backbone : \n",
      "[2021/12/23 14:27:51] root INFO:         model_name : large\n",
      "[2021/12/23 14:27:51] root INFO:         name : MobileNetV3\n",
      "[2021/12/23 14:27:51] root INFO:         scale : 0.5\n",
      "[2021/12/23 14:27:51] root INFO:     Head : \n",
      "[2021/12/23 14:27:51] root INFO:         fc_decay : 0\n",
      "[2021/12/23 14:27:51] root INFO:         name : CTCHead\n",
      "[2021/12/23 14:27:51] root INFO:     Neck : \n",
      "[2021/12/23 14:27:51] root INFO:         encoder_type : rnn\n",
      "[2021/12/23 14:27:51] root INFO:         hidden_size : 96\n",
      "[2021/12/23 14:27:51] root INFO:         name : SequenceEncoder\n",
      "[2021/12/23 14:27:51] root INFO:     Transform : None\n",
      "[2021/12/23 14:27:51] root INFO:     algorithm : CRNN\n",
      "[2021/12/23 14:27:51] root INFO:     model_type : rec\n",
      "[2021/12/23 14:27:51] root INFO: Eval : \n",
      "[2021/12/23 14:27:51] root INFO:     dataset : \n",
      "[2021/12/23 14:27:51] root INFO:         data_dir : ./train_data/ic15_data\n",
      "[2021/12/23 14:27:51] root INFO:         label_file_list : ['./train_data/ic15_data/rec_gt_test.txt']\n",
      "[2021/12/23 14:27:51] root INFO:         name : SimpleDataSet\n",
      "[2021/12/23 14:27:51] root INFO:         transforms : \n",
      "[2021/12/23 14:27:51] root INFO:             DecodeImage : \n",
      "[2021/12/23 14:27:51] root INFO:                 channel_first : False\n",
      "[2021/12/23 14:27:51] root INFO:                 img_mode : BGR\n",
      "[2021/12/23 14:27:51] root INFO:             CTCLabelEncode : None\n",
      "[2021/12/23 14:27:51] root INFO:             RecResizeImg : \n",
      "[2021/12/23 14:27:51] root INFO:                 image_shape : [3, 32, 100]\n",
      "[2021/12/23 14:27:51] root INFO:             KeepKeys : \n",
      "[2021/12/23 14:27:51] root INFO:                 keep_keys : ['image', 'label', 'length']\n",
      "[2021/12/23 14:27:51] root INFO:     loader : \n",
      "[2021/12/23 14:27:51] root INFO:         batch_size_per_card : 256\n",
      "[2021/12/23 14:27:51] root INFO:         drop_last : False\n",
      "[2021/12/23 14:27:51] root INFO:         num_workers : 4\n",
      "[2021/12/23 14:27:51] root INFO:         shuffle : False\n",
      "[2021/12/23 14:27:51] root INFO:         use_shared_memory : False\n",
      "[2021/12/23 14:27:51] root INFO: Global : \n",
      "[2021/12/23 14:27:51] root INFO:     cal_metric_during_train : True\n",
      "[2021/12/23 14:27:51] root INFO:     character_dict_path : ppocr/utils/ic15_dict.txt\n",
      "[2021/12/23 14:27:51] root INFO:     character_type : EN\n",
      "[2021/12/23 14:27:51] root INFO:     checkpoints : output/rec/ic15/best_accuracy\n",
      "[2021/12/23 14:27:51] root INFO:     debug : False\n",
      "[2021/12/23 14:27:51] root INFO:     distributed : False\n",
      "[2021/12/23 14:27:51] root INFO:     epoch_num : 72\n",
      "[2021/12/23 14:27:51] root INFO:     eval_batch_step : [0, 2000]\n",
      "[2021/12/23 14:27:51] root INFO:     infer_img : doc/imgs_words_en/word_10.png\n",
      "[2021/12/23 14:27:51] root INFO:     infer_mode : False\n",
      "[2021/12/23 14:27:51] root INFO:     log_smooth_window : 20\n",
      "[2021/12/23 14:27:51] root INFO:     max_text_length : 25\n",
      "[2021/12/23 14:27:51] root INFO:     pretrained_model : None\n",
      "[2021/12/23 14:27:51] root INFO:     print_batch_step : 10\n",
      "[2021/12/23 14:27:51] root INFO:     save_epoch_step : 3\n",
      "[2021/12/23 14:27:51] root INFO:     save_inference_dir : ./\n",
      "[2021/12/23 14:27:51] root INFO:     save_model_dir : ./output/rec/ic15/\n",
      "[2021/12/23 14:27:51] root INFO:     save_res_path : ./output/rec/predicts_ic15.txt\n",
      "[2021/12/23 14:27:51] root INFO:     use_gpu : True\n",
      "[2021/12/23 14:27:51] root INFO:     use_space_char : False\n",
      "[2021/12/23 14:27:51] root INFO:     use_visualdl : False\n",
      "[2021/12/23 14:27:51] root INFO: Loss : \n",
      "[2021/12/23 14:27:51] root INFO:     name : CTCLoss\n",
      "[2021/12/23 14:27:51] root INFO: Metric : \n",
      "[2021/12/23 14:27:51] root INFO:     main_indicator : acc\n",
      "[2021/12/23 14:27:51] root INFO:     name : RecMetric\n",
      "[2021/12/23 14:27:51] root INFO: Optimizer : \n",
      "[2021/12/23 14:27:51] root INFO:     beta1 : 0.9\n",
      "[2021/12/23 14:27:51] root INFO:     beta2 : 0.999\n",
      "[2021/12/23 14:27:51] root INFO:     lr : \n",
      "[2021/12/23 14:27:51] root INFO:         learning_rate : 0.0005\n",
      "[2021/12/23 14:27:51] root INFO:     name : Adam\n",
      "[2021/12/23 14:27:51] root INFO:     regularizer : \n",
      "[2021/12/23 14:27:51] root INFO:         factor : 0\n",
      "[2021/12/23 14:27:51] root INFO:         name : L2\n",
      "[2021/12/23 14:27:51] root INFO: PostProcess : \n",
      "[2021/12/23 14:27:51] root INFO:     name : CTCLabelDecode\n",
      "[2021/12/23 14:27:51] root INFO: Train : \n",
      "[2021/12/23 14:27:51] root INFO:     dataset : \n",
      "[2021/12/23 14:27:51] root INFO:         data_dir : ./train_data/ic15_data/\n",
      "[2021/12/23 14:27:51] root INFO:         label_file_list : ['./train_data/ic15_data/rec_gt_train.txt']\n",
      "[2021/12/23 14:27:51] root INFO:         name : SimpleDataSet\n",
      "[2021/12/23 14:27:51] root INFO:         transforms : \n",
      "[2021/12/23 14:27:51] root INFO:             DecodeImage : \n",
      "[2021/12/23 14:27:51] root INFO:                 channel_first : False\n",
      "[2021/12/23 14:27:51] root INFO:                 img_mode : BGR\n",
      "[2021/12/23 14:27:51] root INFO:             CTCLabelEncode : None\n",
      "[2021/12/23 14:27:51] root INFO:             RecResizeImg : \n",
      "[2021/12/23 14:27:51] root INFO:                 image_shape : [3, 32, 100]\n",
      "[2021/12/23 14:27:51] root INFO:             KeepKeys : \n",
      "[2021/12/23 14:27:51] root INFO:                 keep_keys : ['image', 'label', 'length']\n",
      "[2021/12/23 14:27:51] root INFO:     loader : \n",
      "[2021/12/23 14:27:51] root INFO:         batch_size_per_card : 256\n",
      "[2021/12/23 14:27:51] root INFO:         drop_last : True\n",
      "[2021/12/23 14:27:51] root INFO:         num_workers : 8\n",
      "[2021/12/23 14:27:51] root INFO:         shuffle : True\n",
      "[2021/12/23 14:27:51] root INFO:         use_shared_memory : False\n",
      "[2021/12/23 14:27:51] root INFO: train with paddle 2.1.2 and device CUDAPlace(0)\n",
      "[2021/12/23 14:27:51] root INFO: Initialize indexs of datasets:['./train_data/ic15_data/rec_gt_test.txt']\n",
      "W1223 14:27:51.861889  5192 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1\n",
      "W1223 14:27:51.865501  5192 device_context.cc:422] device: 0, cuDNN Version: 7.6.\n",
      "[2021/12/23 14:27:56] root INFO: resume from output/rec/ic15/best_accuracy\n",
      "[2021/12/23 14:27:56] root INFO: metric in ckpt ***************\n",
      "[2021/12/23 14:27:56] root INFO: acc:0.48531535869041886\n",
      "[2021/12/23 14:27:56] root INFO: norm_edit_dis:0.7895228681338454\n",
      "[2021/12/23 14:27:56] root INFO: fps:3266.1877400927865\n",
      "[2021/12/23 14:27:56] root INFO: best_epoch:24\n",
      "[2021/12/23 14:27:56] root INFO: start_epoch:25\n",
      "eval model:: 100%|████████████████████████████████| 9/9 [00:02<00:00,  3.32it/s]\n",
      "[2021/12/23 14:27:59] root INFO: metric eval ***************\n",
      "[2021/12/23 14:27:59] root INFO: acc:0.48531535869041886\n",
      "[2021/12/23 14:27:59] root INFO: norm_edit_dis:0.7895228681338454\n",
      "[2021/12/23 14:27:59] root INFO: fps:4491.015930181665\n"
     ]
    }
   ],
   "source": [
    "!python tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints=output/rec/ic15/best_accuracy \\\n",
    "        Global.character_dict_path=ppocr/utils/ic15_dict.txt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "评估后，可以看到训练模型在验证集上的精度。\n",
    "\n",
    "PaddleOCR支持训练和评估交替进行, 可在 `configs/rec/rec_icdar15_train.yml` 中修改 `eval_batch_step` 设置评估频率，默认每2000个iter评估一次。评估过程中默认将最佳acc模型，保存为 `output/rec/ic15/best_accuracy` 。\n",
    "\n",
    "如果验证集很大，测试将会比较耗时，建议减少评估次数，或训练完再进行评估。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 4.3 预测\n",
    "\n",
    "使用 PaddleOCR 训练好的模型，可以通过以下脚本进行快速预测。\n",
    "\n",
    "预测图片：\n",
    "![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.3/doc/imgs_words_en/word_19.png)\n",
    "\n",
    "默认预测图片存储在 `infer_img` 里，通过 `-o Global.checkpoints` 加载训练好的参数文件："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2021/12/23 14:29:19] root INFO: Architecture : \n",
      "[2021/12/23 14:29:19] root INFO:     Backbone : \n",
      "[2021/12/23 14:29:19] root INFO:         model_name : large\n",
      "[2021/12/23 14:29:19] root INFO:         name : MobileNetV3\n",
      "[2021/12/23 14:29:19] root INFO:         scale : 0.5\n",
      "[2021/12/23 14:29:19] root INFO:     Head : \n",
      "[2021/12/23 14:29:19] root INFO:         fc_decay : 0\n",
      "[2021/12/23 14:29:19] root INFO:         name : CTCHead\n",
      "[2021/12/23 14:29:19] root INFO:     Neck : \n",
      "[2021/12/23 14:29:19] root INFO:         encoder_type : rnn\n",
      "[2021/12/23 14:29:19] root INFO:         hidden_size : 96\n",
      "[2021/12/23 14:29:19] root INFO:         name : SequenceEncoder\n",
      "[2021/12/23 14:29:19] root INFO:     Transform : None\n",
      "[2021/12/23 14:29:19] root INFO:     algorithm : CRNN\n",
      "[2021/12/23 14:29:19] root INFO:     model_type : rec\n",
      "[2021/12/23 14:29:19] root INFO: Eval : \n",
      "[2021/12/23 14:29:19] root INFO:     dataset : \n",
      "[2021/12/23 14:29:19] root INFO:         data_dir : ./train_data/ic15_data\n",
      "[2021/12/23 14:29:19] root INFO:         label_file_list : ['./train_data/ic15_data/rec_gt_test.txt']\n",
      "[2021/12/23 14:29:19] root INFO:         name : SimpleDataSet\n",
      "[2021/12/23 14:29:19] root INFO:         transforms : \n",
      "[2021/12/23 14:29:19] root INFO:             DecodeImage : \n",
      "[2021/12/23 14:29:19] root INFO:                 channel_first : False\n",
      "[2021/12/23 14:29:19] root INFO:                 img_mode : BGR\n",
      "[2021/12/23 14:29:19] root INFO:             CTCLabelEncode : None\n",
      "[2021/12/23 14:29:19] root INFO:             RecResizeImg : \n",
      "[2021/12/23 14:29:19] root INFO:                 image_shape : [3, 32, 100]\n",
      "[2021/12/23 14:29:19] root INFO:             KeepKeys : \n",
      "[2021/12/23 14:29:19] root INFO:                 keep_keys : ['image', 'label', 'length']\n",
      "[2021/12/23 14:29:19] root INFO:     loader : \n",
      "[2021/12/23 14:29:19] root INFO:         batch_size_per_card : 256\n",
      "[2021/12/23 14:29:19] root INFO:         drop_last : False\n",
      "[2021/12/23 14:29:19] root INFO:         num_workers : 4\n",
      "[2021/12/23 14:29:19] root INFO:         shuffle : False\n",
      "[2021/12/23 14:29:19] root INFO:         use_shared_memory : False\n",
      "[2021/12/23 14:29:19] root INFO: Global : \n",
      "[2021/12/23 14:29:19] root INFO:     cal_metric_during_train : True\n",
      "[2021/12/23 14:29:19] root INFO:     character_dict_path : ppocr/utils/ic15_dict.txt\n",
      "[2021/12/23 14:29:19] root INFO:     character_type : EN\n",
      "[2021/12/23 14:29:19] root INFO:     checkpoints : output/rec/ic15/best_accuracy\n",
      "[2021/12/23 14:29:19] root INFO:     debug : False\n",
      "[2021/12/23 14:29:19] root INFO:     distributed : False\n",
      "[2021/12/23 14:29:19] root INFO:     epoch_num : 72\n",
      "[2021/12/23 14:29:19] root INFO:     eval_batch_step : [0, 2000]\n",
      "[2021/12/23 14:29:19] root INFO:     infer_img : doc/imgs_words_en/word_19.png\n",
      "[2021/12/23 14:29:19] root INFO:     infer_mode : False\n",
      "[2021/12/23 14:29:19] root INFO:     log_smooth_window : 20\n",
      "[2021/12/23 14:29:19] root INFO:     max_text_length : 25\n",
      "[2021/12/23 14:29:19] root INFO:     pretrained_model : None\n",
      "[2021/12/23 14:29:19] root INFO:     print_batch_step : 10\n",
      "[2021/12/23 14:29:19] root INFO:     save_epoch_step : 3\n",
      "[2021/12/23 14:29:19] root INFO:     save_inference_dir : ./\n",
      "[2021/12/23 14:29:19] root INFO:     save_model_dir : ./output/rec/ic15/\n",
      "[2021/12/23 14:29:19] root INFO:     save_res_path : ./output/rec/predicts_ic15.txt\n",
      "[2021/12/23 14:29:19] root INFO:     use_gpu : True\n",
      "[2021/12/23 14:29:19] root INFO:     use_space_char : False\n",
      "[2021/12/23 14:29:19] root INFO:     use_visualdl : False\n",
      "[2021/12/23 14:29:19] root INFO: Loss : \n",
      "[2021/12/23 14:29:19] root INFO:     name : CTCLoss\n",
      "[2021/12/23 14:29:19] root INFO: Metric : \n",
      "[2021/12/23 14:29:19] root INFO:     main_indicator : acc\n",
      "[2021/12/23 14:29:19] root INFO:     name : RecMetric\n",
      "[2021/12/23 14:29:19] root INFO: Optimizer : \n",
      "[2021/12/23 14:29:19] root INFO:     beta1 : 0.9\n",
      "[2021/12/23 14:29:19] root INFO:     beta2 : 0.999\n",
      "[2021/12/23 14:29:19] root INFO:     lr : \n",
      "[2021/12/23 14:29:19] root INFO:         learning_rate : 0.0005\n",
      "[2021/12/23 14:29:19] root INFO:     name : Adam\n",
      "[2021/12/23 14:29:19] root INFO:     regularizer : \n",
      "[2021/12/23 14:29:19] root INFO:         factor : 0\n",
      "[2021/12/23 14:29:19] root INFO:         name : L2\n",
      "[2021/12/23 14:29:19] root INFO: PostProcess : \n",
      "[2021/12/23 14:29:19] root INFO:     name : CTCLabelDecode\n",
      "[2021/12/23 14:29:19] root INFO: Train : \n",
      "[2021/12/23 14:29:19] root INFO:     dataset : \n",
      "[2021/12/23 14:29:19] root INFO:         data_dir : ./train_data/ic15_data/\n",
      "[2021/12/23 14:29:19] root INFO:         label_file_list : ['./train_data/ic15_data/rec_gt_train.txt']\n",
      "[2021/12/23 14:29:19] root INFO:         name : SimpleDataSet\n",
      "[2021/12/23 14:29:19] root INFO:         transforms : \n",
      "[2021/12/23 14:29:19] root INFO:             DecodeImage : \n",
      "[2021/12/23 14:29:19] root INFO:                 channel_first : False\n",
      "[2021/12/23 14:29:19] root INFO:                 img_mode : BGR\n",
      "[2021/12/23 14:29:19] root INFO:             CTCLabelEncode : None\n",
      "[2021/12/23 14:29:19] root INFO:             RecResizeImg : \n",
      "[2021/12/23 14:29:19] root INFO:                 image_shape : [3, 32, 100]\n",
      "[2021/12/23 14:29:19] root INFO:             KeepKeys : \n",
      "[2021/12/23 14:29:19] root INFO:                 keep_keys : ['image', 'label', 'length']\n",
      "[2021/12/23 14:29:19] root INFO:     loader : \n",
      "[2021/12/23 14:29:19] root INFO:         batch_size_per_card : 256\n",
      "[2021/12/23 14:29:19] root INFO:         drop_last : True\n",
      "[2021/12/23 14:29:19] root INFO:         num_workers : 8\n",
      "[2021/12/23 14:29:19] root INFO:         shuffle : True\n",
      "[2021/12/23 14:29:19] root INFO:         use_shared_memory : False\n",
      "[2021/12/23 14:29:19] root INFO: train with paddle 2.1.2 and device CUDAPlace(0)\n",
      "W1223 14:29:19.803710  5290 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1\n",
      "W1223 14:29:19.807695  5290 device_context.cc:422] device: 0, cuDNN Version: 7.6.\n",
      "[2021/12/23 14:29:25] root INFO: resume from output/rec/ic15/best_accuracy\n",
      "[2021/12/23 14:29:25] root INFO: infer_img: doc/imgs_words_en/word_19.png\n",
      "pred idx: Tensor(shape=[1, 25], dtype=int64, place=CUDAPlace(0), stop_gradient=True,\n",
      "       [[29, 0 , 0 , 0 , 22, 0 , 0 , 0 , 25, 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 33]])\n",
      "[2021/12/23 14:29:25] root INFO: \t result: slow\t0.8795223\n",
      "[2021/12/23 14:29:25] root INFO: success!\n"
     ]
    }
   ],
   "source": [
    "!python tools/infer_rec.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints=output/rec/ic15/best_accuracy Global.character_dict_path=ppocr/utils/ic15_dict.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "得到输入图像的预测结果：\n",
    "\n",
    "```\n",
    "infer_img: doc/imgs_words_en/word_19.png\n",
    "        result: slow\t0.8795223\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 作业\n",
    "\n",
    "**【题目1】**\n",
    "\n",
    "可视化出 PaddleOCR 中的实现的[数据增强](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.4/ppocr/data/imaug/rec_img_aug.py)结果：noise、jitter, 并用语言解释效果。\n",
    "\n",
    "可选测试图片：\n",
    "\n",
    "![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.4/doc/imgs_words/ch/word_1.jpg)\n",
    "\n",
    "![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.4/doc/imgs_words/ch/word_2.jpg)\n",
    "\n",
    "![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.4/doc/imgs_words/ch/word_3.jpg)\n",
    "\n",
    "\n",
    "**【题目2】**\n",
    "\n",
    "更换 configs/rec/rec_icdar15_train.yml 配置中的 backbone 为 PaddleOCR 中的 [ResNet34_vd](https://github.com/PaddlePaddle/PaddleOCR/blob/6ee301be36eb54d91dc437842f754593dce13967/ppocr/modeling/backbones/rec_resnet_vd.py#L176)，当输入图片shape为（3，32，100）时，Head 层最终输出的特征尺寸是多少？\n",
    "\n",
    "\n",
    "**【题目3】**\n",
    "\n",
    "下载10W中文数据集[rec_data_lesson_demo](https://paddleocr.bj.bcebos.com/dataset/rec_data_lesson_demo.tar)，修改 configs/rec/rec_icdar15_train.yml  配置文件训练一个识别模型，提供训练log。\n",
    "\n",
    "可加载预训练模型： https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar \n",
    "\n",
    "\n",
    "## 总结\n",
    "\n",
    "至此，一个基于CRNN的文本识别任务就全部完成了，更多功能和代码可以参考 [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)。\n",
    "\n",
    "如果对项目任何问题或者疑问，欢迎在评论区留言提出"
   ]
  }
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