OCR案例更新

paddle2.0_docs/image_ocr/OCR.ipynb

@@ -22,7 +22,9 @@
     "**数据展示**\n",
     "<p align=\"center\">\n",
     "<img src=\"https://ai-studio-static-online.cdn.bcebos.com/865dd55861e24cfaa601d9f87655776c1458d099b487449f946da9b3138fc700\" width=\"400\"><br/>\n",
-    "</p>"
+    "</p>  \n",
+    "\n",
+    "点此[快速获取本节数据集](https://aistudio.baidu.com/aistudio/datasetdetail/57285)，待数据集下载完毕后可使用`!unzip OCR_Dataset.zip -d data/`命令或熟悉的解压软件进行解压，待数据准备工作完成后修改本文“训练准备”中的`DATA_PATH = 解压后数据集路径`。"
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -126,16 +128,13 @@
     "\n",
     "CTC相关论文：[Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neu](http://people.idsia.ch/~santiago/papers/icml2006.pdf) \n",
     "<p align=\"center\">\n",
-    "<img src=\"https://ai-studio-static-online.cdn.bcebos.com/50cf1fc38f6b40e596acf71dc43333ff49dcaafb5a9f484b8aeee2db2c08ca67\" width=\"800\"><br/>\n",
+    "<img src=\"https://ai-studio-static-online.cdn.bcebos.com/f9458cedbb4441d682f15fefd3f3cae5e49d499bcf0a4bbdb976dfdff5a2e656\" width=\"800\"><br/>\n",
     "</p>\n",
     "\n",
-    "网络部分，因本篇采用数据集较为简单且图像尺寸较小并不适合较深层次网络。若在对尺寸较大的图像进行模型构建，可以考虑使用更深层次网络/注意力机制来完成。当然也可以通过目标检测形式先检出文本位置，然后进行OCR部分模型构建。\n",
+    "网络部分，因本篇采用数据集较为简单且图像尺寸较小并不适合较深层次网络。若在对尺寸较大的图像进行模型构建，可以考虑使用更深层次网络/注意力机制来完成。当然也可以通过目标检测形式先检出文本位置，然后进行OCR部分模型构建。(下方样例来源[PaddleOCR](v))\n",
     "\n",
     "<p align=\"center\">\n",
-    "<img src=\"https://ai-studio-static-online.cdn.bcebos.com/6e0665ddfe6a46e1b658da870cd4043f1d50e5f4dc2746018c710c58d2e0c18c\" width=\"400\"><br/>\n",
-    "  \n",
-    "  \n",
-    "<a href=\"https://github.com/PaddlePaddle/PaddleOCR\">PaddleOCR效果图</a>\n",
+    "<img src=\"https://ai-studio-static-online.cdn.bcebos.com/6e0665ddfe6a46e1b658da870cd4043f1d50e5f4dc2746018c710c58d2e0c18c\" width=\"400\"></br>\n",
     "</p>"
    ]
   },
@@ -164,16 +163,16 @@
     "        self.is_infer = is_infer\n",
     "\n",
     "        # 定义一层3x3卷积+BatchNorm\n",
-    "        self.conv1 = paddle.nn.Conv2d(in_channels=IMAGE_SHAPE_C,\n",
+    "        self.conv1 = paddle.nn.Conv2D(in_channels=IMAGE_SHAPE_C,\n",
     "                                  out_channels=32,\n",
     "                                  kernel_size=3)\n",
-    "        self.bn1 = paddle.nn.BatchNorm2d(32)\n",
+    "        self.bn1 = paddle.nn.BatchNorm2D(32)\n",
     "        # 定义一层步长为2的3x3卷积进行下采样+BatchNorm\n",
-    "        self.conv2 = paddle.nn.Conv2d(in_channels=32,\n",
+    "        self.conv2 = paddle.nn.Conv2D(in_channels=32,\n",
     "                                  out_channels=64,\n",
     "                                  kernel_size=3,\n",
     "                                  stride=2)\n",
-    "        self.bn2 = paddle.nn.BatchNorm2d(64)\n",
+    "        self.bn2 = paddle.nn.BatchNorm2D(64)\n",
     "        # 定义一层1x1卷积压缩通道数，输出通道数设置为比LABEL_MAX_LEN稍大的定值可获取更优效果，当然也可设置为LABEL_MAX_LEN\n",
     "        self.conv3 = paddle.nn.Conv2d(in_channels=64,\n",
     "                                  out_channels=LABEL_MAX_LEN + 4,\n",
@@ -215,6 +214,8 @@
     "        if self.is_infer:\n",
     "            # 输出层 - Shape = (Batch Size, Max label len, Prob) \n",
     "            x = paddle.nn.functional.softmax(x)\n",
+    "            # 转换为标签\n",
+    "            x = paddle.tensor.argmax(x, axis=-1)\n",
     "        return x"
    ]
   },
@@ -286,8 +287,8 @@
     "        super().__init__()\n",
     "\n",
     "    def forward(self, ipt, label):\n",
-    "        input_lengths = paddle.tensor.fill_constant([BATCH_SIZE, 1], \"int64\", LABEL_MAX_LEN + 4)\n",
-    "        label_lengths = paddle.tensor.fill_constant([BATCH_SIZE, 1], \"int64\", LABEL_MAX_LEN)\n",
+    "        input_lengths = paddle.tensor.creation.fill_constant([BATCH_SIZE, 1], \"int64\", LABEL_MAX_LEN + 4)\n",
+    "        label_lengths = paddle.tensor.creation.fill_constant([BATCH_SIZE, 1], \"int64\", LABEL_MAX_LEN)\n",
     "        # 按文档要求进行转换dim顺序\n",
     "        ipt = paddle.tensor.transpose(ipt, [1, 0, 2])\n",
     "        # 计算loss\n",
@@ -452,7 +453,7 @@
    },
    "outputs": [],
    "source": [
-    "# 待预测目录\n",
+    "# 待预测目录 - 可在测试数据集中挑出\b3张图像放在该目录中进行推理\n",
     "INFER_DATA_PATH = \"./sample_img\"\n",
     "# 训练后存档点路径 - 10代表使用第10个存档点\n",
     "CHECKPOINT_PATH = \"./output/10\"\n",
@@ -505,7 +506,7 @@
   {
    "source": [
     "## 开始预测\n",
-    "> 飞桨2.0 CTC Decoder 相关API正在迁移中，本节暂时使用[第三方解码器](https://github.com/awni/speech/blob/072bcf9ff510d814fbfcaad43b2883ecf8f60806/speech/models/ctc_decoder.py)进行解码。"
+    "> 飞桨2.0 CTC Decoder 相关API正在迁移中，本节暂时使用简易版解码器。"
    ],
    "cell_type": "markdown",
    "metadata": {
@@ -533,7 +534,22 @@
     }
    ],
    "source": [
-    "from ctc import decode\n",
+    "# 编写简易版解码器\n",
+    "def ctc_decode(text, blank=10):\n",
+    "    \"\"\"\n",
+    "    简易CTC解码器\n",
+    "    :param text: 待解码数据\n",
+    "    :param blank: 分隔符索引值\n",
+    "    :return: 解码后数据\n",
+    "    \"\"\"\n",
+    "    result = []\n",
+    "    cache_idx = -1\n",
+    "    for char in text:\n",
+    "        if char != blank and char != cache_idx:\n",
+    "            result.append(char)\n",
+    "        cache_idx = char\n",
+    "    return result\n",
+    "\n",
     "\n",
     "# 实例化预测模型\n",
     "model = paddle.Model(Net(is_infer=True), inputs=input_define)\n",
@@ -547,10 +563,10 @@
     "img_names = infer_reader.get_names()\n",
     "results = model.predict(infer_reader, batch_size=BATCH_SIZE)\n",
     "index = 0\n",
-    "for result in results[0]:\n",
-    "    for prob in result:\n",
-    "        out, _ = decode(prob, blank=10)\n",
-    "        print(f\"文件名：{img_names[index]}，预测结果为：{out}\")\n",
+    "for text_batch in results[0]:\n",
+    "    for prob in text_batch:\n",
+    "        out = ctc_decode(prob, blank=10)\n",
+    "        print(f\"文件名：{img_names[index]}，推理结果为：{out}\")\n",
     "        index += 1"
    ]
   }