# Copyright (c) 2025 GeoBenchV2. All rights reserved.
# Licensed under the Apache License 2.0.
"""SpaceNet7 DataModule."""
import os
from collections.abc import Callable, Sequence
from typing import Any
import matplotlib.pyplot as plt
import pandas as pd
import tacoreader
import torch
import torch.nn as nn
from einops import rearrange
from matplotlib.colors import ListedColormap
from torchgeo.datasets.utils import percentile_normalization
from geobench_v2.datasets import GeoBenchSpaceNet7
from .base import GeoBenchSegmentationDataModule
[docs]
class GeoBenchSpaceNet7DataModule(GeoBenchSegmentationDataModule):
"""GeoBench SpaceNet7 Data Module."""
#
[docs]
def __init__(
self,
img_size: int = 512,
band_order: Sequence[float | str] = GeoBenchSpaceNet7.band_default_order,
batch_size: int = 32,
eval_batch_size: int = 64,
num_workers: int = 0,
collate_fn: Callable | None = None,
train_augmentations: nn.Module | None = None,
eval_augmentations: nn.Module | None = None,
pin_memory: bool = False,
**kwargs: Any,
) -> None:
"""Initialize GeoBench SpaceNet7 dataset module.
Args:
img_size: Image size, created patches are of size 512
band_order: The order of bands to return in the sample
batch_size: Batch size during training
eval_batch_size: Evaluation batch size
num_workers: Number of workers
collate_fn: Collate function
train_augmentations: Transforms/Augmentations to apply during training, they will be applied
at the sample level and should include normalization. See :meth:`define_augmentations`
for the default transformation.
eval_augmentations: Transforms/Augmentations to apply during evaluation, they will be applied
at the sample level and should include normalization. See :meth:`define_augmentations`
for the default transformation.
pin_memory: Pin memory
**kwargs: Additional keyword arguments for :class:`geobench_v2.datasets.spacenet7.GeoBenchSpaceNet7`
"""
super().__init__(
dataset_class=GeoBenchSpaceNet7,
img_size=img_size,
band_order=band_order,
batch_size=batch_size,
eval_batch_size=eval_batch_size,
num_workers=num_workers,
collate_fn=collate_fn,
train_augmentations=train_augmentations,
eval_augmentations=eval_augmentations,
pin_memory=pin_memory,
**kwargs,
)
[docs]
def visualize_batch(
self, batch: dict[str, Any] | None = None, split: str = "train"
) -> tuple[Any, dict[str, Any]]:
"""Visualize a batch of data.
Args:
batch: Optional batch of data to visualize. If not provided, a batch will be fetched
from the dataloader.
split: One of 'train', 'validation', 'test'
Returns:
The matplotlib figure and the batch of data
Raises:
AssertionError: If bands needed for plotting are missing
"""
if split == "train":
batch = next(iter(self.train_dataloader()))
elif split == "validation":
batch = next(iter(self.val_dataloader()))
else:
batch = next(iter(self.test_dataloader()))
if hasattr(self.data_normalizer, "unnormalize"):
batch = self.data_normalizer.unnormalize(batch)
images = batch["image"]
masks = batch["mask"]
n_samples = min(8, images.shape[0])
indices = torch.randperm(images.shape[0])[:n_samples]
images = images[indices]
masks = masks[indices]
plot_bands = self.dataset_band_config.plot_bands
rgb_indices = [
self.band_order.index(band)
for band in plot_bands
if band in self.band_order
]
images = images[:, rgb_indices, :, :]
unique_classes = torch.unique(masks).cpu().numpy()
unique_classes = [
int(cls) for cls in unique_classes if cls < len(self.class_names)
]
fig, axes = plt.subplots(n_samples, 2, figsize=(9, 3 * n_samples))
if n_samples == 1:
axes = axes.reshape(1, -1)
masks = batch["mask"][indices]
unique_classes = torch.unique(masks).cpu().numpy()
unique_classes = [
int(cls) for cls in unique_classes if cls < len(self.class_names)
]
colors = {0: "black", 1: "gray", 2: "orange"}
class_colors = [colors[i] for i in range(len(colors))]
build_cmap = ListedColormap(class_colors)
for i in range(n_samples):
ax = axes[i, 0]
img = rearrange(images[i], "c h w -> h w c").cpu().numpy()
img = percentile_normalization(img, lower=2, upper=98)
ax.imshow(img)
ax.set_title("Aerial Image" if i == 0 else "")
ax.axis("off")
ax = axes[i, 1]
mask_img = masks[i].cpu().numpy()
ax.imshow(mask_img, cmap=build_cmap, vmin=0, vmax=2)
ax.set_title("Building Mask" if i == 0 else "")
ax.axis("off")
if i == 0:
legend_elements = []
for cls in unique_classes:
if cls < len(self.class_names) and cls in colors:
legend_elements.append(
plt.Rectangle(
(0, 0),
1,
1,
color=colors[cls],
label=f"{self.class_names[cls]}",
)
)
plt.tight_layout()
fig.legend(
handles=legend_elements,
loc="lower center",
bbox_to_anchor=(0.5, 0.01),
ncol=len(legend_elements),
frameon=True,
fontsize=20,
)
plt.subplots_adjust(bottom=0.1)
return fig, batch
[docs]
def visualize_geolocation_distribution(self) -> None:
"""Visualize the geolocation distribution of the dataset."""
pass