Spaces:
Sleeping
Sleeping
File size: 4,351 Bytes
9e6a96a b0ae40a 9e6a96a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 |
import torch
import torch.nn as nn
import torch.optim as optim
from utils.data_loader import get_data_loaders
from models.resnet_model import MonkeyResNet
import os
import matplotlib.pyplot as plt
from sklearn.utils.class_weight import compute_class_weight
import numpy as np
# This class helps stop training early if validation loss stops improving
class EarlyStopping:
def __init__(self, patience=5):
self.patience = patience
self.counter = 0
self.best_loss = float('inf')
self.early_stop = False
def __call__(self, val_loss):
if val_loss < self.best_loss:
self.best_loss = val_loss
self.counter = 0
else:
self.counter += 1
if self.counter >= self.patience:
self.early_stop = True
# Hyperparameters
data_dir = "data"
epochs = 50
batch_size = 32
lr = 0.001
patience = 5
# Load training and validation data
train_loader, val_loader, class_names = get_data_loaders(data_dir, batch_size)
# Use GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Calculate class weights
train_labels = []
for _, labels in train_loader:
train_labels.extend(labels.numpy())
train_labels = np.array(train_labels)
class_weights = compute_class_weight(
class_weight='balanced',
classes=np.unique(train_labels),
y=train_labels
)
class_weights = torch.tensor(class_weights, dtype=torch.float).to(device)
# Set up model, loss function, optimizer, scheduler
model = MonkeyResNet(num_classes=len(class_names)).to(device)
criterion = nn.CrossEntropyLoss(weight=class_weights)
optimizer = optim.Adam(model.parameters(), lr=lr)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=2)
early_stopper = EarlyStopping(patience=patience)
# Store values for plotting
train_losses, val_losses = [], []
train_accuracies, val_accuracies = [], []
# Start training loop
for epoch in range(epochs):
model.train()
train_loss = 0
correct = 0
total = 0
for images, labels in train_loader:
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
train_accuracy = 100 * correct / total
train_losses.append(train_loss)
train_accuracies.append(train_accuracy)
# Validation step
model.eval()
val_loss = 0
correct_val = 0
total_val = 0
with torch.no_grad():
for images, labels in val_loader:
images, labels = images.to(device), labels.to(device)
outputs = model(images)
loss = criterion(outputs, labels)
val_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
total_val += labels.size(0)
correct_val += (predicted == labels).sum().item()
val_accuracy = 100 * correct_val / total_val
val_losses.append(val_loss)
val_accuracies.append(val_accuracy)
scheduler.step(val_loss)
early_stopper(val_loss)
print(f"Epoch {epoch+1}/{epochs} - Train Loss: {train_loss:.4f} - Val Loss: {val_loss:.4f} - Train Acc: {train_accuracy:.2f}%")
if early_stopper.early_stop:
print(f"Early stopping triggered at epoch {epoch+1}")
break
# Save the trained model
os.makedirs("models", exist_ok=True)
torch.save(model.state_dict(), "models/monkey_resnet.pth")
print("Training done. Model saved.")
# Save training and validation plots
os.makedirs("plots", exist_ok=True)
# Loss plot
plt.figure(figsize=(10, 5))
plt.plot(train_losses, label="Train Loss")
plt.plot(val_losses, label="Val Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.title("Training and Validation Loss")
plt.legend()
plt.grid(True)
plt.savefig("plots/loss_plot.png")
plt.close()
# Accuracy plot
plt.figure(figsize=(10, 5))
plt.plot(train_accuracies, label="Train Accuracy")
plt.plot(val_accuracies, label="Val Accuracy")
plt.xlabel("Epoch")
plt.ylabel("Accuracy (%)")
plt.title("Training and Validation Accuracy")
plt.legend()
plt.grid(True)
plt.savefig("plots/accuracy_plot.png")
plt.close() |