From 22bbd94459b1d7aae9b40170df9a43e48642bcbc Mon Sep 17 00:00:00 2001 From: Roope Astala Date: Mon, 24 Sep 2018 14:19:04 -0400 Subject: [PATCH] Updates to onnx notebooks --- onnx/onnx-inference-emotion-recognition.ipynb | 161 +++++++++--------- onnx/onnx-inference-mnist.ipynb | 160 ++++------------- 2 files changed, 119 insertions(+), 202 deletions(-) diff --git a/onnx/onnx-inference-emotion-recognition.ipynb b/onnx/onnx-inference-emotion-recognition.ipynb index 8f956b1a..d5870fd7 100644 --- a/onnx/onnx-inference-emotion-recognition.ipynb +++ b/onnx/onnx-inference-emotion-recognition.ipynb @@ -12,13 +12,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 02. Facial Expression Recognition using ONNX Runtime GPU on AzureML\n", + "# Facial Expression Recognition using ONNX Runtime on AzureML\n", "\n", "This example shows how to deploy an image classification neural network using the Facial Expression Recognition ([FER](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data)) dataset and Open Neural Network eXchange format ([ONNX](http://aka.ms/onnxdocarticle)) on the Azure Machine Learning platform. This tutorial will show you how to deploy a FER+ model from the [ONNX model zoo](https://github.com/onnx/models), use it to make predictions using ONNX Runtime Inference, and deploy it as a web service in Azure.\n", "\n", "Throughout this tutorial, we will be referring to ONNX, a neural network exchange format used to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools (CNTK, PyTorch, Caffe, MXNet, TensorFlow) and choose the combination that is best for them. ONNX is developed and supported by a community of partners including Microsoft AI, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai) and [open source files](https://github.com/onnx).\n", "\n", - "[ONNX Runtime](https://aka.ms/onnxruntime) is the runtime engine that enables evaluation of trained machine learning (Traditional ML and Deep Learning) models with high performance and low resource utilization.\n", + "[ONNX Runtime](https://aka.ms/onnxruntime-python) is the runtime engine that enables evaluation of trained machine learning (Traditional ML and Deep Learning) models with high performance and low resource utilization. We use the CPU version of ONNX Runtime in this tutorial, but will soon be releasing an additional tutorial for deploying this model using ONNX Runtime GPU.\n", "\n", "#### Tutorial Objectives:\n", "\n", @@ -38,10 +38,10 @@ "\n", "\n", "### 2. Install additional packages needed for this Notebook\n", - "You need to install the popular plotting library `matplotlib` and the `onnx` library in the conda environment where Azure Maching Learning SDK is installed.\n", + "You need to install the popular plotting library `matplotlib`, the image manipulation library `PIL`, and the `onnx` library in the conda environment where Azure Maching Learning SDK is installed.\n", "\n", "```sh\n", - "(myenv) $ pip install matplotlib onnx\n", + "(myenv) $ pip install matplotlib onnx Pillow\n", "```\n", "\n", "### 3. Download sample data and pre-trained ONNX model from ONNX Model Zoo.\n", @@ -85,7 +85,7 @@ "from azureml.core import Workspace\n", "\n", "ws = Workspace.from_config()\n", - "print(ws.name, ws.location, ws.resource_group, ws.location, sep = '\\n')" + "print(ws.name, ws.location, ws.resource_group, sep = '\\n')" ] }, { @@ -188,19 +188,14 @@ "### Model Description\n", "\n", "The FER+ model from the ONNX Model Zoo is summarized by the graphic below. You can see the entire workflow of our pre-trained model in the following image from Barsoum et. al's paper [\"Training Deep Networks for Facial Expression Recognition\n", - "with Crowd-Sourced Label Distribution\"](https://arxiv.org/pdf/1608.01041.pdf), with our (64,64) input images and our output probabilities for each of the labels." + "with Crowd-Sourced Label Distribution\"](https://arxiv.org/pdf/1608.01041.pdf), with our (64 x 64) input images and our output probabilities for each of the labels." ] }, { - "attachments": { - "image.png": { - "image/png": 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- } - }, "cell_type": "markdown", "metadata": {}, "source": [ - "![image.png](attachment:image.png)" + "![](https://raw.githubusercontent.com/vinitra/FERPlus/master/emotion_model_img.png)" ] }, { @@ -239,9 +234,11 @@ "import time\n", "\n", "def init():\n", - " global session\n", + " global session, input_name, output_name\n", " model = Model.get_model_path(model_name = 'onnx_emotion')\n", " session = onnxruntime.InferenceSession(model, None)\n", + " input_name = session.get_inputs()[0].name\n", + " output_name = session.get_outputs()[0].name \n", " \n", "def run(input_data):\n", " '''Purpose: evaluate test input in Azure Cloud using onnxruntime.\n", @@ -250,14 +247,13 @@ "\n", " try:\n", " # load in our data, convert to readable format\n", - " start = time.time()\n", " data = np.array(json.loads(input_data)['data']).astype('float32')\n", - "\n", - " r = session.run([\"Plus214_Output_0\"], {\"Input3\": data})[0]\n", - " result = emotion_map(postprocess(r[0]))\n", + " start = time.time()\n", + " r = session.run([output_name], {input_name : data})\n", " end = time.time()\n", - " result_dict = {\"result\": np.array(result).tolist(),\n", - " \"time\": np.array(end - start).tolist()}\n", + " result = emotion_map(postprocess(r[0]))\n", + " result_dict = {\"result\": result,\n", + " \"time_in_sec\": [end - start]}\n", " except Exception as e:\n", " result_dict = {\"error\": str(e)}\n", " \n", @@ -306,7 +302,7 @@ "myenv = CondaDependencies()\n", "myenv.add_pip_package(\"numpy\")\n", "myenv.add_pip_package(\"azureml-core\")\n", - "myenv.add_pip_package(\"onnxruntime-gpu\")\n", + "myenv.add_pip_package(\"onnxruntime\")\n", "\n", "\n", "with open(\"myenv.yml\",\"w\") as f:\n", @@ -330,15 +326,11 @@ "source": [ "from azureml.core.image import ContainerImage\n", "\n", - "# enable_gpu = True to install CUDA 9.1 and cuDNN 7.0\n", - "\n", "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n", " runtime = \"python\",\n", " conda_file = \"myenv.yml\",\n", " description = \"test\",\n", - " tags = {\"demo\": \"onnx\"},\n", - " enable_gpu = True\n", - " )\n", + " tags = {\"demo\": \"onnx\"})\n", "\n", "\n", "image = ContainerImage.create(name = \"onnxtest\",\n", @@ -388,14 +380,7 @@ "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n", " memory_gb = 1, \n", " tags = {'demo': 'onnx'}, \n", - " description = 'ONNX for facial emotion recognition model')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following cell will likely take a few minutes to run as well." + " description = 'ONNX for emotion recognition model')" ] }, { @@ -406,7 +391,7 @@ "source": [ "from azureml.core.webservice import Webservice\n", "\n", - "aci_service_name = 'onnx-emotion-demo'\n", + "aci_service_name = 'onnx-demo-emotion'\n", "print(\"Service\", aci_service_name)\n", "\n", "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n", @@ -418,6 +403,13 @@ "print(aci_service.state)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following cell will likely take a few minutes to run as well." + ] + }, { "cell_type": "code", "execution_count": null, @@ -460,17 +452,10 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "def preprocess(img):\n", - " \"\"\"Convert image to the write format to be passed into the model\"\"\"\n", - " input_shape = (1, 64, 64)\n", - " img = np.reshape(img, input_shape)\n", - " img = np.expand_dims(img, axis=0)\n", - " return img" + "### Load Test Data" ] }, { @@ -490,6 +475,8 @@ "import json\n", "import os\n", "\n", + "from score import emotion_map, softmax, postprocess\n", + "\n", "test_inputs = []\n", "test_outputs = []\n", "\n", @@ -505,14 +492,15 @@ " with open(input_test_data, 'rb') as f:\n", " tensor.ParseFromString(f.read())\n", " \n", - " input_data = preprocess(numpy_helper.to_array(tensor))\n", + " input_data = numpy_helper.to_array(tensor)\n", " test_inputs.append(input_data)\n", " \n", " with open(output_test_data, 'rb') as f:\n", " tensor.ParseFromString(f.read())\n", " \n", " output_data = numpy_helper.to_array(tensor)\n", - " test_outputs.append(output_data)" + " output_processed = emotion_map(postprocess(output_data))[0]\n", + " test_outputs.append(output_processed)" ] }, { @@ -524,7 +512,7 @@ }, "source": [ "### Show some sample images\n", - "We use `matplotlib` to plot 3 images from the dataset with their labels over them." + "We use `matplotlib` to plot 3 test images from the model zoo with their labels over them." ] }, { @@ -543,8 +531,8 @@ " plt.subplot(1, 8, test_image+1)\n", " plt.axhline('')\n", " plt.axvline('')\n", - " plt.text(x = 10, y = -10, s = test_outputs[test_image][0], fontsize = 18)\n", - " plt.imshow(test_inputs[test_image].reshape(64, 64))\n", + " plt.text(x = 10, y = -10, s = test_outputs[test_image], fontsize = 18)\n", + " plt.imshow(test_inputs[test_image].reshape(64, 64), cmap = plt.cm.Greys)\n", "plt.show()" ] }, @@ -579,14 +567,14 @@ " # predict using the deployed model\n", " r = json.loads(aci_service.run(input_data))\n", " \n", - " if len(r) == 1:\n", + " if \"error\" in r:\n", " print(r['error'])\n", " break\n", " \n", - " result = r['result']\n", - " time_ms = np.round(r['time'] * 1000, 2)\n", + " result = r['result'][0][0]\n", + " time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n", " \n", - " ground_truth = int(np.argmax(test_outputs[i]))\n", + " ground_truth = test_outputs[i]\n", " \n", " # compare actual value vs. the predicted values:\n", " plt.subplot(1, 8, i+2)\n", @@ -598,11 +586,11 @@ " clr_map = plt.cm.gray if ground_truth != result else plt.cm.Greys\n", "\n", " # ground truth labels are in blue\n", - " plt.text(x = 10, y = -30, s = ground_truth, fontsize = 18, color = 'blue')\n", + " plt.text(x = 10, y = -70, s = ground_truth, fontsize = 18, color = 'blue')\n", " \n", " # predictions are in black if correct, red if incorrect\n", - " plt.text(x = 10, y = -20, s = result, fontsize = 18, color = font_color)\n", - " plt.text(x = 5, y = -10, s = str(time_ms) + ' ms', fontsize = 14, color = font_color)\n", + " plt.text(x = 10, y = -45, s = result, fontsize = 18, color = font_color)\n", + " plt.text(x = 5, y = -22, s = str(time_ms) + ' ms', fontsize = 14, color = font_color)\n", "\n", " \n", " plt.imshow(test_inputs[i].reshape(64, 64), cmap = clr_map)\n", @@ -617,6 +605,23 @@ "### Try classifying your own images!" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from PIL import Image\n", + "\n", + "def preprocess(image_path):\n", + " input_shape = (1, 1, 64, 64)\n", + " img = Image.open(image_path)\n", + " img = img.resize((64, 64), Image.ANTIALIAS)\n", + " img_data = np.array(img)\n", + " img_data = np.resize(img_data, input_shape)\n", + " return img_data" + ] + }, { "cell_type": "code", "execution_count": null, @@ -624,22 +629,19 @@ "outputs": [], "source": [ "# Replace the following string with your own path/test image\n", - "# Make sure the dimensions are 28 * 28 pixels\n", + "# Make sure your image is square and the dimensions are equal (i.e. 100 * 100 pixels or 28 * 28 pixels)\n", "\n", "# Any PNG or JPG image file should work\n", "# Make sure to include the entire path with // instead of /\n", "\n", - "# e.g. your_test_image = \"C://Users//vinitra.swamy//Pictures//emotion_test_images//img_1.jpg\"\n", + "# e.g. your_test_image = \"C://Users//vinitra.swamy//Pictures//emotion_test_images//img_1.png\"\n", "\n", "your_test_image = \"\"\n", "\n", - "import matplotlib.image as mpimg\n", - "\n", "if your_test_image != \"\":\n", - " img = mpimg.imread(your_test_image)\n", + " img = preprocess(your_test_image)\n", " plt.subplot(1,3,1)\n", - " plt.imshow(img, cmap = plt.cm.Greys)\n", - " img = img.reshape(1, 1, 64, 64)\n", + " plt.imshow(img.reshape((64,64)), cmap = plt.cm.gray)\n", "else:\n", " img = None" ] @@ -657,21 +659,21 @@ "\n", " try:\n", " r = json.loads(aci_service.run(input_data))\n", - " result = r['result']\n", - " time_ms = np.round(r['time'] * 1000, 2)\n", + " result = r['result'][0][0]\n", + " time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n", " except Exception as e:\n", - " print(json.loads(r)['error'])\n", + " print(str(e))\n", "\n", " plt.figure(figsize = (16, 6))\n", - " plt.subplot(1, 15,1)\n", + " plt.subplot(1,8,1)\n", " plt.axhline('')\n", " plt.axvline('')\n", - " plt.text(x = -100, y = -20, s = \"Model prediction: \", fontsize = 14)\n", - " plt.text(x = -100, y = -10, s = \"Inference time: \", fontsize = 14)\n", - " plt.text(x = 0, y = -20, s = str(result), fontsize = 14)\n", - " plt.text(x = 0, y = -10, s = str(time_ms) + \" ms\", fontsize = 14)\n", - " plt.text(x = -100, y = 14, s = \"Input image: \", fontsize = 14)\n", - " plt.imshow(img.reshape(28, 28), cmap = plt.cm.Greys) " + " plt.text(x = -10, y = -35, s = \"Model prediction: \", fontsize = 14)\n", + " plt.text(x = -10, y = -20, s = \"Inference time: \", fontsize = 14)\n", + " plt.text(x = 100, y = -35, s = str(result), fontsize = 14)\n", + " plt.text(x = 100, y = -20, s = str(time_ms) + \" ms\", fontsize = 14)\n", + " plt.text(x = -10, y = -8, s = \"Input image: \", fontsize = 14)\n", + " plt.imshow(img.reshape(64, 64), cmap = plt.cm.gray) " ] }, { @@ -693,22 +695,23 @@ "\n", "Congratulations!\n", "\n", - "In this tutorial, you have managed to:\n", - "- familiarize yourself with the ONNX standard, ONNX Runtime inference, and the pretrained models in the ONNX model zoo\n", - "- understand a state-of-the-art convolutional neural net image classification model (FER+ in ONNX) and deploy it in the Azure ML cloud\n", - "- ensure that your deep learning model is working correctly (in the cloud) on test data, and check it against some of your own!\n", + "In this tutorial, you have:\n", + "- familiarized yourself with ONNX Runtime inference and the pretrained models in the ONNX model zoo\n", + "- understood a state-of-the-art convolutional neural net image classification model (FER+ in ONNX) and deployed it in the Azure ML cloud\n", + "- ensured that your deep learning model is working perfectly (in the cloud) on test data, and checked it against some of your own!\n", "\n", "Next steps:\n", "- If you have not already, check out another interesting ONNX/AML application that lets you set up a state-of-the-art [handwritten image classification model (MNIST)](https://github.com/Azure/MachineLearningNotebooks/tree/master/onnx/onnx-inference-mnist.ipynb) in the cloud! This tutorial deploys a pre-trained ONNX Computer Vision model for handwritten digit classification in an Azure ML virtual machine.\n", + "- Keep an eye out for an updated version of this tutorial that uses ONNX Runtime GPU.\n", "- Contribute to our [open source ONNX repository on github](http://github.com/onnx/onnx) and/or add to our [ONNX model zoo](http://github.com/onnx/models)" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3", "language": "python", - "name": "python36" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -720,7 +723,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.6.5" }, "msauthor": "vinitra.swamy" }, diff --git a/onnx/onnx-inference-mnist.ipynb b/onnx/onnx-inference-mnist.ipynb index 61c663c1..c49d5700 100644 --- a/onnx/onnx-inference-mnist.ipynb +++ b/onnx/onnx-inference-mnist.ipynb @@ -12,13 +12,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 01. Handwritten Digit Classification (MNIST) using ONNX Runtime on AzureML\n", + "# Handwritten Digit Classification (MNIST) using ONNX Runtime on AzureML\n", "\n", "This example shows how to deploy an image classification neural network using the Modified National Institute of Standards and Technology ([MNIST](http://yann.lecun.com/exdb/mnist/)) dataset and Open Neural Network eXchange format ([ONNX](http://aka.ms/onnxdocarticle)) on the Azure Machine Learning platform. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing number from 0 to 9. This tutorial will show you how to deploy a MNIST model from the [ONNX model zoo](https://github.com/onnx/models), use it to make predictions using ONNX Runtime Inference, and deploy it as a web service in Azure.\n", "\n", "Throughout this tutorial, we will be referring to ONNX, a neural network exchange format used to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools (CNTK, PyTorch, Caffe, MXNet, TensorFlow) and choose the combination that is best for them. ONNX is developed and supported by a community of partners including Microsoft AI, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai) and [open source files](https://github.com/onnx).\n", "\n", - "[ONNX Runtime](https://aka.ms/onnxruntime) is the runtime engine that enables evaluation of trained machine learning (Traditional ML and Deep Learning) models with high performance and low resource utilization.\n", + "[ONNX Runtime](https://aka.ms/onnxruntime-python) is the runtime engine that enables evaluation of trained machine learning (Traditional ML and Deep Learning) models with high performance and low resource utilization.\n", "\n", "#### Tutorial Objectives:\n", "\n", @@ -36,12 +36,11 @@ "### 1. Install Azure ML SDK and create a new workspace\n", "Please follow [00.configuration.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) notebook.\n", "\n", - "\n", "### 2. Install additional packages needed for this Notebook\n", - "You need to install the popular plotting library `matplotlib` and the `onnx` library in the conda environment where Azure Maching Learning SDK is installed.\n", + "You need to install the popular plotting library `matplotlib`, the image manipulation library `opencv`, and the `onnx` library in the conda environment where Azure Maching Learning SDK is installed.\n", "\n", "```sh\n", - "(myenv) $ pip install matplotlib onnx\n", + "(myenv) $ pip install matplotlib onnx opencv-python\n", "```\n", "\n", "### 3. Download sample data and pre-trained ONNX model from ONNX Model Zoo.\n", @@ -222,9 +221,11 @@ "\n", "\n", "def init():\n", - " global session\n", + " global session, input_name, output_name\n", " model = Model.get_model_path(model_name = 'mnist_1')\n", " session = onnxruntime.InferenceSession(model, None)\n", + " input_name = session.get_inputs()[0].name\n", + " output_name = session.get_outputs()[0].name \n", " \n", "def run(input_data):\n", " '''Purpose: evaluate test input in Azure Cloud using onnxruntime.\n", @@ -233,14 +234,14 @@ "\n", " try:\n", " # load in our data, convert to readable format\n", - " start = time.time()\n", " data = np.array(json.loads(input_data)['data']).astype('float32')\n", "\n", - " r = session.run([\"Plus214_Output_0\"], {\"Input3\": data})[0]\n", - " result = choose_class(r[0])\n", + " start = time.time()\n", + " r = session.run([output_name], {input_name: data})[0]\n", " end = time.time()\n", - " result_dict = {\"result\": np.array(result).tolist(),\n", - " \"time\": np.array(end - start).tolist()}\n", + " result = choose_class(r[0])\n", + " result_dict = {\"result\": [result],\n", + " \"time_in_sec\": [end - start]}\n", " except Exception as e:\n", " result_dict = {\"error\": str(e)}\n", " \n", @@ -304,8 +305,7 @@ " runtime = \"python\",\n", " conda_file = \"myenv.yml\",\n", " description = \"test\",\n", - " tags = {\"demo\": \"onnx\"} \n", - " )\n", + " tags = {\"demo\": \"onnx\"}) )\n", "\n", "\n", "image = ContainerImage.create(name = \"onnxtest\",\n", @@ -533,12 +533,12 @@ " # predict using the deployed model\n", " r = json.loads(aci_service.run(input_data))\n", " \n", - " if len(r) == 1:\n", + " if \"error\" in r:\n", " print(r['error'])\n", " break\n", " \n", - " result = r['result']\n", - " time_ms = np.round(r['time'] * 1000, 2)\n", + " result = r['result'][0]\n", + " time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n", " \n", " ground_truth = int(np.argmax(test_outputs[i]))\n", " \n", @@ -570,7 +570,7 @@ "source": [ "### Try classifying your own images!\n", "\n", - "Create your own 28 pixel by 28 pixel handwritten image and pass it into the model." + "Create your own handwritten image and pass it into the model." ] }, { @@ -580,18 +580,22 @@ "outputs": [], "source": [ "# Preprocessing functions\n", + "import cv2\n", "\n", "def rgb2gray(rgb):\n", " \"\"\"Convert the input image into grayscale\"\"\"\n", " return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n", "\n", + "def resize_img(img):\n", + " img = cv2.resize(img, dsize=(28, 28), interpolation=cv2.INTER_AREA)\n", + " img.resize((1, 1, 28, 28))\n", + " return img\n", + "\n", "def preprocess(img):\n", " \"\"\"Resize input images and convert them to grayscale.\"\"\"\n", - " if img.shape[0] != 28:\n", - " print(\"Input image size is not 28 * 28 pixels. Please resize and try again.\")\n", " grayscale = rgb2gray(img)\n", - " grayscale.resize((1, 1, 28, 28))\n", - " return grayscale" + " processed_img = resize_img(grayscale)\n", + " return processed_img" ] }, { @@ -601,7 +605,7 @@ "outputs": [], "source": [ "# Replace this string with your own path/test image\n", - "# Make sure the dimensions are 28 * 28 pixels\n", + "# Make sure your image is square and the dimensions are equal (i.e. 100 * 100 pixels or 28 * 28 pixels)\n", "\n", "# Any PNG or JPG image file should work\n", "# Make sure to include the entire path with // instead of /\n", @@ -636,10 +640,10 @@ "\n", " try:\n", " r = json.loads(aci_service.run(input_data))\n", - " result = r['result']\n", - " time_ms = np.round(r['time'] * 1000, 2)\n", + " result = r['result'][0]\n", + " time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n", " except Exception as e:\n", - " print(str(e), r['error'])\n", + " print(str(e))\n", "\n", " plt.figure(figsize = (16, 6))\n", " plt.subplot(1, 15,1)\n", @@ -657,7 +661,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Optional: How does our MNIST model work? \n", + "## Optional: How does our ONNX MNIST model work? \n", "#### A brief explanation of Convolutional Neural Networks\n", "\n", "A [convolutional neural network](https://en.wikipedia.org/wiki/Convolutional_neural_network) (CNN, or ConvNet) is a type of [feed-forward](https://en.wikipedia.org/wiki/Feedforward_neural_network) artificial neural network made up of neurons that have learnable weights and biases. The CNNs take advantage of the spatial nature of the data. In nature, we perceive different objects by their shapes, size and colors. For example, objects in a natural scene are typically edges, corners/vertices (defined by two of more edges), color patches etc. These primitives are often identified using different detectors (e.g., edge detection, color detector) or combination of detectors interacting to facilitate image interpretation (object classification, region of interest detection, scene description etc.) in real world vision related tasks. These detectors are also known as filters. Convolution is a mathematical operator that takes an image and a filter as input and produces a filtered output (representing say edges, corners, or colors in the input image). \n", @@ -709,95 +713,6 @@ "![](http://www.cntk.ai/jup/conv103d_mnist-conv-mp.png)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Try classifying your own images!\n", - "\n", - "Create your own 28 pixel by 28 pixel handwritten image and pass it into the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Preprocessing functions\n", - "\n", - "def rgb2gray(rgb):\n", - " \"\"\"Convert the input image into grayscale\"\"\"\n", - " return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n", - "\n", - "def preprocess(img):\n", - " \"\"\"Resize input images and convert them to grayscale.\"\"\"\n", - " if img.shape[0] != 28:\n", - " print(\"Input image size is not 28 * 28 pixels. Please resize and try again.\")\n", - " grayscale = rgb2gray(img)\n", - " grayscale.resize((1, 1, 28, 28))\n", - " return grayscale" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Replace this string with your own path/test image\n", - "# Make sure the dimensions are 28 * 28 pixels\n", - "\n", - "# Any PNG or JPG image file should work\n", - "# Make sure to include the entire path with // instead of /\n", - "\n", - "# e.g. your_test_image = \"C://Users//vinitra.swamy//Pictures//digit.png\"\n", - "\n", - "your_test_image = \"\"\n", - "\n", - "import matplotlib.image as mpimg\n", - "\n", - "if your_test_image != \"\":\n", - " img = mpimg.imread(your_test_image)\n", - " plt.subplot(1,3,1)\n", - " plt.imshow(img, cmap = plt.cm.Greys)\n", - " print(\"Old Dimensions: \", img.shape)\n", - " img = preprocess(img)\n", - " print(\"New Dimensions: \", img.shape)\n", - "else:\n", - " img = None" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if img is None:\n", - " print(\"Add the path for your image data.\")\n", - "else:\n", - " input_data = json.dumps({'data': img.tolist()})\n", - "\n", - " try:\n", - " r = json.loads(aci_service.run(input_data))\n", - " result = r['result']\n", - " time_ms = np.round(r['time'] * 1000, 2)\n", - " except Exception as e:\n", - " print(str(e), r['error'])\n", - "\n", - " plt.figure(figsize = (16, 6))\n", - " plt.subplot(1, 15,1)\n", - " plt.axhline('')\n", - " plt.axvline('')\n", - " plt.text(x = -100, y = -20, s = \"Model prediction: \", fontsize = 14)\n", - " plt.text(x = -100, y = -10, s = \"Inference time: \", fontsize = 14)\n", - " plt.text(x = 0, y = -20, s = str(result), fontsize = 14)\n", - " plt.text(x = 0, y = -10, s = str(time_ms) + \" ms\", fontsize = 14)\n", - " plt.text(x = -100, y = 14, s = \"Input image: \", fontsize = 14)\n", - " plt.imshow(img.reshape(28, 28), cmap = plt.cm.gray) " - ] - }, { "cell_type": "code", "execution_count": null, @@ -805,7 +720,6 @@ "outputs": [], "source": [ "# remember to delete your service after you are done using it!\n", - "# uncomment the following line of code to delete your service\n", "\n", "# aci_service.delete()" ] @@ -818,10 +732,10 @@ "\n", "Congratulations!\n", "\n", - "In this tutorial, you have managed to:\n", - "- familiarize yourself with the ONNX model format, ONNX Runtime inference, and the pretrained models in the ONNX model zoo\n", - "- understand a state-of-the-art convolutional neural net image classification model (MNIST in ONNX) and deploy it in the Azure ML cloud\n", - "- ensure that your deep learning model is working perfectly (in the cloud) on test data, and check it against some of your own!\n", + "In this tutorial, you have:\n", + "- familiarized yourself with ONNX Runtime inference and the pretrained models in the ONNX model zoo\n", + "- understood a state-of-the-art convolutional neural net image classification model (MNIST in ONNX) and deployed it in Azure ML cloud\n", + "- ensured that your deep learning model is working perfectly (in the cloud) on test data, and checked it against some of your own!\n", "\n", "Next steps:\n", "- Check out another interesting application based on a Microsoft Research computer vision paper that lets you set up a [facial emotion recognition model](https://github.com/Azure/MachineLearningNotebooks/tree/master/onnx/onnx-inference-emotion-recognition.ipynb) in the cloud! This tutorial deploys a pre-trained ONNX Computer Vision model in an Azure ML virtual machine with GPU support.\n", @@ -831,9 +745,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3", "language": "python", - "name": "python36" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -845,7 +759,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.6.5" }, "msauthor": "vinitra.swamy" },