neural_network/datasets.py

@@ -0,0 +1,42 @@
+import torchvision
+import torch
+import torchvision.transforms as transforms
+
+from torch.utils.data import DataLoader
+
+BATCH_SIZE = 64
+
+
+train_transform = transforms.Compose([
+    transforms.Resize((224, 224)), #validate that all images are 224x244
+    transforms.RandomHorizontalFlip(p=0.5),
+    transforms.RandomVerticalFlip(p=0.5),
+    transforms.GaussianBlur(kernel_size=(5, 9), sigma=(0.1, 5)),
+    transforms.RandomRotation(degrees=(30, 70)), #random effects are applied to prevent overfitting
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=[0.5, 0.5, 0.5],
+        std=[0.5, 0.5, 0.5]
+    )
+])
+
+valid_transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=[0.5, 0.5, 0.5],
+        std=[0.5, 0.5, 0.5]
+    )
+])
+
+train_dataset = torchvision.datasets.ImageFolder(root='./Vegetable Images/train', transform=train_transform)
+
+validation_dataset = torchvision.datasets.ImageFolder(root='./Vegetable Images/validation', transform=valid_transform)
+
+train_loader = DataLoader(
+    train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=0, pin_memory=True
+)
+
+valid_loader = DataLoader(
+    validation_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=0, pin_memory=True
+)

neural_network/inference.py

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+import torch
+import cv2
+import torchvision.transforms as transforms
+import argparse
+from model import CNNModel
+# construct the argument parser
+parser = argparse.ArgumentParser()
+parser.add_argument('-i', '--input', 
+    default='',
+    help='path to the input image')
+args = vars(parser.parse_args())
+
+# the computation device
+device = ('cuda' if torch.cuda.is_available() else 'cpu')
+# list containing all the class labels
+labels = [
+    'bean', 'bitter gourd', 'bottle gourd', 'brinjal', 'broccoli',
+    'cabbage', 'capsicum', 'carrot', 'cauliflower', 'cucumber',
+    'papaya', 'potato', 'pumpkin', 'radish', 'tomato'
+    ]
+
+# initialize the model and load the trained weights
+model = CNNModel().to(device)
+checkpoint = torch.load('outputs/model.pth', map_location=device)
+model.load_state_dict(checkpoint['model_state_dict'])
+model.eval()
+
+# define preprocess transforms
+transform = transforms.Compose([
+    transforms.ToPILImage(),
+    transforms.Resize(224),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=[0.5, 0.5, 0.5],
+        std=[0.5, 0.5, 0.5]
+    )
+])  
+
+
+# read and preprocess the image
+image = cv2.imread(args['input'])
+# get the ground truth class
+gt_class = args['input'].split('/')[-2]
+orig_image = image.copy()
+# convert to RGB format
+image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+image = transform(image)
+# add batch dimension
+image = torch.unsqueeze(image, 0)
+with torch.no_grad():
+    outputs = model(image.to(device))
+output_label = torch.topk(outputs, 1)
+pred_class = labels[int(output_label.indices)]
+cv2.putText(orig_image, 
+    f"GT: {gt_class}",
+    (10, 25),
+    cv2.FONT_HERSHEY_SIMPLEX, 
+    0.6, (0, 255, 0), 2, cv2.LINE_AA
+)
+cv2.putText(orig_image, 
+    f"Pred: {pred_class}",
+    (10, 55),
+    cv2.FONT_HERSHEY_SIMPLEX, 
+    0.6, (0, 0, 255), 2, cv2.LINE_AA
+)
+print(f"GT: {gt_class}, pred: {pred_class}")
+cv2.imshow('Result', orig_image)
+cv2.waitKey(0)
+cv2.imwrite(f"outputs/{gt_class}{args['input'].split('/')[-1].split('.')[0]}.png",
+    orig_image)

neural_network/model.py

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+import torch.nn as nn
+import torch.nn.functional as F
+
+class CNNModel(nn.Module): #model of the CNN type
+    def __init__(self):
+        super(CNNModel, self).__init__()
+        self.conv1 = nn.Conv2d(3, 32, 5)
+        self.conv2 = nn.Conv2d(32, 64, 5)
+        self.conv3 = nn.Conv2d(64, 128, 3)
+        self.conv4 = nn.Conv2d(128, 256, 5)
+        
+        self.fc1 = nn.Linear(256, 50)
+        
+        self.pool = nn.MaxPool2d(2, 2)
+        
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = self.pool(F.relu(self.conv3(x)))
+        x = self.pool(F.relu(self.conv4(x)))
+        bs, _, _, _ = x.shape
+        x = F.adaptive_avg_pool2d(x, 1).reshape(bs, -1)
+        x = self.fc1(x)
+        return x

neural_network/train.py

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+import torch
+import argparse
+import torch.nn as nn
+import torch.optim as optim
+import time
+from tqdm.auto import tqdm
+from model import CNNModel
+from datasets import train_loader, valid_loader
+from utils import save_model, save_plots
+
+# construct the argument parser
+parser = argparse.ArgumentParser()
+parser.add_argument('-e', '--epochs', type=int, default=20,
+    help='number of epochs to train our network for')
+args = vars(parser.parse_args())
+
+
+lr = 1e-3
+epochs = args['epochs']
+device = ('cuda' if torch.cuda.is_available() else 'cpu')
+print(f"Computation device: {device}\n")
+
+model = CNNModel().to(device)
+print(model)
+
+total_params = sum(p.numel() for p in model.parameters())
+print(f"{total_params:,} total parameters.")
+total_trainable_params = sum(
+    p.numel() for p in model.parameters() if p.requires_grad)
+print(f"{total_trainable_params:,} training parameters.")
+# optimizer
+optimizer = optim.Adam(model.parameters(), lr=lr)
+# loss function
+criterion = nn.CrossEntropyLoss()
+
+
+# training
+def train(model, trainloader, optimizer, criterion):
+    model.train()
+    print('Training')
+    train_running_loss = 0.0
+    train_running_correct = 0
+    counter = 0
+    for i, data in tqdm(enumerate(trainloader), total=len(trainloader)):
+        counter += 1
+        image, labels = data
+        image = image.to(device)
+        labels = labels.to(device)
+        optimizer.zero_grad()
+        # forward pass
+        outputs = model(image)
+        # calculate the loss
+        loss = criterion(outputs, labels)
+        train_running_loss += loss.item()
+        # calculate the accuracy
+        _, preds = torch.max(outputs.data, 1)
+        train_running_correct += (preds == labels).sum().item()
+        # backpropagation
+        loss.backward()
+        # update the optimizer parameters
+        optimizer.step()
+    
+    # loss and accuracy for the complete epoch
+    epoch_loss = train_running_loss / counter
+    epoch_acc = 100. * (train_running_correct / len(trainloader.dataset))
+    return epoch_loss, epoch_acc
+
+# validation
+def validate(model, testloader, criterion):
+    model.eval()
+    print('Validation')
+    valid_running_loss = 0.0
+    valid_running_correct = 0
+    counter = 0
+    with torch.no_grad():
+        for i, data in tqdm(enumerate(testloader), total=len(testloader)):
+            counter += 1
+            
+            image, labels = data
+            image = image.to(device)
+            labels = labels.to(device)
+            # forward pass
+            outputs = model(image)
+            # calculate the loss
+            loss = criterion(outputs, labels)
+            valid_running_loss += loss.item()
+            # calculate the accuracy
+            _, preds = torch.max(outputs.data, 1)
+            valid_running_correct += (preds == labels).sum().item()
+        
+    # loss and accuracy for the complete epoch
+    epoch_loss = valid_running_loss / counter
+    epoch_acc = 100. * (valid_running_correct / len(testloader.dataset))
+    return epoch_loss, epoch_acc
+
+# lists to keep track of losses and accuracies
+train_loss, valid_loss = [], []
+train_acc, valid_acc = [], []
+# start the training
+for epoch in range(epochs):
+    print(f"[INFO]: Epoch {epoch+1} of {epochs}")
+    train_epoch_loss, train_epoch_acc = train(model, train_loader, 
+                                              optimizer, criterion)
+    valid_epoch_loss, valid_epoch_acc = validate(model, valid_loader,  
+                                                 criterion)
+    train_loss.append(train_epoch_loss)
+    valid_loss.append(valid_epoch_loss)
+    train_acc.append(train_epoch_acc)
+    valid_acc.append(valid_epoch_acc)
+    print(f"Training loss: {train_epoch_loss:.3f}, training acc: {train_epoch_acc:.3f}")
+    print(f"Validation loss: {valid_epoch_loss:.3f}, validation acc: {valid_epoch_acc:.3f}")
+    print('-'*50)
+    time.sleep(5)
+    
+# save the trained model weights
+save_model(epochs, model, optimizer, criterion)
+# save the loss and accuracy plots
+save_plots(train_acc, valid_acc, train_loss, valid_loss)
+print('TRAINING COMPLETE')

neural_network/utils.py

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+import torch
+import matplotlib
+import matplotlib.pyplot as plt
+matplotlib.style.use('ggplot')
+
+def save_model(epochs, model, optimizer, criterion):
+    """
+    Function to save the trained model to disk.
+    """
+    torch.save({
+                'epoch': epochs,
+                'model_state_dict': model.state_dict(),
+                'optimizer_state_dict': optimizer.state_dict(),
+                'loss': criterion,
+                }, 'outputs/model.pth')
+    
+def save_plots(train_acc, valid_acc, train_loss, valid_loss):
+    """
+    Function to save the loss and accuracy plots to disk.
+    """
+    # accuracy plots
+    plt.figure(figsize=(10, 7))
+    plt.plot(
+        train_acc, color='green', linestyle='-', 
+        label='train accuracy'
+    )
+    plt.plot(
+        valid_acc, color='blue', linestyle='-', 
+        label='validataion accuracy'
+    )
+    plt.xlabel('Epochs')
+    plt.ylabel('Accuracy')
+    plt.legend()
+    plt.savefig('outputs/accuracy.png')
+    
+    # loss plots
+    plt.figure(figsize=(10, 7))
+    plt.plot(
+        train_loss, color='orange', linestyle='-', 
+        label='train loss'
+    )
+    plt.plot(
+        valid_loss, color='red', linestyle='-', 
+        label='validataion loss'
+    )
+    plt.xlabel('Epochs')
+    plt.ylabel('Loss')
+    plt.legend()
+    plt.savefig('outputs/loss.png')