diff --git a/tools/ModelConverter/README_cn.md b/tools/ModelConverter/README_cn.md
new file mode 100644
index 0000000000000000000000000000000000000000..45e393ea7c7d974e6c0936b9d3d4efba3b71f148
--- /dev/null
+++ b/tools/ModelConverter/README_cn.md
@@ -0,0 +1,71 @@
+# PaddleJS Model Converter
+
+PaddleJS Model Converter 是适用于 PaddleJS 的模型转换工具，其作用是将 PaddlePaddle 模型（或称为 fluid 模型）转化为 PaddleJS 模型这种浏览器友好的格式，以供在 PaddleJS 和浏览器中加载预测使用。此外，PaddleJS Model Converter 还提供了强大的模型优化能力，帮助开发者对模型结构进行优化，提高运行时性能。
+
+## 1. 使用教程
+
+### 1.1. 环境搭建
+#### Python 版本确认
+确认运行平台的 Python 环境与版本是否满足要求，若使用 Python3 ，则可能需要将后续命令中的 `python` 换成 `python3`：
+- Python3： 3.5.1+ / 3.6 / 3.7
+- Python2： 2.7.15+
+
+#### 安装虚拟环境
+*由于开发环境可能安装了多个版本的 Python，相关依赖包可能存在不同的版本，为避免产生冲突，**强烈建议**使用 Python 虚拟环境执行转换工具所需的各项命令，以免产生各种问题。若不使用虚拟环境或已安装虚拟环境，可跳过该步骤。*
+
+以 Anaconda 为例：
+前往 [Anaconda](https://www.anaconda.com/) 主页，选择对应平台、Python 版本的 Anaconda 按照官方提示，进行安装；
+
+安装完毕后，在命令行执行以下命令，创建Python 虚拟环境：
+``` bash
+conda create --name <your_env_name>
+```
+
+执行以下命令，切换至虚拟环境
+``` bash
+# Linux 或 macOS下请执行
+source activate <your_env_name>
+
+# Windows 下请执行
+activate <your_env_name>
+```
+
+#### 安装依赖
+- 如果`不需要`使用优化模型的能力，执行命令：
+``` bash
+python -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
+```
+- 如果`需要`使用优化模型的能力，执行命令：
+``` bash
+python -m pip install paddlepaddle paddlelite==2.6.0 -i https://mirror.baidu.com/pypi/simple
+```
+
+### 1.2. 快速上手
+- 如果待转换的 fluid 模型为`合并参数文件`，即一个模型对应一个参数文件：
+``` bash
+python convertToPaddleJSModel.py --modelPath=<fluid_model_file_path> --paramPath=<fluid_param_file_path> --outputDir=<paddlejs_model_directory>
+```
+- 如果待转换的 fluid 模型为`分片参数文件`，即一个模型文件对应多个参数文件：
+``` bash
+# 注意，使用这种方式调用转换器，需要保证 inputDir 中，模型文件名为'__model__'
+python convertToPaddleJSModel.py --inputDir=<fluid_model_directory> --outputDir=<paddlejs_model_directory>
+````
+模型转换器将生成以下两种类型的文件以供 PaddleJS 使用：
+
+- model.json (模型结构与参数清单)
+- chunk_\*.dat (二进制参数文件集合)
+
+## 2. 详细文档
+参数 |  描述
+:-: | :-: 
+--inputDir | fluid 模型所在目录，当且仅当使用分片参数文件时使用该参数，将忽略 `modelPath` 和 `paramPath` 参数，且模型文件名必须为`__model__`
+--modelPath | fluid 模型文件所在路径，使用合并参数文件时使用该参数
+--paramPath | fluid 参数文件所在路径，使用合并参数文件时使用该参数
+--outputDir | `必要参数`， paddleJS 模型输出路径
+--optimize | 是否进行模型优化， `0` 为关闭优化，`1` 为开启优化（需安装 PaddleLite ），默认关闭优化
+--logModelInfo | 是否打印模型结构信息， `0` 为不打印， `1` 为打印，默认不打印
+--sliceDataSize | 分片输出 PaddleJS 参数文件时，每片文件的大小，单位：KB，默认 4096
+
+## 3. 其他信息
+若需要转换的模型为 `TensorFlow/Caffe/ONNX` 格式，可使用 PaddlePaddle 项目下的 `X2Paddle`工具，将其他格式的模型转为 fluid 模型后，再使用本工具转化为 PaddleJS 模型。
+详细请参考 [X2Paddle 项目](https://github.com/PaddlePaddle/X2Paddle)
\ No newline at end of file
diff --git a/tools/ModelConverter/convertModel.py b/tools/ModelConverter/convertModel.py
new file mode 100644
index 0000000000000000000000000000000000000000..deb4570fdac3db5c96f939f3c8cdad8a110c7316
--- /dev/null
+++ b/tools/ModelConverter/convertModel.py
@@ -0,0 +1,267 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+
+import json
+import collections
+import math
+import sys
+import os
+import struct
+import argparse
+import shutil
+import stat
+import traceback
+import numpy as np
+import paddle.fluid as fluid
+
+
+# 输入模型所在目录
+modelDir = None
+# 输入模型名
+modelName = None
+# 输入参数名，当且仅当所有模型参数被保存在一个单独的二进制文件中，它才需要被指定，若为分片模型，请设置为None
+paramsName = None
+# 是否打印模型信息
+enableLogModelInfo = False
+# 输出模型目录
+outputDir = None
+# 分片文件大小，单位：KB
+sliceDataSize = 4 * 1024
+# paddlepaddle运行程序实例
+program = None
+# 存放模型结构
+modelInfo = {"vars": [], "ops": []}
+# 存放参数数值（未排序）
+paramValuesDict = {}
+
+def logModel(info):
+    """ 打印信息 """
+    if enableLogModelInfo:
+        print(info)
+
+def sortDict(oldDict, reverse=False):
+    """ 对字典进行排序，返回有序字典，默认升序 """
+    # 获得排序后的key list
+    keys = sorted(oldDict.keys(), reverse=reverse)
+    orderDict = collections.OrderedDict()
+    # 遍历 key 列表
+    for key in keys:
+        orderDict[key] = oldDict[key]
+    return orderDict
+
+def dumpModelToJsonFile():
+    """ 导出模型数据到json文件 """
+    print("Dumping model structure to json file...")
+    if not os.path.exists(outputDir):
+        os.makedirs(outputDir)
+    outputModelPath = os.path.join(outputDir, "model.json")
+    with open(outputModelPath, 'w') as outputFile:
+        json.dump(modelInfo, outputFile, indent=4, separators=(", ", ": "), sort_keys=True)
+    print("Dumping model structure to json file successfully")
+
+def sliceDataToBinaryFile(paramValueList):
+    """ 将参数数据分片输出到文件，默认分片策略为按4M分片 """
+    totalParamValuesCount = len(paramValueList)
+    countPerSlice = int(sliceDataSize * 1024 / 4)
+
+    if not os.path.exists(outputDir):
+        os.makedirs(outputDir)
+    currentChunkIndex = 0
+    currentParamDataIndex = 0
+
+    while currentParamDataIndex < totalParamValuesCount - 1:
+        remainCount = totalParamValuesCount - currentParamDataIndex
+        if remainCount < countPerSlice:
+            countPerSlice = remainCount
+        chunkPath = os.path.join(outputDir, 'chunk_%s.dat' % (currentChunkIndex + 1))
+        file = open(chunkPath, 'wb')
+        for i in paramValueList[currentParamDataIndex : currentParamDataIndex + countPerSlice]:
+            byte = struct.pack('f', float(i))
+            file.write(byte)
+        file.close()
+        currentParamDataIndex = currentParamDataIndex + countPerSlice
+        currentChunkIndex = currentChunkIndex + 1
+        print("Output No." + str(currentChunkIndex)+ " binary file, remain " + str(totalParamValuesCount - currentParamDataIndex) + " param values.")
+    print("Slicing data to binary files successfully. (" + str(currentChunkIndex)+ " output files and " + str(currentParamDataIndex) + " param values)")
+
+def reorderParamsValue():
+    """ 对参数文件中的数值，按照variable.name字母序排序，返回排序后组合完成的value list """
+    paramValuesOrderDict = sortDict(paramValuesDict)
+    paramValues = []
+    for value in paramValuesOrderDict.values():
+        paramValues += value
+    return paramValues
+
+def mapToPaddleJSTypeName(fluidOPName):
+    """ 处理fluid的OP type与PaddleJS的OP type不对应情况 """
+    if fluidOPName == "batch_norm":
+        return "batchnorm"
+    return fluidOPName  
+
+def organizeModelVariableInfo():
+    """ 组织参数信息 """
+    print("Organizing model variables info...")
+    index = 0
+    # 存放var信息（未排序）
+    varInfoDict = {}
+    # 获取program中所有的var，遍历并获取所有未排序的var信息和参数数值
+    vars = list(program.list_vars())
+    for v in vars:
+        # 跳过feed和fetch
+        if "feed" == v.name:
+            continue
+        if "fetch" == v.name:
+            continue
+
+        varShape = list(v.shape)
+
+        # FIXME:start paddlejs 不支持shape中为-1，这里需要手动过滤一下，支持了以后可以删除
+        varShapeExcludeNegativeOne = []
+        for s in varShape:
+            if s == -1:
+                continue
+            varShapeExcludeNegativeOne.append(s)
+        varShape = varShapeExcludeNegativeOne
+        # FIXME:end
+
+        # 存放variable信息，在dump成json时排序
+        varInfo = {}
+        varInfo["shape"] = varShape
+        # 数据是否是持久化数据，如tensor为持久化数据，op的output不是持久化数据
+        # 只输出持久化数据，paddlejs中也仅读取持久化数据
+        varInfo["persistable"] = v.persistable
+        varInfoDict[v.name] = varInfo
+       
+        logModel("[Var index:" + str(index) + " name:" + v.name + "]")
+        jsonDumpsIndentStr = json.dumps(varInfo, indent=2)
+        logModel(jsonDumpsIndentStr)
+        logModel("")
+        index += 1
+
+        # persistable数据存入paramValuesDict，等待排序
+        if v.persistable:
+            data = np.array(fluid.global_scope().find_var(v.name).get_tensor()).flatten().tolist()
+            paramValuesDict[v.name] = data
+
+    # 对var信息dict，按照key（var名）进行字母顺序排序
+    varInfoOrderDict = sortDict(varInfoDict)
+
+    # 将var信息按照顺序，添加到model info的vars中
+    for key, value in varInfoOrderDict.items():
+        value["name"] = key
+        modelInfo["vars"].append(value)
+    print("Organizing model variables info successfully.")
+
+def organizeModelOpInfo():
+    """ 组织模型OP结构信息 """
+    print("Organizing model operators info...")
+    ops = program.current_block().ops
+    feedOutputName = None
+    index = 0
+    for op in ops:
+        opInfo = {}
+
+        # 获取OP type，需要映射到PaddleJS的名字
+        opInfo["type"] = mapToPaddleJSTypeName(op.type)
+        
+        # 获取OP input
+        inputs = {}
+        for name in op.input_names:
+            value = op.input(name)
+            if len(value) <= 0:
+                continue
+            if value[0] == feedOutputName:
+                # FIXME:workaround,PaddleJSfeed 输入必须是image，且为单输入，这里修改feed后面的OP的input为image，建立前后关联
+                inputs[name] = ["image"]
+            else:
+                inputs[name] = value
+        opInfo["inputs"] = inputs
+        
+        # 获取OP output
+        outputs = {}
+        for name in op.output_names:
+            value = op.output(name)
+            if len(value) <= 0:
+                continue
+            if op.type == "feed":
+                # FIXME:workaround,PaddleJSfeed 输入必须是image，且为单输入，这里保存原始的输出名，以便映射
+                feedOutputName = value[0]
+                outputs[name] = ["image"]
+            else:
+                outputs[name] = value
+        opInfo["outputs"] = outputs
+
+        # 获取OP attribute    
+        attrs = {}
+        for name in op.attr_names:
+            # 过滤不需要的参数
+            if name in ["op_callstack", 'col', 'op_role', 'op_namescope', 'op_role_var']:
+                continue
+            value = op.attr(name)
+            attrs[name] = value
+        opInfo["attrs"] = attrs
+
+        # 存入modelInfo 
+        modelInfo["ops"].append(opInfo)
+        logModel("[OP index:" + str(index) + " type:" + op.type + "]")
+        jsonDumpsIndentStr = json.dumps(opInfo, indent=2)
+        logModel(jsonDumpsIndentStr)
+        logModel("")
+        index += 1
+    print("Organizing model operators info successfully.")
+
+def convertToPaddleJSModel():
+    """ 转换fluid modle为paddleJS model """
+    # 初始化fluid运行环境和配置
+    exe = fluid.Executor(fluid.CPUPlace())
+    result = fluid.io.load_inference_model(dirname=modelDir, executor=exe, model_filename=modelName, params_filename=paramsName)
+    global program
+    program = result[0]
+    
+    # 获取program中所有的op，按op顺序加入到model info
+    organizeModelOpInfo()
+
+    # 获取program中所有的var，按照字母顺序加入到model info，同时读取参数数值
+    organizeModelVariableInfo()
+
+    # 导出模型文件到json
+    dumpModelToJsonFile()
+
+    # 对参数数值dict，按照key（参数名）进行字母顺序排序，并组合到一起
+    paramValues = reorderParamsValue()
+    
+    # 导出分片参数文件
+    sliceDataToBinaryFile(paramValues) 
+
+if __name__ == "__main__":
+    try:
+        p = argparse.ArgumentParser(description='模型转换参数解析')
+        p.add_argument('--inputDir', help='fluid模型所在目录。当且仅当使用分片参数文件时使用该参数。将过滤modelPath和paramsPath参数，且模型文件名必须为`__model__`', required=False)
+        p.add_argument('--modelPath', help='fluid模型文件所在路径，使用合并参数文件时使用该参数', required=False)
+        p.add_argument('--paramPath', help='fluid参数文件所在路径，使用合并参数文件时使用该参数', required=False)
+        p.add_argument("--outputDir", help='paddleJS模型输出路径，必要参数', required=True)
+        p.add_argument("--logModelInfo", type=int, default=0, help='是否输出模型结构信息，非必要参数，0为不输出，1为输出，默认不输出', required=False)
+        p.add_argument("--sliceDataSize", type=int, default=4096, help='分片输出参数文件时，每片文件的大小，单位：KB，非必要参数，默认4096KB', required=False)
+        
+        args = p.parse_args()
+        modelDir = args.inputDir
+        modelPath = args.modelPath
+        paramPath = args.paramPath
+        if not modelDir:
+            modelDir, modelName = os.path.split(modelPath)
+            paramDir, paramsName = os.path.split(paramPath)
+            if paramDir != modelDir:
+                print("\033[31mModel and param file should be put in a same directory!\033[0m")
+                raise Exception()
+        outputDir = args.outputDir
+        sliceDataSize = args.sliceDataSize
+
+        if args.logModelInfo == 1:
+            enableLogModelInfo = True
+
+        convertToPaddleJSModel()
+
+    except Exception as identifier:
+        print("\033[31mA fetal error occured. Failed to convert model.\033[0m")
+        print(traceback.format_exc())
+        pass
\ No newline at end of file
diff --git a/tools/ModelConverter/convertToPaddleJSModel.py b/tools/ModelConverter/convertToPaddleJSModel.py
new file mode 100644
index 0000000000000000000000000000000000000000..3cca9b3759226b9bb3aad7424133fe83b98d27da
--- /dev/null
+++ b/tools/ModelConverter/convertToPaddleJSModel.py
@@ -0,0 +1,107 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+
+import sys
+import os
+import argparse
+import shutil
+import stat
+import traceback
+
+def cleanTempModel(optimizedModelTempDir):
+    """ 清理opt优化完的临时模型文件 """
+    if os.path.exists(optimizedModelTempDir):
+        print("Cleaning optimized temporary model...")
+        shutil.rmtree(optimizedModelTempDir, onerror=grantWritePermission)
+
+def grantWritePermission(func, path, execinfo):
+    """ 文件授权 """
+    os.chmod(path, stat.S_IWRITE)
+    func(path)
+
+
+if __name__ == "__main__":
+    """
+    Example:
+    'python convertToPaddleJSModel.py --modelPath=../infer_model/MobileNetV2/__model__ --paramPath=../infer_model/MobileNetV2/params --outputDir=../jsmodel --optimize=1'
+    """
+    try:
+        p = argparse.ArgumentParser(description='转化为PaddleJS模型参数解析')
+        p.add_argument('--inputDir', help='fluid模型所在目录。当且仅当使用分片参数文件时使用该参数。将过滤modelPath和paramsPath参数，且模型文件名必须为`__model__`', required=False)
+        p.add_argument('--modelPath', help='fluid模型文件所在路径，使用合并参数文件时使用该参数', required=False)
+        p.add_argument('--paramPath', help='fluid参数文件所在路径，使用合并参数文件时使用该参数', required=False)
+        p.add_argument("--outputDir", help='paddleJS模型输出路径，必要参数', required=True)
+        p.add_argument("--optimize", type=int, default=0, help='是否进行模型优化，非必要参数，0为关闭优化，1为开启优化，默认关闭优化', required=False)
+        p.add_argument("--logModelInfo", type=int, default=0, help='是否输出模型结构信息，非必要参数，0为不输出，1为输出，默认不输出', required=False)
+        p.add_argument("--sliceDataSize", type=int, default=4096, help='分片输出参数文件时，每片文件的大小，单位：KB，非必要参数，默认4096KB', required=False)
+        
+        args = p.parse_args()
+
+        # TODO: 由于PaddleLite和PaddlePaddle存在包冲突，因此将整个模型转换工具拆成两个python文件，由一个入口python文件通过命令行调用
+        optimizeCmd = " optimizeModel.py"
+        convertCmd = " convertModel.py"
+
+        inputDir = args.inputDir
+        modelPath = args.modelPath
+        paramPath = args.paramPath
+        outputDir = args.outputDir
+        enableOptimization = args.optimize
+        enableLogModelInfo = args.logModelInfo
+        sliceDataSize = args.sliceDataSize
+
+        optimizedModelTempDir = None
+        if enableOptimization == 1:
+            optimizedModelTempDir = os.path.join(outputDir, "optimize")
+            if inputDir:
+                optimizeCmd = optimizeCmd + " --inputDir=" + inputDir
+                convertCmd = convertCmd + " --inputDir=" + optimizedModelTempDir
+            else:
+                optimizeCmd = optimizeCmd + " --modelPath=" + modelPath + " --paramPath=" + paramPath
+                # optimizeModelPath, modelName = os.path.split(modelPath)
+                # optimizeParamPath, paramName = os.path.split(paramPath)
+                optimizeModelPath = os.path.join(optimizedModelTempDir, "model")
+                optimizeParamPath = os.path.join(optimizedModelTempDir, "params")
+                convertCmd = convertCmd + " --modelPath=" + optimizeModelPath + " --paramPath=" + optimizeParamPath
+            optimizeCmd = optimizeCmd + " --outputDir=" + optimizedModelTempDir
+        else:
+            if inputDir:
+                convertCmd = convertCmd + " --inputDir=" + inputDir
+            else:
+                convertCmd = convertCmd + " --modelPath=" + modelPath + " --paramPath=" + paramPath
+
+        convertCmd = convertCmd + " --outputDir=" + outputDir + " --sliceDataSize=" + str(sliceDataSize) + " --logModelInfo=" + str(args.logModelInfo)
+
+        print("============Convert Model Args=============")
+        if inputDir:
+            print("inputDir: " + inputDir)
+        else:
+            print("modelPath: " + modelPath)
+            print("paramPath: " + paramPath)
+        print("outputDir: " + outputDir)
+        print("enableOptimizeModel: " + str(enableOptimization))
+        print("enableLogModelInfo: " + str(enableLogModelInfo))
+        print("sliceDataSize:" + str(sliceDataSize))
+
+        pythonCmd = "python"
+
+        print("Starting...")
+        if enableOptimization:
+            print("Optimizing model...")
+            os.system(pythonCmd + optimizeCmd)
+            print("\033[32m\nOptimizing model successfully.\033[0m")
+        else:
+            print("\033[33mYou choosed not to optimize model, consequently, optimizing model is skiped.\033[0m")
+
+        print("Converting model...")
+        os.system(pythonCmd + convertCmd)
+        print("\033[32mConverting model successfully.\033[0m")
+        
+        if enableOptimization:
+            cleanTempModel(optimizedModelTempDir)
+            print("Temporary files has been deleted successfully.")
+        print("\033[32m============ALL DONE============\033[0m")
+
+    except Exception as identifier:
+        print("\033[31mA fetal error occured. Failed to convert model.\033[0m")
+        print(traceback.format_exc())
+        pass
\ No newline at end of file
diff --git a/tools/ModelConverter/optimizeModel.py b/tools/ModelConverter/optimizeModel.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c7a1bf3b7ed9afcc64a7a064df2eedf7a6b75d5
--- /dev/null
+++ b/tools/ModelConverter/optimizeModel.py
@@ -0,0 +1,81 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+
+import collections
+import argparse
+import traceback
+import paddlelite.lite as lite
+
+def optimizeModel(inputDir, modelPath, paramPath, outputDir):
+    """ 使用opt python接口执行模型优化 """
+    opt = lite.Opt()
+    if inputDir:
+        # 分片参数文件优化
+        opt.set_model_dir(inputDir)
+    else:
+        # 合并参数文件优化
+        opt.set_model_file(modelPath)
+        opt.set_param_file(paramPath)
+
+    opt.set_valid_places("arm")
+    opt.set_model_type("protobuf")
+    opt.set_optimize_out(outputDir)
+    
+    optimize_passes = [
+        "lite_conv_elementwise_fuse_pass",
+        "lite_conv_bn_fuse_pass",
+        "lite_conv_elementwise_fuse_pass",
+        "lite_conv_activation_fuse_pass",
+        "lite_var_conv_2d_activation_fuse_pass",
+        "lite_fc_fuse_pass",
+        "lite_shuffle_channel_fuse_pass",
+        "lite_transpose_softmax_transpose_fuse_pass",
+        "lite_interpolate_fuse_pass",
+        "identity_scale_eliminate_pass",
+        "elementwise_mul_constant_eliminate_pass",
+        "lite_sequence_pool_concat_fuse_pass",
+        "lite_elementwise_add_activation_fuse_pass",
+        "static_kernel_pick_pass",
+        "variable_place_inference_pass",
+        "argument_type_display_pass",
+        "type_target_cast_pass",
+        "variable_place_inference_pass",
+        "argument_type_display_pass",
+        "io_copy_kernel_pick_pass",
+        "argument_type_display_pass",
+        "variable_place_inference_pass",
+        "argument_type_display_pass",
+        "type_precision_cast_pass",
+        "variable_place_inference_pass",
+        "argument_type_display_pass",
+        "type_layout_cast_pass",
+        "argument_type_display_pass",
+        "variable_place_inference_pass",
+        "argument_type_display_pass",
+        "runtime_context_assign_pass",
+        "argument_type_display_pass"
+    ]
+    opt.set_passes_internal(optimize_passes)
+    opt.run()
+
+
+if __name__ == "__main__":
+    try:
+        p = argparse.ArgumentParser('模型优化参数解析')
+        p.add_argument('--inputDir', help='fluid模型所在目录。当且仅当使用分片参数文件时使用该参数。将过滤modelPath和paramsPath参数，且模型文件名必须为`__model__`', required=False)
+        p.add_argument('--modelPath', help='fluid模型文件所在路径，使用合并参数文件时使用该参数', required=False)
+        p.add_argument('--paramPath', help='fluid参数文件所在路径，使用合并参数文件时使用该参数', required=False)
+        p.add_argument("--outputDir", help='优化后fluid模型目录，必要参数', required=True)
+       
+        args = p.parse_args()
+        inputDir = args.inputDir
+        modelPath = args.modelPath
+        paramPath = args.paramPath
+        outputDir = args.outputDir
+
+        optimizeModel(inputDir, modelPath, paramPath, outputDir)
+            
+    except Exception as identifier:
+        print("\033[31mA fetal error occured. Failed to optimize model.\033[0m")
+        print(traceback.format_exc())
+        pass
\ No newline at end of file
diff --git a/tools/toWebModel.py b/tools/toWebModel.py
deleted file mode 100644
index 2cfb10cceedbd3e2a270aec199bb7f8509baccb5..0000000000000000000000000000000000000000
--- a/tools/toWebModel.py
+++ /dev/null
@@ -1,313 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: UTF-8 -*-
-
-import json
-import paddle.fluid as fluid
-import paddle
-import numpy as np
-import collections
-import math
-import sys as sys
-import os
-import struct
-
-#常量控制
-#抽样打印数据数量
-logDataCount = 50
-
-# 输入模型所在目录
-modelDir = "humanseg/"
-# 输入模型名
-modelName = "model"
-# 输入参数名，当且仅当所有模型参数被保存在一个单独的二进制文件中，它才需要被指定，若为分片模型，请设置为None
-paramsName = None
-# 模型feed shape
-inputShape = (1, 3, 192, 192)
-# 输入数据
-inputData = np.full(inputShape, 1, "float32")
-# 输出模型目录
-outputDir = "../dist/model/humanseg/"
-# 权重分片扩展名
-extensionName = ".dat"
-# 输出各var数据
-outputVarData = False
-
-# 确认fluid的版本号
-print(paddle.__version__)
-
-# 采样输出list数据，采样的个数logDataCount为常量
-def stridePrint1(data):
-    dataCount = len(data)
-    stride = math.floor(dataCount / logDataCount)
-    if stride == 0:
-        stride = 1
-    nums = []
-    # outputCount = logDataCount
-    # if dataCount < logDataCount:
-    #     outputCount = dataCount
-    # for i in range(outputCount):
-    #     # nums.append(str(i) + ": " + str(data[i]))
-    #     nums.append(data[i])
-    
-    for i in range(0, logDataCount):
-        item = data[i * stride]
-        nums.append(str(i * stride) + ": " + str(item))
-    print(nums)
-
-def stridePrint(tensor):
-    length = len(tensor)
-#    if length < 3000:
-#        print(tensor)
-#        return
-    size = 20
-    stride = math.floor(length / size)
-    if stride == 0:
-        stride = 1
-    size = math.floor(length / stride)
-    nums = []
-    for i in range(0, size):
-        item = tensor[i * stride]
-        nums.append(str(i * stride) + ": " + str(item))
-    print(nums)
-
-
-
-
-# 对字典进行排序，返回有序字典，默认升序
-def sortDict(oldDict, reverse = False):
-    # 获得排序后的key list
-    keys = sorted(oldDict.keys(), reverse = reverse)
-    orderDict = collections.OrderedDict()
-    # 遍历 key 列表
-    for key in keys:
-        orderDict[key] = oldDict[key]
-    return orderDict
-
-
-# 将权重数据分片输出到文件，默认分片策略为按4M分片
-def sliceDataToBinaryFile(weightValueList, sliceMethod = 0):
-    # TODO: 分片这里不太对，待修改
-    totalWeightCount = len(weightValueList)
-    countPerSlice = 0
-    # sliceCount = 0
-    if sliceMethod == 0:
-        # 分片策略 0:按4M分片
-        countPerSlice = int(4 * 1024 * 1024 / 4)
-        # sliceCount = math.ceil(totalWeightCount / countPerSlice)
-    else:
-        # 分片策略 1:按<=4M等分
-        # TODO: 待实现
-        countPerSlice = 0
-        # sliceCount = 0
-
-    if not os.path.exists(outputDir):
-        os.makedirs(outputDir)
-    currentChunkIndex = 0
-    currentWeightIndex = 0
-
-    while currentWeightIndex < totalWeightCount - 1:
-        remainCount = totalWeightCount - currentWeightIndex
-        if remainCount < countPerSlice:
-            countPerSlice = remainCount
-        chunkPath = outputDir + 'chunk_%s' % (currentChunkIndex + 1) + extensionName
-        file = open(chunkPath, 'wb')
-        for i in weightValueList[currentWeightIndex : currentWeightIndex + countPerSlice]:
-            byte = struct.pack('f', float(i))
-            file.write(byte)
-        file.close()
-        currentWeightIndex = currentWeightIndex + countPerSlice
-        currentChunkIndex = currentChunkIndex + 1
-        # for debug
-        print("第" + str(currentChunkIndex + 1) + "片权重输出完毕，输出个数：" + str(countPerSlice) + " 剩余个数:" + str(totalWeightCount - currentWeightIndex))
-
-    # for debug
-    print("========权重输出完毕，共" + str(currentWeightIndex) + "个数据，" + str(currentChunkIndex) + "个分片文件" + "========")
-
-# 处理fluid的OP type与PaddleJS的OP type不对应情况
-def mapToPaddleJSTypeName(fluidOPName):
-    if fluidOPName == "batch_norm":
-        return "batchnorm"
-    return fluidOPName
-
-
-# 将shape扩充为4维
-def padToFourDimShape(shape):
-    fourDimShape = []
-    if len(shape) == 4:
-        fourDimShape = shape
-    elif len(shape) < 4:
-        for i in range(0, 4 - len(shape)):
-            fourDimShape.append(1)
-        fourDimShape = fourDimShape + shape
-    else:
-        return []
-    return fourDimShape
-
-
-# for debug，将NCHW排布的数据转为NHWC排布的数据
-def convertNCHW2NHWC(data, shape):
-    fourDimShape = padToFourDimShape(shape)
-    N = fourDimShape[0]
-    C = fourDimShape[1]
-    H = fourDimShape[2]
-    W = fourDimShape[3]
-    print(fourDimShape)
-    HXW = H * W
-    CXHXW = C * H * W
-    index = 0
-    nhwcData = []
-    for n in range(0, N):
-        for h in range(0, H):
-            for w in range(0, W):
-                for c in range(0, C):
-                    nhwcData.append(data[n * CXHXW + c * HXW + h * W + w])
-                    index = index + 1
-    return nhwcData
-
-# for debug 输出特定varName对应的数据
-def writeTempOutputData(name):
-    # FIXME:待完善
-    return
-    dataList = np.array(fluid.global_scope().find_var(name).get_tensor()).flatten().tolist()
-    path = '/Users/bluebird/baidu/fluid_tools/check-temp/filter.txt'
-    if os.path.exists(path):
-        os.remove()
-        file = open(path,'a')
-        for a in range(0, len(dataList)):
-            file.write(str(dataList[a]))
-            file.write(",")
-        file.close()
-
-def convertToPaddleJSModel():
-    # 1. 初始化fluid运行环境和配置
-    exe = fluid.Executor(fluid.CPUPlace())
-    [prog, feed_target_names, fetch_targets] = fluid.io.load_inference_model(dirname=modelDir, executor=exe, model_filename=modelName, params_filename=paramsName)
-    out = exe.run(prog, feed={feed_target_names[0]: inputData}, fetch_list=fetch_targets, return_numpy=False)
-    print(out)
-    
-    index = 0
-    # 存放模型结构
-    modelInfo = {"vars": [], "ops": []}
-    # 存放var信息（未排序）
-    varInfoDict = {}
-    # 存放权重数值（未排序）
-    weightValueDict = {}
-
-    # 2. 获取program中所有的var，遍历并获取所有未排序的var信息和权重数值
-    vars = list(prog.list_vars())
-    for v in vars:
-        # 跳过feed和fetch
-        if "feed" == v.name:
-            continue
-        if "fetch" == v.name:
-            continue
-
-        print("Var index:" + str(index) + " name:" + v.name)
-        print(v)
-        index += 1
-
-        varShape = list(v.shape)
-
-        # FIXME:start paddlejs 不支持shape中为-1，这里需要手动过滤一下，支持了以后可以删除
-        varShapeExcludeNegativeOne = []
-        for s in varShape:
-            if s == -1:
-                continue
-            varShapeExcludeNegativeOne.append(s)
-        varShape = varShapeExcludeNegativeOne
-        # FIXME:end
-
-        # 存放variable信息，在dump成json时排序
-        varInfo = {}
-        varInfo["shape"] = varShape
-        # 数据是否是持久化数据，如weight为持久化数据，op的output不是持久化数据
-        # 只输出持久化数据，paddlejs中也仅读取持久化数据
-        varInfo["persistable"] = v.persistable
-        varInfoDict[v.name] = varInfo
-        
-        # for debug，输出var变量
-        if outputVarData:
-            writeTempOutputData(v.name)
-
-        # persistable数据存入weightDict，等待排序
-        if v.persistable:
-            data = np.array(fluid.global_scope().find_var(v.name).get_tensor()).flatten().tolist()
-            weightValueDict[v.name] = data
-
-    # 3. 对var信息dict，按照key（var名）进行字母顺序排序
-    varInfoOrderDict = sortDict(varInfoDict)
-
-    # 4. 将var信息按照顺序，添加到model info的vars中
-    for key, value in varInfoOrderDict.items():
-        value["name"] = key
-        modelInfo["vars"].append(value)
-    
-    # 5. 对权重数值dict，按照key（权重名）进行字母顺序排序，并组合到一起
-    weightValueOrderDict = sortDict(weightValueDict)
-    weightValues = []
-    for key, value in weightValueOrderDict.items():
-        weightValues += value
-    
-    # 6. 分片输出权重
-    sliceDataToBinaryFile(weightValues)
-
-    # 7. 获取program中所有的op，按op顺序加入到model info
-    ops = prog.current_block().ops
-    feedOutputName = None
-    for op in ops:
-        opInfo = {}
-
-        # 获取OP type，需要映射到PaddleJS的名字
-        opInfo["type"] = mapToPaddleJSTypeName(op.type)
-        
-        # 获取OP input
-        inputs = {}
-        for name in op.input_names:
-            value = op.input(name)
-            if len(value) <= 0:
-                continue
-            if value[0] == feedOutputName:
-                # FIXME:workaround,PaddleJSfeed 输入必须是image，且为单输入
-                # 这里修改feed后面的OP的input为image，建立前后关联
-                # 这里可能会有问题
-                inputs[name] = ["image"]
-            else:
-                inputs[name] = value
-        opInfo["inputs"] = inputs
-        
-        # 获取OP output
-        outputs = {}
-        for name in op.output_names:
-            value = op.output(name)
-            if len(value) <= 0:
-                continue
-            if op.type == "feed":
-                # FIXME:workaround,PaddleJSfeed 输入必须是image，且为单输入
-                # 这里可能会有问题
-                feedOutputName = value[0]
-                outputs[name] = ["image"]
-            else:
-                outputs[name] = value
-        opInfo["outputs"] = outputs
-
-        # 获取OP attribute    
-        attrs = {}
-        for name in op.attr_names:
-            # 过滤不需要的参数
-            if name in ["op_callstack", 'col', 'op_role', 'op_namescope', 'op_role_var']:
-                continue
-            value = op.attr(name)
-            attrs[name] = value
-        opInfo["attrs"] = attrs
-
-        # 存入modelInfo 
-        modelInfo["ops"].append(opInfo)
-
-    # 8. 模型信息按照key字母顺序导出到json
-    outputModelPath = outputDir + "model.json"
-    with open(outputModelPath, 'w') as outputFile:
-        json.dump(modelInfo, outputFile, indent = 4, separators=(", ", ": "), sort_keys = True)
-
-    print("========模型结构输出完毕========")
-
-convertToPaddleJSModel()