# Copyright (c) 2025 GeoBenchV2. All rights reserved.
# Licensed under the Apache License 2.0.
"""Cloud12Sen 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 torch import Tensor
from torchgeo.datasets.utils import percentile_normalization
from geobench_v2.datasets import GeoBenchCloudSen12
from .base import GeoBenchSegmentationDataModule
[docs]
class GeoBenchCloudSen12DataModule(GeoBenchSegmentationDataModule):
"""GeoBench CloudSen12 Data Module."""
[docs]
def __init__(
self,
img_size: int = 512,
band_order: Sequence[
float | str
] = GeoBenchCloudSen12.dataset_band_config.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 CloudSen12 DataModule.
Args:
img_size: Image size
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.cloudsen12.GeoBenchCloudSen12`
"""
super().__init__(
dataset_class=GeoBenchCloudSen12,
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, Tensor] | None = None, split: str = "train"
) -> tuple[plt.Figure, dict[str, Tensor]]:
"""Visualize a batch of data.
Args:
batch: Batch of data to visualize
split: One of 'train', 'validation', 'test'
Returns:
The matplotlib figure and the batch of data
"""
if batch is None:
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
plot_index = self.band_order.index(plot_bands[0])
images = images[:, plot_index, :, :]
# Create figure with 3 columns: image, mask, and legend
fig, axes = plt.subplots(
n_samples,
3,
figsize=(12, 3 * n_samples),
gridspec_kw={"width_ratios": [1, 1, 0.5]},
)
if n_samples == 1:
axes = axes.reshape(1, -1)
unique_classes = torch.unique(masks).cpu().numpy()
unique_classes = [
int(cls) for cls in unique_classes if cls < len(self.class_names)
]
cmap = plt.cm.tab20
for i in range(n_samples):
ax = axes[i, 0]
img = images[i].cpu().numpy()
img = percentile_normalization(img, lower=2, upper=98)
ax.imshow(img, cmap="gray")
ax.set_title("SAR Image" if i == 0 else "")
ax.axis("off")
ax = axes[i, 1]
mask_img = masks[i].cpu().numpy()
ax.imshow(mask_img, cmap="tab20", vmin=0, vmax=19)
ax.set_title("Mask" if i == 0 else "")
ax.axis("off")
ax = axes[i, 2]
ax.axis("off")
if i == 0:
legend_elements = []
for cls in unique_classes:
if cls < len(self.class_names):
color = cmap(cls / 20.0 if cls < 20 else 0)
legend_elements.append(
plt.Rectangle(
(0, 0),
1,
1,
color=color,
label=f"{cls}: {self.class_names[cls]}",
)
)
ax.legend(
handles=legend_elements,
loc="center",
frameon=True,
fontsize="small",
title="Classes",
)
plt.tight_layout()
return fig, batch
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def visualize_geolocation_distribution(self) -> None:
"""Visualize the geolocation distribution of the dataset."""
pass