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"""
Visualization utilities for architectural style classification.
"""
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import torch
from typing import Dict, List, Optional, Tuple
def plot_attention_weights(attention_weights: torch.Tensor,
image: torch.Tensor,
title: str = "Attention Weights",
save_path: Optional[str] = None) -> None:
"""
Plot attention weights overlaid on the image.
Args:
attention_weights: Attention weights tensor [H, W]
image: Input image tensor [C, H, W]
title: Plot title
save_path: Path to save the plot
"""
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))
# Plot original image
img_np = image.permute(1, 2, 0).numpy()
img_np = (img_np - img_np.min()) / (img_np.max() - img_np.min())
ax1.imshow(img_np)
ax1.set_title("Original Image")
ax1.axis('off')
# Plot attention weights
attention_np = attention_weights.numpy()
im = ax2.imshow(attention_np, cmap='hot', alpha=0.7)
ax2.imshow(img_np, alpha=0.3)
ax2.set_title(title)
ax2.axis('off')
plt.colorbar(im, ax=ax2)
plt.tight_layout()
if save_path:
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
def plot_training_curves(train_losses: List[float],
val_losses: List[float],
train_accuracies: Optional[List[float]] = None,
val_accuracies: Optional[List[float]] = None,
save_path: Optional[str] = None) -> None:
"""
Plot training and validation curves.
Args:
train_losses: Training losses per epoch
val_losses: Validation losses per epoch
train_accuracies: Training accuracies per epoch
val_accuracies: Validation accuracies per epoch
save_path: Path to save the plot
"""
epochs = range(1, len(train_losses) + 1)
if train_accuracies and val_accuracies:
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
# Plot losses
ax1.plot(epochs, train_losses, 'b-', label='Training Loss')
ax1.plot(epochs, val_losses, 'r-', label='Validation Loss')
ax1.set_title('Training and Validation Loss')
ax1.set_xlabel('Epoch')
ax1.set_ylabel('Loss')
ax1.legend()
ax1.grid(True)
# Plot accuracies
ax2.plot(epochs, train_accuracies, 'b-', label='Training Accuracy')
ax2.plot(epochs, val_accuracies, 'r-', label='Validation Accuracy')
ax2.set_title('Training and Validation Accuracy')
ax2.set_xlabel('Epoch')
ax2.set_ylabel('Accuracy')
ax2.legend()
ax2.grid(True)
else:
fig, ax = plt.subplots(1, 1, figsize=(10, 6))
ax.plot(epochs, train_losses, 'b-', label='Training Loss')
ax.plot(epochs, val_losses, 'r-', label='Validation Loss')
ax.set_title('Training and Validation Loss')
ax.set_xlabel('Epoch')
ax.set_ylabel('Loss')
ax.legend()
ax.grid(True)
plt.tight_layout()
if save_path:
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
def plot_confusion_matrix(confusion_matrix: np.ndarray,
class_names: List[str],
title: str = "Confusion Matrix",
save_path: Optional[str] = None) -> None:
"""
Plot confusion matrix.
Args:
confusion_matrix: Confusion matrix array
class_names: List of class names
title: Plot title
save_path: Path to save the plot
"""
plt.figure(figsize=(10, 8))
sns.heatmap(confusion_matrix, annot=True, fmt='d', cmap='Blues',
xticklabels=class_names, yticklabels=class_names)
plt.title(title)
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.xticks(rotation=45)
plt.yticks(rotation=0)
if save_path:
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
def plot_class_distribution(class_counts: Dict[str, int],
title: str = "Class Distribution",
save_path: Optional[str] = None) -> None:
"""
Plot class distribution.
Args:
class_counts: Dictionary mapping class names to counts
title: Plot title
save_path: Path to save the plot
"""
classes = list(class_counts.keys())
counts = list(class_counts.values())
plt.figure(figsize=(12, 6))
bars = plt.bar(classes, counts)
plt.title(title)
plt.xlabel('Class')
plt.ylabel('Count')
plt.xticks(rotation=45)
# Add count labels on bars
for bar, count in zip(bars, counts):
plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,
str(count), ha='center', va='bottom')
plt.tight_layout()
if save_path:
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
def plot_feature_maps(feature_maps: torch.Tensor,
title: str = "Feature Maps",
num_maps: int = 16,
save_path: Optional[str] = None) -> None:
"""
Plot feature maps from a convolutional layer.
Args:
feature_maps: Feature maps tensor [B, C, H, W]
title: Plot title
num_maps: Number of feature maps to display
save_path: Path to save the plot
"""
# Take first batch and select first num_maps channels
maps = feature_maps[0, :num_maps].detach().cpu()
# Calculate grid size
grid_size = int(np.ceil(np.sqrt(num_maps)))
fig, axes = plt.subplots(grid_size, grid_size, figsize=(12, 12))
axes = axes.flatten()
for i in range(num_maps):
if i < maps.shape[0]:
im = axes[i].imshow(maps[i], cmap='viridis')
axes[i].set_title(f'Feature Map {i+1}')
axes[i].axis('off')
else:
axes[i].axis('off')
plt.suptitle(title)
plt.tight_layout()
if save_path:
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
def plot_learning_rate_schedule(learning_rates: List[float],
steps: List[int],
title: str = "Learning Rate Schedule",
save_path: Optional[str] = None) -> None:
"""
Plot learning rate schedule.
Args:
learning_rates: List of learning rates
steps: List of step numbers
title: Plot title
save_path: Path to save the plot
"""
plt.figure(figsize=(10, 6))
plt.plot(steps, learning_rates, 'b-', linewidth=2)
plt.title(title)
plt.xlabel('Step')
plt.ylabel('Learning Rate')
plt.yscale('log')
plt.grid(True)
if save_path:
plt.savefig(save_path, dpi=300, bbox_inches='tight')
plt.show()
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