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import os
import json
import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLoader, random_split
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from PIL import Image
import requests
import matplotlib.pyplot as plt
# Ensure that matplotlib does not try to open a window (useful if running on a server)
import matplotlib
matplotlib.use('Agg')
# Check if CUDA is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f'Using device: {device}')
def load_config(config_file='config.json'):
"""
Loads configuration parameters from a JSON file.
Args:
config_file (str): Path to the JSON config file.
Returns:
config (dict): Dictionary containing configuration parameters.
"""
with open(config_file, 'r') as f:
return json.load(f)
def download_quickdraw_data():
"""
Downloads 'cat.npy' and 'dog.npy' files from the Quick, Draw! dataset.
"""
os.makedirs('quickdraw_data', exist_ok=True)
base_url = 'https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/'
categories = ['cat', 'dog']
for category in categories:
url = f"{base_url}{category}.npy"
save_path = os.path.join('quickdraw_data', f"{category}.npy")
if os.path.exists(save_path):
print(f"{category}.npy already exists, skipping download.")
continue
print(f"Downloading {category}.npy...")
response = requests.get(url, stream=True)
if response.status_code == 200:
with open(save_path, 'wb') as f:
for chunk in response.iter_content(chunk_size=8192):
f.write(chunk)
print(f"Downloaded {category}.npy")
else:
print(f"Failed to download {category}.npy. Status code: {response.status_code}")
def load_and_preprocess_data(num_samples=5000):
"""
Loads and preprocesses the data for 'cat' and 'dog' categories.
Args:
num_samples (int): Number of samples to load for each category.
Returns:
train_loader, test_loader: DataLoaders for training and testing.
"""
# Load data
cat_data = np.load('quickdraw_data/cat.npy')
dog_data = np.load('quickdraw_data/dog.npy')
# Limit the number of samples
cat_data = cat_data[:num_samples]
dog_data = dog_data[:num_samples]
# Create labels: 0 for cat, 1 for dog
cat_labels = np.zeros(len(cat_data), dtype=np.int64)
dog_labels = np.ones(len(dog_data), dtype=np.int64)
# Combine data and labels
data = np.concatenate((cat_data, dog_data), axis=0)
labels = np.concatenate((cat_labels, dog_labels), axis=0)
# Normalize data
data = data.astype('float32') / 255.0
# Reshape data for PyTorch: (batch_size, channels, height, width)
data = data.reshape(-1, 1, 28, 28)
# Convert to PyTorch tensors
data_tensor = torch.tensor(data)
labels_tensor = torch.tensor(labels)
# Create a TensorDataset
dataset = TensorDataset(data_tensor, labels_tensor)
# Split dataset into training and testing sets (80% train, 20% test)
train_size = int(0.8 * len(dataset))
test_size = len(dataset) - train_size
train_dataset, test_dataset = random_split(dataset, [train_size, test_size])
# Create DataLoaders
config = load_config()
batch_size = config['batch_size']
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size)
return train_loader, test_loader
class SimpleCNN(nn.Module):
"""
Defines a simple Convolutional Neural Network for binary classification.
"""
def __init__(self):
super(SimpleCNN, self).__init__()
# Convolutional layers
self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
# Fully connected layers
self.fc1 = nn.Linear(64 * 7 * 7, 128)
self.fc2 = nn.Linear(128, 2) # 2 output classes: cat and dog
def forward(self, x):
x = F.relu(self.conv1(x)) # Convolutional layer 1
x = self.pool(x) # Max pooling
x = F.relu(self.conv2(x)) # Convolutional layer 2
x = self.pool(x) # Max pooling
x = x.view(-1, 64 * 7 * 7) # Flatten
x = F.relu(self.fc1(x)) # Fully connected layer 1
x = self.fc2(x) # Output layer
return x
def train_model(model, train_loader, num_epochs=5, learning_rate=0.001):
"""
Trains the model using the training DataLoader.
Args:
model: The neural network model to train.
train_loader: DataLoader for the training data.
num_epochs (int): Number of epochs to train.
learning_rate (float): Learning rate for the optimizer.
"""
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
model.train()
for epoch in range(num_epochs):
running_loss = 0.0
for images, labels in train_loader:
images = images.to(device)
labels = labels.to(device)
# Zero the parameter gradients
optimizer.zero_grad()
# Forward pass
outputs = model(images)
loss = criterion(outputs, labels)
# Backward pass and optimize
loss.backward()
optimizer.step()
running_loss += loss.item() * images.size(0)
epoch_loss = running_loss / len(train_loader.dataset)
print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {epoch_loss:.4f}')
def evaluate_model(model, test_loader):
"""
Evaluates the model on the test DataLoader.
Args:
model: The trained neural network model.
test_loader: DataLoader for the test data.
"""
model.eval()
correct = 0
total = 0
with torch.no_grad():
for images, labels in test_loader:
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
accuracy = 100 * correct / total
print(f'Test Accuracy: {accuracy:.2f}%')
def save_model(model, filepath='cat_dog_classifier.pth'):
"""
Saves the trained model to a file.
Args:
model: The trained neural network model.
filepath (str): The path where the model will be saved.
"""
torch.save(model.state_dict(), filepath)
print(f'Model saved to {filepath}')
def load_model(model, filepath='cat_dog_classifier.pth'):
"""
Loads the model parameters from a file.
Args:
model: The neural network model to load parameters into.
filepath (str): The path to the saved model file.
"""
model.load_state_dict(torch.load(filepath, map_location=device))
model.to(device)
print(f'Model loaded from {filepath}')
def predict_image(model, image):
"""
Predicts the class of a single image.
Args:
model: The trained neural network model.
image: A PIL Image or NumPy array.
Returns:
prediction (str): The predicted class label ('cat' or 'dog').
"""
# Preprocess the image
if isinstance(image, Image.Image):
image = image.resize((28, 28)).convert('L')
image = np.array(image).astype('float32') / 255.0
elif isinstance(image, np.ndarray):
if image.shape != (28, 28):
image = Image.fromarray(image).resize((28, 28)).convert('L')
image = np.array(image).astype('float32') / 255.0
else:
raise ValueError("Image must be a PIL Image or NumPy array.")
image = image.reshape(1, 1, 28, 28)
image_tensor = torch.tensor(image).to(device)
# Get prediction
model.eval()
with torch.no_grad():
output = model(image_tensor)
_, predicted = torch.max(output.data, 1)
return 'cat' if predicted.item() == 0 else 'dog'
def visualize_predictions(model, test_loader, num_images=8):
"""
Visualizes sample predictions from the test set.
Args:
model: The trained neural network model.
test_loader: DataLoader for the test data.
num_images (int): Number of images to display.
"""
model.eval()
dataiter = iter(test_loader)
images, labels = next(dataiter) # Use the built-in next() function
images = images.to(device)
labels = labels.to(device)
# Get predictions
outputs = model(images)
_, predicted = torch.max(outputs, 1)
# Move images to CPU for plotting
images = images.cpu().numpy()
predicted = predicted.cpu().numpy()
labels = labels.cpu().numpy()
# Plot the images with predicted and true labels
fig = plt.figure(figsize=(10, 4))
for idx in range(num_images):
ax = fig.add_subplot(2, num_images // 2, idx+1)
img = images[idx][0]
ax.imshow(img, cmap='gray')
pred_label = 'cat' if predicted[idx] == 0 else 'dog'
true_label = 'cat' if labels[idx] == 0 else 'dog'
ax.set_title(f'Pred: {pred_label}\nTrue: {true_label}')
ax.axis('off')
plt.tight_layout()
plt.savefig('sample_predictions.png')
print('Sample predictions saved to sample_predictions.png')
def main():
# Load configuration
config = load_config()
# Step 1: Download the data
download_quickdraw_data()
# Step 2: Load and preprocess the data
train_loader, test_loader = load_and_preprocess_data(num_samples=config['num_samples'])
# Step 3: Initialize the model
model = SimpleCNN().to(device)
# Step 4: Train the model
train_model(model, train_loader, num_epochs=config['num_epochs'], learning_rate=config['learning_rate'])
# Step 5: Evaluate the model
evaluate_model(model, test_loader)
# Step 6: Visualize sample predictions
visualize_predictions(model, test_loader, num_images=8)
# Step 7: Save the model
save_model(model, config['model_save_path'])
if __name__ == '__main__':
main()
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