# Lab 2 - Train your model In this lab we are going the train a PyTorch Model that can classify Simpsons using the resources we have created in the [previous lab](https://workshops.henkboelman.com/developers-guide-to-azure-ai/azure-machine-learning/lab-1). ## 1. Connect to your resources ### Import dependencies Start with importing dependencies. If you are using a Notebook in Azure Machine Learning Studio, you have all the latest versions install. If you are running your own Jupyter notebook then you have to install the azureml-sdk (pip install azureml-sdk). * Paste the code below in the first cell and run this cell. ``` import os, random import azureml import shutil import urllib.request from pathlib import Path import matplotlib.pyplot as plt from mpl_toolkits.axes_grid1 import AxesGrid import cv2 import urllib3 import zipfile from azureml.core.model import Model, InferenceConfig from azureml.core import Workspace, Datastore, Experiment, Run, Environment, ScriptRunConfig from azureml.core.compute import ComputeTarget, AmlCompute, AksCompute, ComputeTarget from azureml.train.dnn import PyTorch from azureml.widgets import RunDetails from azureml.core.webservice import Webservice, AksWebservice, AciWebservice from azureml.core.dataset import Dataset from azureml.core.resource_configuration import ResourceConfiguration from azureml.core.conda_dependencies import CondaDependencies # check core SDK version number print("Azure ML SDK Version: ", azureml.core.VERSION) ``` ![Import dependencies](https://475228360-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MF0ro-V-CjhNEKidSUU%2Fsync%2F3e89d1f56ad5773c7defa605da725160826c10ee.png?generation=1597759379306347\&alt=media) ### Connect to workspace * Create a new code cell by clicking on the '+ Code' button * Paste the code below ``` ws = Workspace.from_config() print("Connected to workspace: ",ws.name) ``` * Run the cell * Performing the interactive authentication using the link and code provide * Run the cell again * It should say: "Connected to workspace: " ### Connect to Azure Machine Learning Compute Cluster ``` compute_target = ComputeTarget(workspace=ws, name="gpu-cluster") print("Connected to compute target:",compute_target.name) ``` ### Connect to the default datastore ``` ds = Datastore.get_default(ws) print("Connected to datastore:",ds.name) ``` ![Connect to resources](https://475228360-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MF0ro-V-CjhNEKidSUU%2Fsync%2Fea0fa83a1c1778c31bb5e95232b79340500dd80a.png?generation=1597759377867115\&alt=media) ### Create an experiment ``` exp = Experiment(workspace=ws, name='Simpsons-PyTorch') print("Experiment created:",exp.name) ``` > View your created experiment on: [https://ml.azure.com](https://ml.azure.com/experiments) ## 2. Data ### Download the dataset from Github ``` data_url = "https://github.com/hnky/dataset-lego-figures/raw/master/_download/train-and-validate.zip" data_path = "./data" download_path = os.path.join(data_path,"train-and-validate.zip") if not os.path.exists(data_path): os.mkdir(data_path,); urllib.request.urlretrieve(data_url, filename=download_path) ``` ### Unzip the dataset ``` zip_ref = zipfile.ZipFile(download_path, 'r') zip_ref.extractall(data_path) zip_ref.close() print("Data extracted in: {}".format(data_path)) os.remove(download_path) print("Downloaded file removed: {}".format(download_path)) ``` > View your the downloaded dataset: [https://ml.azure.com](https://ml.azure.com/fileexplorerAzNB) > > *Choose the refresh button above your folder structure to see data files* ![Dataset](https://475228360-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MF0ro-V-CjhNEKidSUU%2Fsync%2Ffb6811ca83b548c5f46199c9227645b42907412b.png?generation=1597759379577765\&alt=media) ### Preview the dataset To take a peak at the images in the dataset paste and the run the code below. ``` from mpl_toolkits.axes_grid1 import AxesGrid import random import cv2 import matplotlib.pyplot as plt path = r"data/train" random_filenames = [] for tag in os.listdir(path): random_filenames.append(path+"/"+tag+"/"+random.choice([ x for x in os.listdir(os.path.join(path,tag)) if os.path.isfile(os.path.join(path,tag, x)) ])) grid = AxesGrid(plt.figure(1, (20,20)), 111, nrows_ncols=(4, 5), axes_pad=0, label_mode="1") i = 0 for img_name in random_filenames[0:10]: image = cv2.imread(img_name) image = cv2.resize(image, (352, 352)) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Show image in grid grid[i].imshow(image) i = i+1 ``` ![Dataset](https://475228360-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MF0ro-V-CjhNEKidSUU%2Fsync%2F9120866bed8886424db4daa189854dd0e910f1db.png?generation=1597759378977194\&alt=media) ### Upload the data to the datastore ``` ds.upload(src_dir=data_path, target_path='simpsonslego', overwrite=True, show_progress=True) ``` ### Create a dataset from the data in the datastore ``` datastore_paths = [(ds, 'simpsonslego/**')] simpsons_ds = Dataset.File.from_files(path=datastore_paths) ``` ### Register the dataset ``` simpsons_ds.register(workspace=ws, name='LegoSimpsons', description='Simpsons dataset with Lego Figures', create_new_version = True) ``` ### Connect to the dataset ``` simpsons_ds = Dataset.get_by_name(ws, name='LegoSimpsons') ``` ## Train the model ### Download the training script ``` project_folder = "./trainingscripts" training_script_url = "https://raw.githubusercontent.com/hnky/DevelopersGuideToAI/master/amls/resources/train.py" training_script_download_path = os.path.join(project_folder,"train.py") if not os.path.exists(project_folder): os.mkdir(project_folder); urllib.request.urlretrieve(training_script_url, filename=training_script_download_path) ``` > Refresh your files and validate that 'train.py' is downloaded in the folder 'trainingscripts': [https://ml.azure.com](https://ml.azure.com/fileexplorerAzNB) ![Training Script](https://475228360-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MF0ro-V-CjhNEKidSUU%2Fsync%2Fee62d3251ac3b58f54ef7e945a5a33146d508740.png?generation=1597759378572779\&alt=media) ``` curated_env_name = 'AzureML-PyTorch-1.6-GPU' pytorch_env = Environment.get(workspace=ws, name=curated_env_name) pytorch_env = pytorch_env.clone(new_name='pytorch-1.6-gpu') ``` ``` args = [ '--data-folder', simpsons_ds.as_named_input('simpsons').as_mount(), '--num-epochs', 15 ] project_folder = "./trainingscripts" config = ScriptRunConfig( source_directory = project_folder, script = 'train.py', compute_target=compute_target, environment = pytorch_env, arguments=args, ) ``` ### Submit the PyTorch estimator ``` run = exp.submit(config) ``` ### Follow the progress of the run ``` RunDetails(run).show() ``` * Click on 'View run details' to view all the details of the run under the experiment. ![Training starting](https://475228360-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MF0ro-V-CjhNEKidSUU%2Fsync%2Ff59573e3d1b184dddbce1386dde9c5f52e100bc5.png?generation=1597759379146174\&alt=media) > **This step can take up to 15 minutes to complete** ![Training done](https://475228360-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MF0ro-V-CjhNEKidSUU%2Fsync%2Fb49f81a6090d90277fb33a04c7cf01b06b2fe1f0.png?generation=1597759378087123\&alt=media) ### Register the model ``` model = run.register_model(model_name='Simpsons-PyTorch', model_path='outputs', model_framework='PyTorch', model_framework_version='1.3', description="Simpsons PyTorch Classifier", tags={'Conference':'Awesome AI Workshop'}, resource_configuration=ResourceConfiguration(cpu=1, memory_in_gb=2)) print("Model '{}' version {} registered ".format(model.name,model.version)) ``` ![Test results](https://475228360-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MF0ro-V-CjhNEKidSUU%2Fsync%2Fcd099854de313c592ba03e972301729f076751f3.png?generation=1597759379565495\&alt=media) > Validate that your model is visible under 'Models': [https://ml.azure.com](https://ml.azure.com/model/list) ## Download and test your model ### Download the model ``` model.download(exist_ok=True) ``` ### Download test images ``` test_images_url = "https://github.com/hnky/dataset-lego-figures/raw/master/_download/test-images.zip" test_images_path = r"./data/test" test_images_download_path = os.path.join(test_images_path,"test-images.zip") if not os.path.exists(test_images_path): os.mkdir(test_images_path,); urllib.request.urlretrieve(test_images_url, filename=test_images_download_path) ``` ### Unzip test images ``` zip_ref = zipfile.ZipFile(test_images_download_path, 'r') zip_ref.extractall(test_images_path) zip_ref.close() print("Data extracted in: {}".format(test_images_path)) os.remove(test_images_download_path) print("Downloaded file removed: {}".format(test_images_download_path)) ``` ### Run the model over the test images ``` import os import torch import torch.nn as nn import torchvision from torchvision import transforms import json import urllib from PIL import Image # Load the model loaded_model = torch.load(os.path.join('outputs','model.pth'), map_location=lambda storage, loc: storage) loaded_model.eval() # Load the labels with open(os.path.join('outputs','labels.txt'), 'rt') as lf: global labels labels = [l.strip() for l in lf.readlines()] def scoreImage(image_link): # Load the image to predict input_image = Image.open(image_link) # Pre process preprocess = transforms.Compose([ transforms.Resize(225), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) input_tensor = preprocess(input_image) input_batch = input_tensor.unsqueeze(0) # Predict the image if torch.cuda.is_available(): input_batch = input_batch.to('cuda') loaded_model.to('cuda') with torch.no_grad(): output = loaded_model(input_batch) index = output.data.cpu().numpy().argmax() probability = torch.nn.functional.softmax(output[0], dim=0).data.cpu().numpy().max() #Return the result return {"label": labels[index], "probability": round(probability*100,2)} path = r"data/test" grid = AxesGrid(plt.figure(1, (20,20)), 111, nrows_ncols=(4, 5), axes_pad=0, label_mode="1") i = 0 for img in os.listdir(path): #Score the image result = scoreImage(os.path.join(path,img)) # Download image image = cv2.imread(os.path.join(path,img)) image = cv2.resize(image, (352, 352)) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) cv2.rectangle(image, (0,260),(352,352),(255,255,255), -1) cv2.putText(image, "{} - {}%".format(result['label'],result['probability']),(10, 300), cv2.FONT_HERSHEY_SIMPLEX, 0.65,(0,0,0),2,cv2.LINE_AA) # Show image in grid grid[i].imshow(image) i = i+1 ``` ![Test results](https://475228360-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MF0ro-V-CjhNEKidSUU%2Fsync%2F630f19023d9b05e1942bc18aa67e82a27242a7ed.png?generation=1597759378513330\&alt=media) [**Continue with lab 3 >**](https://workshops.henkboelman.com/developers-guide-to-azure-ai/azure-machine-learning/lab-3)