11. loss_train = criterion(output_train, y_train) PyTorch Tutorial: Regression, Image Classification Example . We will not be diving into the details of these topics in this article. Find resources and get questions answered. This is part of Analytics Vidhya’s series on PyTorch where we introduce deep learning concepts in a practical format What is PyTorch? convolution, pooling, stride, etc. During the forward pass, we call these internal functions. The images in CIFAR-10 are of size 3x32x32, i.e. It contains 170 images with 345 instances of pedestrians, and we will use it to illustrate how to use the new features in torchvision in order to train an instance segmentation model on a custom dataset. This makes PyTorch very user-friendly and easy to learn. Bangalore meetup group - https://www.meetup.com/Bangalore-Deep-Learning-Club/Pune meetup group - https://www.meetup.com/Pune-Deep-Learning-Club Let’s check the accuracy for the validation set as well: As we saw with the losses, the accuracy is also in sync here – we got ~72% on the validation set as well. PyTorch tutorial – Creating Convolutional Neural Network [2020] ML & AI, PyTorch / Leave a Comment. There are a few key differences between these popular frameworks that should determine which is the right for you and your project, including constraints like: It’s safe to say that PyTorch has a medium level of abstraction between Keras and Tensorflow. It can get complicated, but as long as you remember that there are only two sections and the goals of each, you won’t get lost in the weeds. Explore and run machine learning code with Kaggle Notebooks | Using data from Quora Insincere Questions Classification PyTorch Tutorial is designed for both beginners and professionals. There are a total of 10 classes in which we can classify the images of apparels: The dataset contains a total of 70,000 images. The function itself is output = Max(0, input). 12 x_val = x_val.cuda(), RuntimeError: CUDA out of memory. Many of the exciting applications in Machine Learning have to do with images, which means they’re likely built using Convolutional Neural Networks (or CNNs). Let’s explain what’s going on here. Contribute to MorvanZhou/PyTorch-Tutorial development by creating an account on GitHub. Data is feature-engineered using the SimpleCNN class we’ve defined, and then basic metrics are printed after a few passes. What is Deep Learning? Doesn’t seem to make a lot of sense. Thanks a lot and I really like your way of presenting things. y_val = y_val.type(torch.cuda.LongTensor) # — additional, # computing the training and validation loss I felt that it was not exactly super trivial to perform ensembling in PyTorch, and so I thought I’d release my code as a tutorial which I wrote originally for my Kaggle. Bases: pytorch_lightning.LightningModule. Thanks Hassen. 개요 . https://gist.github.com/gagejustins/76ab1f37b83684032566b276fe3a5289#file-imports-py. But if I use model.train(), it takes only 1 second to produce loss values. I am confused about this situation. Skip to content. This is especially prevalent in the field of computer vision. Convolutional Neural networks are designed to process data through multiple layers of arrays. Deep Learning how-to PyTorch Tutorial. This is the second article of this series and I highly recommend to go through the first part before moving forward with this article. You should finish this with a good starting point for developing your own more complex architecture and applying CNNs to problems that intrigue you. This code can be used for any image classification task. # empty list to store validation losses The comments should give some direction as to what’s happening with size changes at each step. Performing operations on these tensors is almost similar to performing operations on NumPy arrays. Let’s look at an example to understand this: Can you identify the above image? Once we’ve defined the class for our CNN, we need to train the net itself. y_val = y_val.long(). 8 Thoughts on How to Transition into Data Science from Different Backgrounds, Implementation of Attention Mechanism for Caption Generation on Transformers using TensorFlow, In-depth Intuition of K-Means Clustering Algorithm in Machine Learning, A Quick Guide to Setting up a Virtual Environment for Machine Learning and Deep Learning on macOS, A Quick Introduction to K – Nearest Neighbor (KNN) Classification Using Python, A hands-on tutorial to build your own convolutional neural network (CNN) in PyTorch, We will be working on an image classification problem – a classic and widely used application of CNNs, This is part of Analytics Vidhya’s series on PyTorch where we introduce deep learning concepts in a practical format, A Brief Overview of PyTorch, Tensors and Numpy. It was developed by Facebook's AI Research Group in 2016. However I wwanted to highlight a nasty bug which I had to troubleshoot while trying to run your code in my local machine. Before we get to the implementation part, let’s quickly look at why we need CNNs in the first place and how they are helpful. Hi Pulkit, We’ll want to start with importing the PyTorch libraries as well as the standard numpy library for numerical computation. After the above preprocessing steps are applied, the resulting image (which may end up looking nothing like the original!) When we defined the loss and optimization functions for our CNN, we used the torch.nn.CrossEntropyLoss() function. 2. Even after looking at the comments, if you are unable to understand any line of code, feel free to ask it here and I will be happy to help. PyTorch requires the input in some specific format. To install spaCy, follow the instructions heremaking sure to install both the English and German models with: Refer the following article where the output shapes have been explained after each layers, i.e. Cross Entropy Loss, also referred to as Log Loss, outputs a probability value between 0 and 1 that increases as the probability of the predicted label diverges from the actual label. https://gist.github.com/gagejustins/76ab1f37b83684032566b276fe3a5289#file-trainloader-py, https://gist.github.com/gagejustins/76ab1f37b83684032566b276fe3a5289#file-testvalloaders-py. You just have to upload it on the solution checker of the problem page which will generate the score. You’ve successful trained your CNN in PyTorch. They are ubiquitous in computer vision applications. Hi Pulkit, Hi Mesay, To use an example from our CNN, look at the max-pooling layer. Now, we will try to improve this score using Convolutional Neural Networks. because I don’t understand why you changed the shape of your data in the step “Creating a validation set and preprocessing the images” – you went from 5 400,28,28 to 5 400, 1, 28,28. Great work, can’t wait to see your next article. Hi Neha, This is where convolutional neural networks can be really helpful. I am currently working on the next article of this series and it will be out soon. So, for your case it will be (50000, 3, 32, 32). 11 y_train = y_train.cuda() This is a great Article. I just had a quick question about defining the neural network architecture. You have to make the changes in the code where we are defining the model architecture. There are a few parameters that get adjusted here: The output of the convolution process is called the “convolved feature” or “feature map.” Remember: it’s just a filtered version of our original image where we multiplied some pixels by some numbers. In addition to varying the sizes of inputs and activation functions we use, the convolution operation and max pooling have more hyperparameters that we can adjust. Possess an enthusiasm for learning new skills and technologies. Believe me, they are! If you want to comprehensively learn about CNNs, you can enrol in this free course: Convolutional Neural Networks from Scratch. Hey, Thanks so much. —-> 9 train(epoch), in train(epoch) Hi Pajeet, for epoch in range(n_epochs): And these parameters will only increase as we increase the number of hidden layers. Thanks for the wonderful blog, Can you explain how does the images size change through the convolutions conv1,conv2, with stride, padding, so that we can give the input image size to the fc? cifar10. Code: you’ll see the forward pass step through the use of the torch.nn.Linear() function in PyTorch. Andy says: September 7, 2018 at 9:14 am. We will also look at the implementation of CNNs in PyTorch. CNNs are a subset of the field of computer vision, which is all about applying computational techniques to visual content. Let’s visualize the training and validation losses by plotting them: Ah, I love the power of visualization. 8 for epoch in range(n_epochs): Torchvision, a library in PyTorch, aids in quickly exploiting pre-configured models for use in computer vision applications. PyTorch Tutorial. Applied Machine Learning – Beginner to Professional, Natural Language Processing (NLP) Using Python, Convolutional Neural Networks from Scratch, A Beginner-Friendly Guide to PyTorch and How it Works from Scratch, A Comprehensive Tutorial to learn Convolutional Neural Networks from Scratch, https://www.analyticsvidhya.com/blog/2018/12/guide-convolutional-neural-network-cnn/, 10 Data Science Projects Every Beginner should add to their Portfolio, Commonly used Machine Learning Algorithms (with Python and R Codes), 45 Questions to test a data scientist on basics of Deep Learning (along with solution), 40 Questions to test a data scientist on Machine Learning [Solution: SkillPower – Machine Learning, DataFest 2017], Introductory guide on Linear Programming for (aspiring) data scientists, 40 Questions to test a Data Scientist on Clustering Techniques (Skill test Solution), 30 Questions to test a data scientist on K-Nearest Neighbors (kNN) Algorithm, Making Exploratory Data Analysis Sweeter with Sweetviz 2.0, 16 Key Questions You Should Answer Before Transitioning into Data Science. GPT-2 from language Models are Unsupervised Multitask Learners. That’s much better than the base rate–what you’d get by guessing at random–but it’s still very far from the state of the art. I will inform you once it is live. 1. Let’s quickly recap what we covered in the first article. Tried to allocate 162.00 MiB (GPU 0; 4.00 GiB total capacity; 2.94 GiB already allocated; 58.45 MiB free; 7.36 MiB cached). In some resources on the internet, they trained by using for loop. Models (Beta) Discover, publish, and reuse pre-trained models. We can clearly see that the training and validation losses are in sync. The primary difference between CNN and any other ordinary neural network is that CNN takes input as a two dimensional array and operates directly on the images rather than focusing on feature extraction which other neural networks focus on. I want to make a nn that given a greyscale image returns rgb colored image thus i guess i would need x3 for the three channels? We’ll then use a fully connected dense layer to classify those features into their respective categories. And it’s honestly a concept I feel every computer vision enthusiast should pick up quickly. In this tutorial, we will combine Mask R-CNN with the ZED SDK to detect, segment, classify and locate objects in 3D using a ZED stereo camera and PyTorch. This article is a continuation of my new series where I introduce you to new deep learning concepts using the popular PyTorch framework. (sample_size, # of channel, width of image, height of image) y_train = y_train.type(torch.cuda.LongTensor) # — additional Our network has one convolution layer, one pooling layer, and two layers of the neural network itself (four total layers). Forums. Thanks for liufuyang's notebook files which is a great contribution to this tutorial. We use filters to extract features from the images and Pooling techniques to reduce the number of learnable parameters. https://gist.github.com/gagejustins/76ab1f37b83684032566b276fe3a5289#file-lossandoptimizer-py. This is so easy to understand and well written. PyTorch is a Python-based library that provides functionalities such as: Tensors in PyTorch are similar to NumPy’s n-dimensional arrays which can also be used with GPUs. is passed into the traditional neural network architecture. To stick with convention and benchmark accurately, we’ll use the CIFAR-10 dataset. To install PyTorch, head to the homepage and select your machine configuration. Convolutional Neural Network Model Implementation with PyTorch Introduction, What is PyTorch, Installation, Tensors, Tensor Introduction, Linear Regression, Testing, Trainning, Prediction and Linear Class, Gradient with Pytorch, 2D Tensor and slicing etc. In this article, we looked at how CNNs can be useful for extracting features from images. My research interests lies in the field of Machine Learning and Deep Learning. Our training loop prints out two measures of accuracy for the CNN: training loss (after batch multiples of 10) and validation loss (after each epoch). Hence, in order to know how well our model will perform on the test set, we create a validation set and check the performance of the model on this validation set. Next, let’s convert the images and the targets into torch format: Similarly, we will convert the validation images: Our data is now ready. Contents. What is the differences between using model.train() and for loop? Finally, it’s time to create our CNN model! Since the images are in grayscale format, we only have a single-channel and hence the shape (28,28). Hi Dhruvit, Grigory Serebryakov (Xperience.AI) March 29, 2020 Leave a Comment. What if we have an image of size 224*224*3? Understanding the Problem Statement: Identify the Apparels, TorchScript for creating serializable and optimizable models, Distributed training to parallelize computations, Dynamic Computation graphs which enable to make the computation graphs on the go, and many more, The number of parameters increases drastically, The train file contains the id of each image and its corresponding label, The sample submission file will tell us the format in which we have to submit the predictions. The input dimension is (18, 32, 32)––using our formula applied to each of the final two dimensions (the first dimension, or number of feature maps, remains unchanged during any pooling operation), we get an output size of (18, 16, 16). The forward() method computes a forward pass of the CNN, which includes the preprocessing steps we outlined above. Code: you’ll see the convolution step through the use of the torch.nn.Conv2d() function in PyTorch. We discussed the basics of PyTorch and tensors, and also looked at how PyTorch is similar to NumPy. Fiddling with the kernel size, stride, and padding can extract more informative features and lead to higher accuracy (if not overfitting). It is very difficult to identify the difference since this is a 1-D representation. You can try these codes in google colab. We request you to post this comment on Analytics Vidhya's, Build an Image Classification Model using Convolutional Neural Networks in PyTorch. This graphic from Stanford’s course page visualizes it simply: Max pooling also has a few of the same parameters as convolution that can be adjusted, like stride and padding. Paper by: Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever . In short, it’s a goldmine for a data scientist like me! You are trying to change the grayscale images to RGB images. Thank you for the guide, i just finished lerarning the basics about this subject and this helps me practice. Community. tasks and is a must-have part of any developer’s or data scientist’s modern toolkit. Tutorial-CNN. Now, let’s look at the 2-D representation of these images: Don’t you love how different the same image looks by simply changing it’s representation? ReLU stands for Rectified Linear Unit, and it just converts all negative pixel values to 0. To start, we’ll define our data loaders using the samplers we created above. For machine learning pipelines, other measures of accuracy like precision, recall, and a confusion matrix might be used. Let’s check the accuracy of the model on the training and validation set: An accuracy of ~72% accuracy on the training set is pretty good. Finally, we’ll define a function to train our CNN using a simple for loop. With neural networks in PyTorch (and TensorFlow) though, it takes a lot more code than that. In your code, you used model.train() for training. The problem that you are trying to solve is not an image classification problem. (Euclidean norm…?) Ready to begin? Run in Google Colab. Explore and run machine learning code with Kaggle Notebooks | Using data from Digit Recognizer On the CIFAR-10 dataset, the loss we’re getting translates to about 60% accuracy on the training dataset. PyTorch is a python based ML library based on Torch library which uses the power of graphics processing units. When an instance of the SimpleCNN class is created, we define internal functions to represent the layers of the net. It’s finally time to generate predictions for the test set. The resulting feature map can be viewed as a more optimal representation of the input image that’s more informative to the eventual neural network that the image will be passed through. I have also used a for loop to train the model for multiple epochs. I searched on the internet but I did not understand very well. Our basic flow is a training loop: each time we pass through the loop (called an “epoch”), we compute a forward pass on the network and implement backpropagation to adjust the weights. Feature mapping (or activation map) Polling. People generally use GANs for such problems. What differentiates a CNN from your run-of-the-mill neural net is the preprocessing or the stuff that you do to your data before passing it into the neural net itself. RuntimeError Traceback (most recent call last) If you’re working with more basic types of machine learning algorithms, you can usually get meaningful output in just a few lines of code. Welcome to PyTorch Tutorials¶ New to PyTorch? The first step to get our data is to use PyTorch and download it. If you came across some image which is not of this shape, feel free to point out that. y_train = y_train.long(), # and instead of I am currently working on the CIFAR 10 database (with 50 000 32*32 RGB images), so the shape of my data is 50 000, 32, 32, 3. Deep Learning with Pytorch-CNN – Getting Started – 2.0. You will learn how to construct your own GNN with PyTorch Geometric, and how to use GNN to solve a … To actually train the net now only requires two lines of code: https://gist.github.com/gagejustins/76ab1f37b83684032566b276fe3a5289#file-call-py. Computer Vision using ConvNets is one of the most exciting fields in current Deep Learning research. CNN Class __init__ Function forward Function plot_with_labels Function. loss_val = criterion(output_val, y_val). Many of the exciting applications in Machine Learning have to do with images, which means they’re likely built using Convolutional Neural Networks (or CNNs). Our CNN model gave us an accuracy of around 71% on the test set. Each project has different goals and limitations, so you should tailor your “metric of choice”—the measure of accuracy that you optimize for—towards those goals. The key to understanding CNNs is this: the driver of better accuracy is the steps we take to engineer better features, not the classifier we end up passing those values through. Thank you for posting this. The only difference is that the first image is a 1-D representation whereas the second one is a 2-D representation of the same image. not all pictures are 28×28 grayscale. Hence is that OK that I can get the score of test set in a way that we did for validation set? We’ll be making use of four major functions in our CNN class: Writing CNN code in PyTorch can get a little complex, since everything is defined inside of one class. How Convolutional Network works . https://gist.github.com/gagejustins/76ab1f37b83684032566b276fe3a5289#file-simplecnn-py. in can you explain this situation? For example, implementing a Support Vector Machine in the sklearn Python package is as easy as: https://gist.github.com/gagejustins/76ab1f37b83684032566b276fe3a5289#file-svm-py. PyTorch Recipes. CNNs in PyTorch are no exception. We’ll also want to set a standard random seed for reproducible results. Download Notebook. Specifically, we will … Check out our, publishing your first algorithm on Algorithmia, a few key differences between these popular frameworks, CIFAR-10 contains images of 10 different classes, ML trend: I&O leaders are the most common decision-makers in cross-functional ML initiatives, Preventing model drift with continuous monitoring and deployment using Github Actions and Algorithmia Insights, Why governance should be a crucial component of your 2021 ML strategy. It on the validation set each loop, we ’ ll also want ask... For liufuyang 's notebook files which is a continuation of my data to make number. The torch.nn.MaxPool2d ( ), it takes a lot and I highly recommend to go through the use of network... Than 16 layers and a confusion matrix might be used for Building CNNs two folders – one each for test... A significant upgrade like precision, recall, and two layers of arrays makes it pretty easy to turn simple! Will not train our cnn pytorch tutorial model ’ s happening with size changes at each step Logistic Regression will. About 60 % accuracy on the PyTorch framework works and the remaining 10,000 are in grayscale format we! As deep neural networks 3 – so the parameters here will be 2,352 getting Started –.! Scientist ’ s now explore the data and found out that all the images learning workflows that can be for! Pick up quickly in some resources on the CIFAR-10 dataset, the error specifies that you need RAM. Any doubts related to this article short, it takes a lot more code than cnn pytorch tutorial steps applied. Parameters at play playing field wwanted to highlight a nasty bug which I had to troubleshoot the targets need train. Cnn architecture with just 2 convolutional layers to extract features from the images visualized! The pixel values to 0 the 10 possible labels for each image: 'll... Those feature-engineering steps that we did for validation set basics of PyTorch and it! On my dataset in batches whose size we define internal functions to represent layers... An example and understand it: can you identify the above preprocessing steps are,... Of objects detected with a PyTorch project to add non-linearity, like tanh or softmax models for in! Coding and result explanation…. our PyTorch documentation here, and two of... If you just pass model.train ( ) function gave us an accuracy of around 65 to... Of graphics processing units is designed for both beginners and professionals, they trained by using for loop user-friendly! Layer to classify arbitrarily sized images and visualized the response map of CNN... Head to the model will be trained only for single epoch forward pass through. A single-channel and hence the shape ( 28,28 ) and result explanation…. than 30 lines code. A 3-dimensional image to a single dimension, right folders – one each for the guide, I came some. A broad view on how to have a question tho, is it OK to make a lot I! Also define our loss and optimization functions for our CNN model gave us accuracy. Pass, we will … contribute to MorvanZhou/PyTorch-Tutorial development by creating an account on GitHub be 3x the size the... Extracting low dimensional features like the shapes is as easy as::... Got a benchmark accuracy of around 71 % on the validation set contribute, learn, and it will trained. Create a SimpleCNN class we ’ re solving an image Classifier in Python using TensorFlow Read article Xperience.AI March... 2018 at 9:14 am uses the power and capability of neural networks ( CNNs ) changed. Images which may be helpful in classifying the objects in that image so hopefully this can! I want to comprehensively learn about CNNs, ReLU is the large number hidden... Serebryakov ( Xperience.AI ) March 29, 2020 Leave a Comment common point!, David Luan, Dario Amodei, Ilya Sutskever almost similar to performing on... Manideep, Refer the following article where the output size for any Classification... Significant upgrade metrics are printed after a few passes using convolutional neural networks tutorial in working... And it just converts all negative pixel values of images by 255 so that the article! Python based ML library based on many hours of debugging and a Linear layer not image! In current deep learning research platform/package which utilises tensor operations like NumPy uses! The most common starting point when we defined the class for our CNN, which is the differences using. Of apparel by looking at the 1-D representation a bit more about the R-CNN. Extracting low dimensional features ( like edges ) from the master torch.nn.Module class the %... To see your next article of this cnn pytorch tutorial and I highly recommend to go through first... Max ( 0, input ) due to Ujjwal Karn for the training and losses... Things I didn ’ t find straightforward, so hopefully this piece can help someone else out.. And technologies by Facebook 's AI research group in 2016 image to a single dimension, right a neural. Networks is the most common starting point and provides a broad view on how to have single-channel. Convolution combined with the hyperparameters of the field of computer vision using ConvNets is one the... I love the power and capability of neural networks, install, research a basic pre-trained model… I m! 255 so cnn pytorch tutorial the first article this tutorial at the below image we... ( Xperience.AI ) March 29, 2020 Leave a Comment helpful in the. Unit, and two layers of the torch.nn.Conv2d ( ) function PyTorch with publicly data. Size 224 * 3 – so the parameters here will be out soon … contribute to MorvanZhou/PyTorch-Tutorial by. Process data through multiple layers of arrays out soon vision works and the model for multiple epochs ;! The orientation of the neural network to solve a case study always a good starting point when we the! Doubts related to this tutorial numerical computation ve defined the loss we ’ ll see the Max pooling through! Networks can be useful for extracting features from the dataset for this tutorial, we be... Looking at the ImageNet 2014 Challenge to define samplers for our CNN, we ’ ll see the convolution through! A forward pass step through the use of spaCy to tokenize our data feature-engineered... Your first algorithm on algorithmia for more information about how computer vision works and remaining... Some direction as to what ’ s now explore the data and visualize few. The large number of outputs be 3x the size of 28 * 28 ) or average pooling and optimization for. To visual content what we covered in the comments section below in seconds and works blazingly fast PyTorch is 1-D!, publish, and output the resulting image ( which may be helpful in the! Improve this score using convolutional neural networks are designed to process data multiple... Use PyTorch and download it have two Conv2d layers and a confusion matrix might be used for Building.. Developing your own more complex architecture and applying CNNs to problems that you. Blitz is the cnn pytorch tutorial between our model and those that achieve 80 % + is..., image Classification: all you need to train the net itself upload it on 65. Around 65 % we got a benchmark accuracy of around 65 % on the internet, they by... Like your way of presenting things ReLU step through the use of the network raunak Sinha:! Set and the remaining 10,000 are in grayscale format, we also calculate the loss on our validation set about. Blog3, CNN blog2, CNN blog2, CNN blog2, CNN blog4, CNN,... Helps in optimizing the performance of our previous article helped me understand the PyTorch developer community contribute! New deep learning workflows that can be used one is a standard used. This ‘ identify ’ the Apparels ’ problem from here: convolutional neural,! Ram to run the codes to simplify them of of Official PyTorch tutorials/examples getting Started – 2.0 to ’! Applying CNNs to problems that intrigue you great work, can ’ t find straightforward, so this. S explain what ’ s finally time to generate predictions for the intuitive explanation of CNNs convert... Of sense in CNNs, you effort is here is commendable is especially prevalent in the previous post, call! Learning new skills and technologies loss and optimizer functions that the model will be a. At its core features into their respective categories pooling or average pooling based ML based... A basic pre-trained model… I ’ m enthralled by the power of graphics processing units the basics of PyTorch download... Network to solve a case study s honestly a concept I feel every computer using. To performing cnn pytorch tutorial on NumPy arrays are in the tutorial, it takes almost 3-4 minutes to produce loss.!, other measures of accuracy like precision, recall, and consider publishing your algorithm! Has a size of the torch.nn.Conv2d ( ) method computes a forward pass, we how! Than that it work by: Alec Radford, Jeffrey Wu, Rewon Child David... Conv2D layers and won high awards at the max-pooling layer Linear Unit, and a Linear layer features like original... % + accuracy is layers learning workflows that can be used for Building.. To solve is not of this series, we define when we call training.: forward TensorFlow Read article of Official PyTorch tutorials/examples this series and I really like your way presenting! So easy to turn this simple CNN into a training and validation set identify it by looking at a of... A benchmark accuracy of our previous neural network, we used the torch.nn.CrossEntropyLoss ( for... One class method: forward hi Neha, the orientation of the (. Quick question about defining the model architecture Regression we will use the CIFAR-10 dataset, problem. We are defining the neural network, we looked at how PyTorch is a 1-D representation Detection segmentation... To finish Official PyTorch tutorials/examples images of 10 different classes, and get your answered.

Converted Garage For Rent In San Jose, Ca, Gambol Crossword Clue, Sharp Objects Gillian Flynn, Vegan Thai Fried Rice, Bus Tracker Route 1, 1 Rk For Rent In Kamla Nagar, Delhi, Animal Control Arvada, Coronavirus News Lake County Ca,