<filename>Samples/Tutorial Samples/PyTorch Image Classification/PyTorchTraining - Image Classification/PyTorchTraining.py
# Import the needed packages
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import matplotlib.pyplot as plt
import numpy as np
import torch.onnx
from torchvision.datasets import CIFAR10
from torchvision.transforms import transforms
from torch.utils.data import DataLoader
from torch.optim import Adam
from torch.autograd import Variable

# Loading and normalizing the data.
# Define transformations for the training and test sets
transformations = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# CIFAR10 dataset consists of 50K training images. We define the batch size of 10 to load 5,000 batches of images.
batch_size = 10
number_of_labels = 10 

# Create an instance for training.  
# When we run this code for the first time, the CIFAR10 train dataset will be downloaded locally. 
train_set =CIFAR10(root="./data",train=True,transform=transformations,download=True)

# Create a loader for the training set which will read the data within batch size and put into memory.
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=0)
print("The number of images in a training set is: ", len(train_loader)*batch_size)

# Create an instance for testing, note that train is set to False.
# When we run this code for the first time, the CIFAR10 test dataset will be downloaded locally. 
test_set = CIFAR10(root="./data", train=False, transform=transformations, download=True)

# Create a loader for the test set which will read the data within batch size and put into memory. 
# Note that both shuffle is set to false for the test loader.
test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=0)
print("The number of images in a test set is: ", len(test_loader)*batch_size)
print(" ")

print("The number of batches per epoch is: ", len(train_loader))
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')


# Define a convolution neural network
class Network(nn.Module):
    def __init__(self):
        super(Network, self).__init__()
        
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=5, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(12)
        self.conv2 = nn.Conv2d(in_channels=12, out_channels=12, kernel_size=5, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(12)
        
        self.pool = nn.MaxPool2d(2,2)

        self.conv4 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=5, stride=1, padding=1)
        self.bn4 = nn.BatchNorm2d(24)
        self.conv5 = nn.Conv2d(in_channels=24, out_channels=24, kernel_size=5, stride=1, padding=1)
        self.bn5 = nn.BatchNorm2d(24)

        self.fc1 = nn.Linear(24*10*10, 10)

    def forward(self, input):
        output = F.relu(self.bn1(self.conv1(input)))      # the tensor shape is [kernel=5, channels=12, height=30, width=30]
        output = F.relu(self.bn2(self.conv2(output)))     # the tensor shape is [5,12,28,28]
        output = self.pool(output)                        # the tensor shape is [5,12,14,14]
        output = F.relu(self.bn4(self.conv4(output)))     # the tensor shape is [5,24,12,12]
        output = F.relu(self.bn5(self.conv5(output)))     # the tensor shape is [5,24,10,10]
        output = output.view(-1, 24*10*10)
        output = self.fc1(output)

        return output

# Instantiate a neural network model 
model = Network()

# Define the loss function with Classification Cross-Entropy loss and an optimizer with Adam optimizer
loss_fn = nn.CrossEntropyLoss()
optimizer = Adam(model.parameters(), lr=0.001, weight_decay=0.0001)

# Function to save the model
def saveModel():
    path = "./myFirstModel.pth"
    torch.save(model.state_dict(), path)

# Function to test the model with the test dataset and print the accuracy for the test images
def testAccuracy():
    
    model.eval()
    accuracy = 0.0
    total = 0.0
    
    with torch.no_grad():
        
        for data in test_loader:
            images, labels = data
            # Run the model on the test set to predict labels
            outputs = model(images)
            # The label with the highest energy will be our prediction
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            accuracy += (predicted == labels).sum().item()
    
    # Compute the accuracy over all test images
    accuracy = (100 * accuracy / total)
    return(accuracy)   


# Training function. We simply have to loop over our data iterator, and feed the inputs to the network and optimize.
def train(num_epochs):
    
    best_accuracy = 0.0

    # Define your execution device
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    print("The model will be running on", device, "device")
    # Convert model parameters and buffers to CPU or Cuda
    model.to(device)

    for epoch in range(num_epochs):  # loop over the dataset multiple times
        running_loss = 0.0

        for i, (images, labels) in enumerate(train_loader, 0):
            
            # wrap the inputs in Variable
            images = Variable(images.to(device))
            labels = Variable(labels.to(device))

            # zero the parameter gradients
            optimizer.zero_grad()
            # predict classes using images from the training set
            outputs = model(images)
            # compute the loss based on model output and real labels
            loss = loss_fn(outputs, labels)
            # backpropagate the loss
            loss.backward()
            # adjust parameters based on the calculated gradients
            optimizer.step()

            # Let's print statistics for every 1,000 images
            running_loss += loss.item()     # extract the loss value
            if i % 1000 == 999:    
                # print every 1000 (twice per epoch) 
                print('[%d, %5d] loss: %.3f' %
                      (epoch + 1, i + 1, running_loss / 1000))
                # zero the loss
                running_loss = 0.0

        # Compute and print the average accuracy of this epoch when tested over all 10000 test images
        accuracy = testAccuracy()
        print('For epoch', epoch+1,'the test accuracy over the whole test set is %d %%' % (accuracy))
        
        # We want to save the model if the accuracy is the best
        if accuracy > best_accuracy:
            saveModel()
            best_accuracy = accuracy


# Function to show the images
def imageshow(img):
    img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
    plt.show()


# Function to test the model with a batch of images and show the labels predictions
def testBatch():
    # get batch of images from the test DataLoader  
    images, labels = next(iter(test_loader))

    # show all images as one image grid
    imageshow(torchvision.utils.make_grid(images))
   
    # Show the real labels on the screen 
    print('Real labels: ', ' '.join('%5s' % classes[labels[j]] 
                               for j in range(batch_size)))
  
    # Let's see what if the model identifiers the  labels of those example
    outputs = model(images)
    
    # We got the probability for every 10 labels. The highest (max) probability should be correct label
    _, predicted = torch.max(outputs, 1)
    
    # Let's show the predicted labels on the screen to compare with the real ones
    print('Predicted: ', ' '.join('%5s' % classes[predicted[j]] 
                              for j in range(batch_size)))
 

#Function to Convert to ONNX
def Convert_ONNX():
    
    # set the model to inference mode
    model.eval()
    
    # Let's create a dummy input tensor 
    dummy_input = torch.randn(1, 3, 32, 32, requires_grad=True)

    # Export the model  
    torch.onnx.export(model,                 # model being run
                  dummy_input,               # model input (or a tuple for multiple inputs)
                  "ImageClassifier.onnx",          # where to save the model (can be a file or file-like object)
                  export_params=True,        # store the trained parameter weights inside the model file
                  opset_version=10,          # the ONNX version to export the model to
                  do_constant_folding=True,  # whether to execute constant folding for optimization
                  input_names = ['modelInput'],   # the model's input names
                  output_names = ['modelOutput'], # the model's output names
                  dynamic_axes={'modelInput' : {0 : 'batch_size'},    # variable length axes
                                'modelOutput' : {0 : 'batch_size'}})
    print(" ")
    print('Model has been converted to ONNX')


# Function to test what classes performed well
def testClassess():
    class_correct = list(0. for i in range(number_of_labels))
    class_total = list(0. for i in range(number_of_labels))
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            outputs = model(images)
            _, predicted = torch.max(outputs, 1)
            c = (predicted == labels).squeeze()
            for i in range(batch_size):
                label = labels[i]
                class_correct[label] += c[i].item()
                class_total[label] += 1

    for i in range(number_of_labels):
        print('Accuracy of %5s : %2d %%' % (
            classes[i], 100 * class_correct[i] / class_total[i]))

    

if __name__ == "__main__":
    
    # Let's build our model
    train(5)
    print('Finished Training')

    # Test which classes performed well
    testAccuracy()
    
    # Let's load the model we just created and test the accuracy per label
    model = Network()
    path = "myFirstModel.pth"
    model.load_state_dict(torch.load(path))

    # Test with batch of images
    testBatch()
    # Test how the classes performed
    testClassess()

    # Conversion to ONNX
    Convert_ONNX()
        

     
    
<filename>Samples/Tutorial Samples/PyTorch Image Classification/PyTorchTraining - Image Classification/PyTorchTraining.py
# Import the needed packages
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import matplotlib.pyplot as plt
import numpy as np
import torch.onnx
from torchvision.datasets import CIFAR10
from torchvision.transforms import transforms
from torch.utils.data import DataLoader
from torch.optim import Adam
from torch.autograd import Variable

# Loading and normalizing the data.
# Define transformations for the training and test sets
transformations = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# CIFAR10 dataset consists of 50K training images. We define the batch size of 10 to load 5,000 batches of images.
batch_size = 10
number_of_labels = 10 

# Create an instance for training.  
# When we run this code for the first time, the CIFAR10 train dataset will be downloaded locally. 
train_set =CIFAR10(root="./data",train=True,transform=transformations,download=True)

# Create a loader for the training set which will read the data within batch size and put into memory.
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=0)
print("The number of images in a training set is: ", len(train_loader)*batch_size)

# Create an instance for testing, note that train is set to False.
# When we run this code for the first time, the CIFAR10 test dataset will be downloaded locally. 
test_set = CIFAR10(root="./data", train=False, transform=transformations, download=True)

# Create a loader for the test set which will read the data within batch size and put into memory. 
# Note that both shuffle is set to false for the test loader.
test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=0)
print("The number of images in a test set is: ", len(test_loader)*batch_size)
print(" ")

print("The number of batches per epoch is: ", len(train_loader))
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')


# Define a convolution neural network
class Network(nn.Module):
    def __init__(self):
        super(Network, self).__init__()
        
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=5, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(12)
        self.conv2 = nn.Conv2d(in_channels=12, out_channels=12, kernel_size=5, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(12)
        
        self.pool = nn.MaxPool2d(2,2)

        self.conv4 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=5, stride=1, padding=1)
        self.bn4 = nn.BatchNorm2d(24)
        self.conv5 = nn.Conv2d(in_channels=24, out_channels=24, kernel_size=5, stride=1, padding=1)
        self.bn5 = nn.BatchNorm2d(24)

        self.fc1 = nn.Linear(24*10*10, 10)

    def forward(self, input):
        output = F.relu(self.bn1(self.conv1(input)))      # the tensor shape is [kernel=5, channels=12, height=30, width=30]
        output = F.relu(self.bn2(self.conv2(output)))     # the tensor shape is [5,12,28,28]
        output = self.pool(output)                        # the tensor shape is [5,12,14,14]
        output = F.relu(self.bn4(self.conv4(output)))     # the tensor shape is [5,24,12,12]
        output = F.relu(self.bn5(self.conv5(output)))     # the tensor shape is [5,24,10,10]
        output = output.view(-1, 24*10*10)
        output = self.fc1(output)

        return output

# Instantiate a neural network model 
model = Network()

# Define the loss function with Classification Cross-Entropy loss and an optimizer with Adam optimizer
loss_fn = nn.CrossEntropyLoss()
optimizer = Adam(model.parameters(), lr=0.001, weight_decay=0.0001)

# Function to save the model
def saveModel():
    path = "./myFirstModel.pth"
    torch.save(model.state_dict(), path)

# Function to test the model with the test dataset and print the accuracy for the test images
def testAccuracy():
    
    model.eval()
    accuracy = 0.0
    total = 0.0
    
    with torch.no_grad():
        
        for data in test_loader:
            images, labels = data
            # Run the model on the test set to predict labels
            outputs = model(images)
            # The label with the highest energy will be our prediction
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            accuracy += (predicted == labels).sum().item()
    
    # Compute the accuracy over all test images
    accuracy = (100 * accuracy / total)
    return(accuracy)   


# Training function. We simply have to loop over our data iterator, and feed the inputs to the network and optimize.
def train(num_epochs):
    
    best_accuracy = 0.0

    # Define your execution device
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    print("The model will be running on", device, "device")
    # Convert model parameters and buffers to CPU or Cuda
    model.to(device)

    for epoch in range(num_epochs):  # loop over the dataset multiple times
        running_loss = 0.0

        for i, (images, labels) in enumerate(train_loader, 0):
            
            # wrap the inputs in Variable
            images = Variable(images.to(device))
            labels = Variable(labels.to(device))

            # zero the parameter gradients
            optimizer.zero_grad()
            # predict classes using images from the training set
            outputs = model(images)
            # compute the loss based on model output and real labels
            loss = loss_fn(outputs, labels)
            # backpropagate the loss
            loss.backward()
            # adjust parameters based on the calculated gradients
            optimizer.step()

            # Let's print statistics for every 1,000 images
            running_loss += loss.item()     # extract the loss value
            if i % 1000 == 999:    
                # print every 1000 (twice per epoch) 
                print('[%d, %5d] loss: %.3f' %
                      (epoch + 1, i + 1, running_loss / 1000))
                # zero the loss
                running_loss = 0.0

        # Compute and print the average accuracy of this epoch when tested over all 10000 test images
        accuracy = testAccuracy()
        print('For epoch', epoch+1,'the test accuracy over the whole test set is %d %%' % (accuracy))
        
        # We want to save the model if the accuracy is the best
        if accuracy > best_accuracy:
            saveModel()
            best_accuracy = accuracy


# Function to show the images
def imageshow(img):
    img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
    plt.show()


# Function to test the model with a batch of images and show the labels predictions
def testBatch():
    # get batch of images from the test DataLoader  
    images, labels = next(iter(test_loader))

    # show all images as one image grid
    imageshow(torchvision.utils.make_grid(images))
   
    # Show the real labels on the screen 
    print('Real labels: ', ' '.join('%5s' % classes[labels[j]] 
                               for j in range(batch_size)))
  
    # Let's see what if the model identifiers the  labels of those example
    outputs = model(images)
    
    # We got the probability for every 10 labels. The highest (max) probability should be correct label
    _, predicted = torch.max(outputs, 1)
    
    # Let's show the predicted labels on the screen to compare with the real ones
    print('Predicted: ', ' '.join('%5s' % classes[predicted[j]] 
                              for j in range(batch_size)))
 

#Function to Convert to ONNX
def Convert_ONNX():
    
    # set the model to inference mode
    model.eval()
    
    # Let's create a dummy input tensor 
    dummy_input = torch.randn(1, 3, 32, 32, requires_grad=True)

    # Export the model  
    torch.onnx.export(model,                 # model being run
                  dummy_input,               # model input (or a tuple for multiple inputs)
                  "ImageClassifier.onnx",          # where to save the model (can be a file or file-like object)
                  export_params=True,        # store the trained parameter weights inside the model file
                  opset_version=10,          # the ONNX version to export the model to
                  do_constant_folding=True,  # whether to execute constant folding for optimization
                  input_names = ['modelInput'],   # the model's input names
                  output_names = ['modelOutput'], # the model's output names
                  dynamic_axes={'modelInput' : {0 : 'batch_size'},    # variable length axes
                                'modelOutput' : {0 : 'batch_size'}})
    print(" ")
    print('Model has been converted to ONNX')


# Function to test what classes performed well
def testClassess():
    class_correct = list(0. for i in range(number_of_labels))
    class_total = list(0. for i in range(number_of_labels))
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            outputs = model(images)
            _, predicted = torch.max(outputs, 1)
            c = (predicted == labels).squeeze()
            for i in range(batch_size):
                label = labels[i]
                class_correct[label] += c[i].item()
                class_total[label] += 1

    for i in range(number_of_labels):
        print('Accuracy of %5s : %2d %%' % (
            classes[i], 100 * class_correct[i] / class_total[i]))

    

if __name__ == "__main__":
    
    # Let's build our model
    train(5)
    print('Finished Training')

    # Test which classes performed well
    testAccuracy()
    
    # Let's load the model we just created and test the accuracy per label
    model = Network()
    path = "myFirstModel.pth"
    model.load_state_dict(torch.load(path))

    # Test with batch of images
    testBatch()
    # Test how the classes performed
    testClassess()

    # Conversion to ONNX
    Convert_ONNX()
        

     
    
# Import the needed packages # Loading and normalizing the data. # Define transformations for the training and test sets # CIFAR10 dataset consists of 50K training images. We define the batch size of 10 to load 5,000 batches of images. # Create an instance for training. # When we run this code for the first time, the CIFAR10 train dataset will be downloaded locally. # Create a loader for the training set which will read the data within batch size and put into memory. # Create an instance for testing, note that train is set to False. # When we run this code for the first time, the CIFAR10 test dataset will be downloaded locally. # Create a loader for the test set which will read the data within batch size and put into memory. # Note that both shuffle is set to false for the test loader. # Define a convolution neural network # the tensor shape is [kernel=5, channels=12, height=30, width=30] # the tensor shape is [5,12,28,28] # the tensor shape is [5,12,14,14] # the tensor shape is [5,24,12,12] # the tensor shape is [5,24,10,10] # Instantiate a neural network model # Define the loss function with Classification Cross-Entropy loss and an optimizer with Adam optimizer # Function to save the model # Function to test the model with the test dataset and print the accuracy for the test images # Run the model on the test set to predict labels # The label with the highest energy will be our prediction # Compute the accuracy over all test images # Training function. We simply have to loop over our data iterator, and feed the inputs to the network and optimize. # Define your execution device # Convert model parameters and buffers to CPU or Cuda # loop over the dataset multiple times # wrap the inputs in Variable # zero the parameter gradients # predict classes using images from the training set # compute the loss based on model output and real labels # backpropagate the loss # adjust parameters based on the calculated gradients # Let's print statistics for every 1,000 images # extract the loss value # print every 1000 (twice per epoch) # zero the loss # Compute and print the average accuracy of this epoch when tested over all 10000 test images # We want to save the model if the accuracy is the best # Function to show the images # unnormalize # Function to test the model with a batch of images and show the labels predictions # get batch of images from the test DataLoader # show all images as one image grid # Show the real labels on the screen # Let's see what if the model identifiers the  labels of those example # We got the probability for every 10 labels. The highest (max) probability should be correct label # Let's show the predicted labels on the screen to compare with the real ones #Function to Convert to ONNX # set the model to inference mode # Let's create a dummy input tensor # Export the model # model being run # model input (or a tuple for multiple inputs) # where to save the model (can be a file or file-like object) # store the trained parameter weights inside the model file # the ONNX version to export the model to # whether to execute constant folding for optimization # the model's input names # the model's output names # variable length axes # Function to test what classes performed well # Let's build our model # Test which classes performed well # Let's load the model we just created and test the accuracy per label # Test with batch of images # Test how the classes performed # Conversion to ONNX