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Plotting utilities for PyTorch Playground demos.
Provides consistent, styled plots for training metrics, tensors, etc.
"""
from typing import List, Optional, Dict, Any, Tuple
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
# Use non-interactive backend for Gradio
matplotlib.use("Agg")
# Consistent style
COLORS = {
"primary": "#ee4c2c", # PyTorch orange
"secondary": "#29b6f6", # Light blue
"tertiary": "#66bb6a", # Green
"quaternary": "#ab47bc", # Purple
"loss": "#ef5350", # Red
"accuracy": "#66bb6a", # Green
"lr": "#ff9800", # Orange
}
def setup_plot_style():
"""Apply consistent plot styling."""
plt.style.use("seaborn-v0_8-whitegrid")
plt.rcParams.update(
{
"figure.facecolor": "white",
"axes.facecolor": "white",
"axes.edgecolor": "#cccccc",
"axes.labelcolor": "#333333",
"text.color": "#333333",
"xtick.color": "#666666",
"ytick.color": "#666666",
"grid.color": "#eeeeee",
"font.size": 10,
"axes.titlesize": 12,
"axes.labelsize": 10,
}
)
def create_loss_plot(
losses: List[float],
val_losses: Optional[List[float]] = None,
title: str = "Training Loss",
figsize: Tuple[int, int] = (8, 5),
) -> plt.Figure:
"""
Create a training loss plot.
Args:
losses: List of training loss values
val_losses: Optional list of validation loss values
title: Plot title
figsize: Figure size
Returns:
matplotlib Figure object
"""
setup_plot_style()
fig, ax = plt.subplots(figsize=figsize)
epochs = range(1, len(losses) + 1)
ax.plot(epochs, losses, color=COLORS["loss"], linewidth=2, label="Training Loss")
if val_losses:
ax.plot(
epochs,
val_losses,
color=COLORS["secondary"],
linewidth=2,
linestyle="--",
label="Validation Loss",
)
ax.set_xlabel("Epoch")
ax.set_ylabel("Loss")
ax.set_title(title)
ax.legend()
# Set y-axis to start at 0 if all losses are positive
if min(losses) >= 0:
ax.set_ylim(bottom=0)
plt.tight_layout()
return fig
def create_metrics_plot(
metrics: Dict[str, List[float]],
title: str = "Training Metrics",
figsize: Tuple[int, int] = (10, 5),
) -> plt.Figure:
"""
Create a multi-metric plot.
Args:
metrics: Dictionary of metric name -> values
title: Plot title
figsize: Figure size
Returns:
matplotlib Figure object
"""
setup_plot_style()
fig, axes = plt.subplots(1, len(metrics), figsize=figsize)
if len(metrics) == 1:
axes = [axes]
colors = list(COLORS.values())
for idx, (name, values) in enumerate(metrics.items()):
ax = axes[idx]
epochs = range(1, len(values) + 1)
ax.plot(epochs, values, color=colors[idx % len(colors)], linewidth=2)
ax.set_xlabel("Epoch")
ax.set_ylabel(name.capitalize())
ax.set_title(name.capitalize())
plt.suptitle(title)
plt.tight_layout()
return fig
def create_confusion_matrix_plot(
cm: np.ndarray,
class_names: Optional[List[str]] = None,
title: str = "Confusion Matrix",
figsize: Tuple[int, int] = (8, 6),
cmap: str = "Blues",
) -> plt.Figure:
"""
Create a confusion matrix heatmap.
Args:
cm: Confusion matrix array
class_names: Optional list of class names
title: Plot title
figsize: Figure size
cmap: Colormap name
Returns:
matplotlib Figure object
"""
setup_plot_style()
fig, ax = plt.subplots(figsize=figsize)
im = ax.imshow(cm, interpolation="nearest", cmap=cmap)
ax.figure.colorbar(im, ax=ax)
if class_names is None:
class_names = [str(i) for i in range(len(cm))]
ax.set(
xticks=np.arange(len(class_names)),
yticks=np.arange(len(class_names)),
xticklabels=class_names,
yticklabels=class_names,
ylabel="True label",
xlabel="Predicted label",
title=title,
)
plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor")
# Add text annotations
thresh = cm.max() / 2.0
for i in range(len(class_names)):
for j in range(len(class_names)):
ax.text(
j,
i,
format(cm[i, j], "d"),
ha="center",
va="center",
color="white" if cm[i, j] > thresh else "black",
)
plt.tight_layout()
return fig
def create_tensor_visualization(
tensor: "torch.Tensor",
title: str = "Tensor Visualization",
figsize: Tuple[int, int] = (8, 6),
) -> plt.Figure:
"""
Create a visualization of a tensor.
Args:
tensor: PyTorch tensor to visualize
title: Plot title
figsize: Figure size
Returns:
matplotlib Figure object
"""
import torch
setup_plot_style()
# Convert to numpy for plotting
data = tensor.detach().cpu().numpy()
if data.ndim == 1:
# 1D: Bar chart
fig, ax = plt.subplots(figsize=figsize)
ax.bar(range(len(data)), data, color=COLORS["primary"])
ax.set_xlabel("Index")
ax.set_ylabel("Value")
ax.set_title(f"{title} (1D Tensor)")
elif data.ndim == 2:
# 2D: Heatmap
fig, ax = plt.subplots(figsize=figsize)
im = ax.imshow(data, cmap="RdYlBu_r", aspect="auto")
ax.figure.colorbar(im, ax=ax)
ax.set_xlabel("Column")
ax.set_ylabel("Row")
ax.set_title(f"{title} (2D Tensor: {data.shape})")
elif data.ndim == 3:
# 3D: Show as image if channel-like, else show slices
if data.shape[0] in [1, 3, 4]: # Likely CHW format
if data.shape[0] == 1:
data = data[0] # Squeeze channel dim
fig, ax = plt.subplots(figsize=figsize)
im = ax.imshow(data, cmap="gray")
ax.set_title(f"{title} (Grayscale Image)")
else:
# RGB(A) image
if data.shape[0] == 3:
data = np.transpose(data, (1, 2, 0))
else:
data = np.transpose(data, (1, 2, 0))[:, :, :3]
# Normalize to 0-1
data = (data - data.min()) / (data.max() - data.min() + 1e-8)
fig, ax = plt.subplots(figsize=figsize)
ax.imshow(data)
ax.set_title(f"{title} (RGB Image)")
else:
# Show first slice
fig, ax = plt.subplots(figsize=figsize)
im = ax.imshow(data[0], cmap="viridis", aspect="auto")
ax.figure.colorbar(im, ax=ax)
ax.set_title(f"{title} (3D Tensor slice 0 of {data.shape[0]})")
elif data.ndim == 4:
# 4D: Show grid of first batch items
n_show = min(4, data.shape[0])
fig, axes = plt.subplots(1, n_show, figsize=(figsize[0], figsize[1] // 2))
if n_show == 1:
axes = [axes]
for i, ax in enumerate(axes):
if data.shape[1] in [1, 3]:
img = data[i]
if img.shape[0] == 1:
img = img[0]
ax.imshow(img, cmap="gray")
else:
img = np.transpose(img, (1, 2, 0))
img = (img - img.min()) / (img.max() - img.min() + 1e-8)
ax.imshow(img)
else:
ax.imshow(data[i, 0], cmap="viridis")
ax.set_title(f"Sample {i}")
ax.axis("off")
fig.suptitle(f"{title} (4D Tensor: {data.shape})")
else:
# Higher dimensions: just show stats
fig, ax = plt.subplots(figsize=figsize)
ax.text(
0.5,
0.5,
f"Tensor shape: {data.shape}\n"
f"Min: {data.min():.4f}\n"
f"Max: {data.max():.4f}\n"
f"Mean: {data.mean():.4f}\n"
f"Std: {data.std():.4f}",
transform=ax.transAxes,
fontsize=12,
verticalalignment="center",
horizontalalignment="center",
)
ax.set_title(f"{title} (High-dimensional Tensor)")
ax.axis("off")
plt.tight_layout()
return fig
def create_lr_schedule_plot(
lrs: List[float],
title: str = "Learning Rate Schedule",
figsize: Tuple[int, int] = (8, 4),
) -> plt.Figure:
"""
Plot learning rate schedule.
Args:
lrs: List of learning rate values
title: Plot title
figsize: Figure size
Returns:
matplotlib Figure object
"""
setup_plot_style()
fig, ax = plt.subplots(figsize=figsize)
ax.plot(range(len(lrs)), lrs, color=COLORS["lr"], linewidth=2)
ax.set_xlabel("Step")
ax.set_ylabel("Learning Rate")
ax.set_title(title)
ax.set_yscale("log")
plt.tight_layout()
return fig
def create_timing_comparison_plot(
results: Dict[str, float],
title: str = "Timing Comparison",
figsize: Tuple[int, int] = (8, 5),
) -> plt.Figure:
"""
Create a bar chart comparing timing results.
Args:
results: Dictionary of method name -> time in ms
title: Plot title
figsize: Figure size
Returns:
matplotlib Figure object
"""
setup_plot_style()
fig, ax = plt.subplots(figsize=figsize)
names = list(results.keys())
times = list(results.values())
colors = [COLORS["primary"], COLORS["secondary"], COLORS["tertiary"]][: len(names)]
bars = ax.bar(names, times, color=colors)
ax.set_ylabel("Time (ms)")
ax.set_title(title)
# Add value labels on bars
for bar, time in zip(bars, times):
height = bar.get_height()
ax.annotate(
f"{time:.2f}ms",
xy=(bar.get_x() + bar.get_width() / 2, height),
xytext=(0, 3),
textcoords="offset points",
ha="center",
va="bottom",
fontsize=10,
)
plt.tight_layout()
return fig
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