updated CntkOnnxExport to include pretrained model example (#7)

mx-iao · prasanthpul · commit 7abff361cf6e · 2017-12-08T09:20:48.000-08:00
diff --git a/tutorials/CntkOnnxExport.ipynb b/tutorials/CntkOnnxExport.ipynb
@@ -25,15 +25,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To export to ONNX, simply make sure you have CNTK 2.3 or higher installed. <br>\n",
+    "To export to ONNX, simply make sure you have CNTK 2.3.1 or higher installed. <br>\n",
     "Follow CNTK installation instructions __[here](https://docs.microsoft.com/en-us/cognitive-toolkit/Setup-CNTK-on-your-machine)__."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Exporting in Python"
+    "## API Usage"
    ]
   },
   {
@@ -43,6 +43,13 @@
     "To save a CNTK model to the ONNX format, specify the ONNX format in the format parameter of the save function."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Using Python API **  "
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -51,7 +58,7 @@
     "import cntk as C\n",
     "\n",
     "x = C.input_variable(<input shape>)\n",
-    "z = create_model(x)\n",
+    "z = create_model(x) #your create model function\n",
     "z.save(<path of where to save your ONNX model>, format=C.ModelFormat.ONNX)\n",
     "```"
    ]
@@ -60,7 +67,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Exporting in C# #"
+    "** Exporting in C# **"
    ]
   },
   {
@@ -69,10 +76,94 @@
    "source": [
     "```csharp\n",
     "var x = CNTKLib.InputVariable(<specify input variable parameters>);\n",
-    "Function z = CreateModel(x);\n",
+    "Function z = CreateModel(x); //your create model function\n",
     "z.Save(<path of where to save your ONNX model>, ModelFormat.ONNX);\n",
     "```\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Trying it out with ResNet-20"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's go through an example of exporting a pretrained CNTK model to ONNX."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 1: Prepare a CNTK model to export"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this tutorial, we will be using a pretrained ResNet-20 model (trained on the CIFAR-10 dataset) from the collection of pretrained CNTK models found [here](https://github.com/Microsoft/CNTK/blob/master/PretrainedModels/Image.md). Download the model to your working directory. (Note that not all of the models found here are exportable to the ONNX format yet.)  \n",
+    "Download link: https://www.cntk.ai/Models/CNTK_Pretrained/ResNet20_CIFAR10_CNTK.model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 2: Load the model into CNTK"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import cntk as C"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "model_path = \"ResNet20_CIFAR10_CNTK.model\"\n",
+    "z = C.Function.load(model_path, device=C.device.cpu())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3: Export the model to ONNX"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, export the CNTK model by saving it out to the ONNX format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "z.save(\"model.onnx\", format=C.ModelFormat.ONNX)"
+   ]
   }
  ],
  "metadata": {
