From 05bef767c7a2e15336398095999ffe7fdfafcf03 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o=20Lucas=20de=20Sousa=20Almeida?=
<joao.l.sa.9.3@gmail.com>
Date: Wed, 5 Feb 2025 16:20:38 -0300
Subject: [PATCH 01/25] updates for documentation
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Signed-off-by: João Lucas de Sousa Almeida <joao.l.sa.9.3@gmail.com>
---
docs/data.md | 19 ++++++++++++++++---
docs/tasks.md | 3 ++-
terratorch/tasks/classification_tasks.py | 1 -
terratorch/tasks/regression_tasks.py | 5 +----
terratorch/tasks/segmentation_tasks.py | 3 +--
5 files changed, 20 insertions(+), 11 deletions(-)
diff --git a/docs/data.md b/docs/data.md
index 48aa0a7b..8010ee07 100644
--- a/docs/data.md
+++ b/docs/data.md
@@ -1,4 +1,4 @@
-# Data
+# Data Processing
We rely on TorchGeo for the implementation of datasets and data modules.
Check out [the TorchGeo tutorials on datasets](https://torchgeo.readthedocs.io/en/stable/tutorials/custom_raster_dataset.html) for more in depth information.
@@ -75,6 +75,19 @@ For the `NonGeoDataset` case, we also provide "generic" datasets and datamodules
Below is a documented example of how a custom dataset and data module class can be implemented.
-### :::terratorch.datasets.fire_scars
+### Datasets
+
+#### :::terratorch.datasets.fire_scars
+#### :::terratorch.datasets.biomassters
+#### :::terratorch.datasets.burn_intensity
+#### :::terratorch.datasets.carbonflux
+#### :::terratorch.datasets.forestnet
+
+### Datamodules
+#### :::terratorch.datamodules.fire_scars
+#### :::terratorch.datamodules.biomassters
+#### :::terratorch.datamodules.burn_intensity
+#### :::terratorch.datamodules.carbonflux
+#### :::terratorch.datamodules.forestnet
+
-### :::terratorch.datamodules.fire_scars
diff --git a/docs/tasks.md b/docs/tasks.md
index adaf588b..ffe281cd 100644
--- a/docs/tasks.md
+++ b/docs/tasks.md
@@ -3,7 +3,8 @@ Tasks provide a convenient abstraction over the training of a model for a specif
They encapsulate the model, optimizer, metrics, loss as well as training, validation and testing steps.
-The task expects to be passed a model factory, to which the model_args arguments are passed to instantiate the model that will be trained. The models produced by this model factory should output ModelOutput instances and conform to the Model ABC.
+The task expects to be passed a model factory, to which the model_args arguments are passed to instantiate the model that will be trained.
+The models produced by this model factory should output ModelOutput instances and conform to the Model ABC.
Tasks are best leveraged using config files. Check out some examples [here](./examples.md).
diff --git a/terratorch/tasks/classification_tasks.py b/terratorch/tasks/classification_tasks.py
index 60b33235..72567074 100644
--- a/terratorch/tasks/classification_tasks.py
+++ b/terratorch/tasks/classification_tasks.py
@@ -72,7 +72,6 @@ def __init__(
"""Constructor
Args:
-
Defaults to None.
model_args (Dict): Arguments passed to the model factory.
model_factory (str, optional): ModelFactory class to be used to instantiate the model.
diff --git a/terratorch/tasks/regression_tasks.py b/terratorch/tasks/regression_tasks.py
index 9849b0b2..4481a8d1 100644
--- a/terratorch/tasks/regression_tasks.py
+++ b/terratorch/tasks/regression_tasks.py
@@ -123,10 +123,7 @@ def reset(self) -> None:
class PixelwiseRegressionTask(TerraTorchTask):
"""Pixelwise Regression Task that accepts models from a range of sources.
- This class is analog in functionality to
- [PixelwiseRegressionTask]
- (https://torchgeo.readthedocs.io/en/stable/api/trainers.html#torchgeo.trainers.PixelwiseRegressionTask)
- defined by torchgeo.
+ This class is analog in functionality to PixelwiseRegressionTask defined by torchgeo.
However, it has some important differences:
- Accepts the specification of a model factory
- Logs metrics per class
diff --git a/terratorch/tasks/segmentation_tasks.py b/terratorch/tasks/segmentation_tasks.py
index f8ab82af..26300775 100644
--- a/terratorch/tasks/segmentation_tasks.py
+++ b/terratorch/tasks/segmentation_tasks.py
@@ -32,7 +32,7 @@ def to_segmentation_prediction(y: ModelOutput) -> Tensor:
class SemanticSegmentationTask(TerraTorchTask):
"""Semantic Segmentation Task that accepts models from a range of sources.
- This class is analog in functionality to class:SemanticSegmentationTask defined by torchgeo.
+ This class is analog in functionality to class SemanticSegmentationTask defined by torchgeo.
However, it has some important differences:
- Accepts the specification of a model factory
- Logs metrics per class
@@ -70,7 +70,6 @@ def __init__(
"""Constructor
Args:
-
Defaults to None.
model_args (Dict): Arguments passed to the model factory.
model_factory (str, optional): ModelFactory class to be used to instantiate the model.
From 557d2fe23c88e79054d45f09eaebdbeefd5e826f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o=20Lucas=20de=20Sousa=20Almeida?=
<joao.l.sa.9.3@gmail.com>
Date: Wed, 5 Feb 2025 16:56:37 -0300
Subject: [PATCH 02/25] updating documentation
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Signed-off-by: João Lucas de Sousa Almeida <joao.l.sa.9.3@gmail.com>
---
docs/architecture.md | 2 +-
docs/index.md | 11 +++++++----
docs/quick_start.md | 14 ++++++++++----
3 files changed, 18 insertions(+), 9 deletions(-)
diff --git a/docs/architecture.md b/docs/architecture.md
index 2354fbbc..53ab55b6 100644
--- a/docs/architecture.md
+++ b/docs/architecture.md
@@ -1,4 +1,4 @@
-# Overview (for developers)
+# Architecure Overview
The main goal of the design is to extend TorchGeo's existing tasks to be able to handle Prithvi backbones with appropriate decoders and heads.
At the same time, we wish to keep the existing TorchGeo functionality intact so it can be leveraged with pretrained models that are already included.
diff --git a/docs/index.md b/docs/index.md
index 5461998f..ceb3eb71 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -15,12 +15,15 @@ This library provides:
- Flexible trainers for Image Segmentation, Pixel Wise Regression and Classification (more in progress).
- Launching of fine-tuning tasks through powerful configuration files.
-A good starting place is familiarization with [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/), which this project is built on, and to a certain extent [TorchGeo](https://torchgeo.readthedocs.io/en/stable/)
+A good starting place is familiarization with [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/), which this project is built on.
+[TorchGeo](https://torchgeo.readthedocs.io/en/stable/) is also an important complementary reference.
+
+Check out the [architecture overview](architecture.md) for a general description about how TerraTorch is
+organized.
## Quick start
To get started, check out the [quick start guide](quick_start.md)
-## For developers
-
-Check out the [architecture overview](architecture.md)
+## License
+TerraTorch is distributed under the terms of License Apache 2.0, see [here](licence.md) for more details.
diff --git a/docs/quick_start.md b/docs/quick_start.md
index c95d6749..3ccbba94 100644
--- a/docs/quick_start.md
+++ b/docs/quick_start.md
@@ -1,18 +1,24 @@
# Quick start
-We suggest using `3.10 <= Python <= 3.12`.
+## Configuring the environment
+
+### Python
+TerraTorch is currently tested for Python in `3.10 <= Python <= 3.12`.
+
To get started, make sure to have `[PyTorch](https://pytorch.org/get-started/locally/) >= 2.0.0` and [GDAL](https://gdal.org/index.html) installed.
-Installing GDAL can be quite a complex process. If you don't have GDAL set up on your system, we reccomend using a conda environment and installing it with `conda install -c conda-forge gdal`.
+### GDAL
+GDAL is required to read and write TIFF images. It is usually easy to install in Unix/Linux systems, but if it is not your case
+we reccomend using a conda environment and installing it with `conda install -c conda-forge gdal`.
+## Installing TerraTorch
For a stable point-release, use `pip install terratorch`.
If you prefer to get the most recent version of the main branch, install the library with `pip install git+https://github.com/IBM/terratorch.git`.
To install as a developer (e.g. to extend the library) clone this repo, and run `pip install -e .`.
-You can interact with the library at several levels of abstraction. Each deeper level of abstraction trades off some amount of flexibility for ease of use and configuration.
-
## Creating Backbones
+You can interact with the library at several levels of abstraction. Each deeper level of abstraction trades off some amount of flexibility for ease of use and configuration.
In the simplest case, we might only want access a backbone and code all the rest ourselves. In this case, we can simply use the library as a backbone factory:
```python title="Instantiating a prithvi backbone"
From fc3349839a278ad2633eb6b2c2815ca4b3225a76 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o=20Lucas=20de=20Sousa=20Almeida?=
<joao.l.sa.9.3@gmail.com>
Date: Wed, 5 Feb 2025 17:53:03 -0300
Subject: [PATCH 03/25] updating documentation
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Signed-off-by: João Lucas de Sousa Almeida <joao.l.sa.9.3@gmail.com>
---
docs/models.md | 7 +++----
mkdocs.yml | 2 +-
2 files changed, 4 insertions(+), 5 deletions(-)
diff --git a/docs/models.md b/docs/models.md
index 4b1566de..30ec6673 100644
--- a/docs/models.md
+++ b/docs/models.md
@@ -1,10 +1,10 @@
# Models
-To interface with terratorch tasks correctly, models must conform to the [Model][terratorch.models.model.Model] ABC:
+To interface with terratorch tasks correctly, models must inherit from the [Model][terratorch.models.model.Model] parent class:
::: terratorch.models.model.Model
-and have a forward method which returns a [ModelOutput][terratorch.models.model.ModelOutput]:
+and have a forward method which returns an object [ModelOutput][terratorch.models.model.ModelOutput]:
:::terratorch.models.model.ModelOutput
@@ -12,8 +12,7 @@ and have a forward method which returns a [ModelOutput][terratorch.models.model.
## Model Factories
In order to be used by tasks, models must have a Model Factory which builds them.
-
-Factories must conform to the [ModelFactory][terratorch.models.model.ModelFactory] ABC:
+Factories must conform to the [ModelFactory][terratorch.models.model.ModelFactory] parent class.
::: terratorch.models.model.ModelFactory
diff --git a/mkdocs.yml b/mkdocs.yml
index 0ffd9b8b..63637d9e 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -32,7 +32,7 @@ nav:
- EncoderDecoderFactory: encoder_decoder_factory.md
- Examples: examples.md
#- FAQ: faq.md
- - For Developers: architecture.md
+ - Architecture Overview: architecture.md
- License: license.md
markdown_extensions:
From b089111ae425c957592af2c3591f24d381d26f28 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o=20Lucas=20de=20Sousa=20Almeida?=
<joao.l.sa.9.3@gmail.com>
Date: Wed, 5 Feb 2025 17:57:09 -0300
Subject: [PATCH 04/25] updating documentation
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Signed-off-by: João Lucas de Sousa Almeida <joao.l.sa.9.3@gmail.com>
---
terratorch/tasks/classification_tasks.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/terratorch/tasks/classification_tasks.py b/terratorch/tasks/classification_tasks.py
index 72567074..27488b8d 100644
--- a/terratorch/tasks/classification_tasks.py
+++ b/terratorch/tasks/classification_tasks.py
@@ -28,7 +28,7 @@ def to_class_prediction(y: ModelOutput) -> Tensor:
class ClassificationTask(TerraTorchTask):
"""Classification Task that accepts models from a range of sources.
- This class is analog in functionality to class:ClassificationTask defined by torchgeo.
+ This class is analog in functionality to the class ClassificationTask defined by torchgeo.
However, it has some important differences:
- Accepts the specification of a model factory
- Logs metrics per class
From 5d2804b49a2e6479b749869cca5355083ffc877c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o=20Lucas=20de=20Sousa=20Almeida?=
<joao.l.sa.9.3@gmail.com>
Date: Wed, 5 Feb 2025 18:29:45 -0300
Subject: [PATCH 05/25] updating documentation
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Signed-off-by: João Lucas de Sousa Almeida <joao.l.sa.9.3@gmail.com>
---
docs/examples.md | 8 +++++++-
docs/quick_start.md | 2 --
2 files changed, 7 insertions(+), 3 deletions(-)
diff --git a/docs/examples.md b/docs/examples.md
index 1edc94e0..1ceea2cb 100644
--- a/docs/examples.md
+++ b/docs/examples.md
@@ -16,4 +16,10 @@ Under `examples/confs`
* Burn Scar Segmentation: `burn_scars.yaml`
-* Scene Classification: `eurosat.yaml`
\ No newline at end of file
+* Scene Classification: `eurosat.yaml`
+
+External examples available in [Prithvi-EO-2.0](https://github.com/NASA-IMPACT/Prithvi-EO-2.0)
+
+* [Carbon Flux](https://github.com/NASA-IMPACT/Prithvi-EO-2.0/tree/main/examples/carbon_flux)
+* [Landslide](https://github.com/NASA-IMPACT/Prithvi-EO-2.0/blob/main/examples/example_landslide4sense.ipynb)
+* [Multitemporal Crop](https://github.com/NASA-IMPACT/Prithvi-EO-2.0/blob/main/examples/example_multitemporalcrop.ipynb)
diff --git a/docs/quick_start.md b/docs/quick_start.md
index 3ccbba94..6cc9ffef 100644
--- a/docs/quick_start.md
+++ b/docs/quick_start.md
@@ -4,8 +4,6 @@
### Python
TerraTorch is currently tested for Python in `3.10 <= Python <= 3.12`.
-To get started, make sure to have `[PyTorch](https://pytorch.org/get-started/locally/) >= 2.0.0` and [GDAL](https://gdal.org/index.html) installed.
-
### GDAL
GDAL is required to read and write TIFF images. It is usually easy to install in Unix/Linux systems, but if it is not your case
we reccomend using a conda environment and installing it with `conda install -c conda-forge gdal`.
From 8c63531a313832492cfaedd6f55480804a4420af Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o=20Lucas=20de=20Sousa=20Almeida?=
<joao.l.sa.9.3@gmail.com>
Date: Wed, 5 Feb 2025 18:50:49 -0300
Subject: [PATCH 06/25] updating documentation
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Signed-off-by: João Lucas de Sousa Almeida <joao.l.sa.9.3@gmail.com>
---
docs/index.md | 12 ++++++------
1 file changed, 6 insertions(+), 6 deletions(-)
diff --git a/docs/index.md b/docs/index.md
index ceb3eb71..d1f72f36 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,17 +1,17 @@
# Welcome to TerraTorch
+<img src="https://github.com/user-attachments/assets/f8c9586f-6220-4a53-9669-2aee3300b492" alt="TerraTorch" width="400"/>
## Overview
-The purpose of this library is twofold:
-
-1. To integrate prithvi backbones into the TorchGeo framework.
-2. To provide generic LightningDataModules that can be built at runtime.
-3. To build a flexible fine-tuning framework based on TorchGeo which can be interacted with at different abstraction levels.
+The purpose of this package is to build a flexible fine-tuning framework for Geospatial Foundation Models (GFMs) based on TorchGeo and Lightning
+which can be employed at different abstraction levels. It currently supports models from the
+[Prithvi](https://huggingface.co/ibm-nasa-geospatial)
+and [Granite](https://huggingface.co/ibm-granite/granite-geospatial-land-surface-temperature) series, and also have been tested with others models available on HuggingFace.
This library provides:
- All the functionality in TorchGeo.
-- Easy access to prithvi, timm and smp backbones.
+- Easy access to Prithvi, timm and smp backbones.
- Flexible trainers for Image Segmentation, Pixel Wise Regression and Classification (more in progress).
- Launching of fine-tuning tasks through powerful configuration files.
From 270bf23107a769c4c64f708c84e3c5dc346a7be9 Mon Sep 17 00:00:00 2001
From: Bianca Zadrozny <biancaz@br.ibm.com>
Date: Wed, 12 Feb 2025 17:58:56 -0300
Subject: [PATCH 07/25] changing order of navigation topics
---
mkdocs.yml | 7 ++++---
1 file changed, 4 insertions(+), 3 deletions(-)
diff --git a/mkdocs.yml b/mkdocs.yml
index 63637d9e..e351e63a 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -24,16 +24,17 @@ repo_url: https://github.com/IBM/terratorch
nav:
- index.md
- Getting Started: quick_start.md
+
+ - Examples: examples.md
+ - License: license.md
- Components:
+ - Architecture Overview: architecture.m
- Tasks: tasks.md
- Models: models.md
- Data: data.md
- Registries: registry.md
- EncoderDecoderFactory: encoder_decoder_factory.md
- - Examples: examples.md
#- FAQ: faq.md
- - Architecture Overview: architecture.md
- - License: license.md
markdown_extensions:
- pymdownx.highlight:
From 7f04776537f3a65005223875dab65b686841551e Mon Sep 17 00:00:00 2001
From: Bianca Zadrozny <biancaz@br.ibm.com>
Date: Wed, 12 Feb 2025 18:03:26 -0300
Subject: [PATCH 08/25] small changes in tasks and architeture descriptions
---
docs/architecture.md | 2 +-
docs/tasks.md | 33 +++++----------------------------
2 files changed, 6 insertions(+), 29 deletions(-)
diff --git a/docs/architecture.md b/docs/architecture.md
index 53ab55b6..ce8275cc 100644
--- a/docs/architecture.md
+++ b/docs/architecture.md
@@ -1,4 +1,4 @@
-# Architecure Overview
+# Architecture Overview
The main goal of the design is to extend TorchGeo's existing tasks to be able to handle Prithvi backbones with appropriate decoders and heads.
At the same time, we wish to keep the existing TorchGeo functionality intact so it can be leveraged with pretrained models that are already included.
diff --git a/docs/tasks.md b/docs/tasks.md
index ffe281cd..cb254a8e 100644
--- a/docs/tasks.md
+++ b/docs/tasks.md
@@ -6,40 +6,17 @@ They encapsulate the model, optimizer, metrics, loss as well as training, valida
The task expects to be passed a model factory, to which the model_args arguments are passed to instantiate the model that will be trained.
The models produced by this model factory should output ModelOutput instances and conform to the Model ABC.
-Tasks are best leveraged using config files. Check out some examples [here](./examples.md).
+Tasks are best leveraged using config files, where they are specified in the `model` section under `class_path`. You can check out some examples of config files [here](./examples.md).
-!!! info "Argument parsing in configs"
- Argument parsing of configs relies on argument names and type hints in the code.
- To pass arguments that do not conform to this (e.g. for classes that make use of **kwargs)
- put those arguments in `dict_kwargs` instead of `init_args`.
+Below are the details of the tasks currently implemented in TerraTorch (Pixelwise Regression, Semantic Segmentation and Classification).
----
-### Multi Temporal Inputs
-
-Multi temporal inputs are also supported!
-However, we leverage albumentations for augmentations, and it does not support multitemporal input.
-We currently get around this using the following strategy in the transform:
-
-```yaml
-train_transform:
- - class_path: FlattenTemporalIntoChannels
- # your transforms here, wrapped by these other ones
- # e.g. a random flip
- - class_path: albumentations.Flip
- # end of your transforms
- - class_path: ToTensorV2
- - class_path: UnflattenTemporalFromChannels
- init_args:
- n_timesteps: 3 # your number of timesteps here
- # alternatively, n_channels can be specified
-```
-See an example of this [here](examples.md).
---
-## ::: terratorch.tasks.regression_tasks.PixelwiseRegressionTask
-
## :::terratorch.tasks.segmentation_tasks.SemanticSegmentationTask
+## ::: terratorch.tasks.regression_tasks.PixelwiseRegressionTask
+
## :::terratorch.tasks.classification_tasks.ClassificationTask
+
From 311bc1e46eb37d4ad8973e2be17d11bd2d3ebf15 Mon Sep 17 00:00:00 2001
From: Bianca Zadrozny <biancaz@br.ibm.com>
Date: Wed, 12 Feb 2025 18:09:42 -0300
Subject: [PATCH 09/25] fixing file name in mkdocs
---
mkdocs.yml | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/mkdocs.yml b/mkdocs.yml
index e351e63a..5bab8e8a 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -28,7 +28,7 @@ nav:
- Examples: examples.md
- License: license.md
- Components:
- - Architecture Overview: architecture.m
+ - Architecture Overview: architecture.md
- Tasks: tasks.md
- Models: models.md
- Data: data.md
From d9faad0ad617553dfbcecff54970385852d5860b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o=20Lucas=20de=20Sousa=20Almeida?=
<joao.l.sa.9.3@gmail.com>
Date: Thu, 13 Feb 2025 14:31:41 -0300
Subject: [PATCH 10/25] Broken link
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Signed-off-by: João Lucas de Sousa Almeida <joao.l.sa.9.3@gmail.com>
---
docs/index.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/docs/index.md b/docs/index.md
index d1f72f36..8466750b 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -26,4 +26,4 @@ organized.
To get started, check out the [quick start guide](quick_start.md)
## License
-TerraTorch is distributed under the terms of License Apache 2.0, see [here](licence.md) for more details.
+TerraTorch is distributed under the terms of License Apache 2.0, see [here](license.md) for more details.
From 873ed47a6349319494f9c10ca9daf90592d919e4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o=20Lucas=20de=20Sousa=20Almeida?=
<joao.l.sa.9.3@gmail.com>
Date: Thu, 13 Feb 2025 14:35:32 -0300
Subject: [PATCH 11/25] fixing typo
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Signed-off-by: João Lucas de Sousa Almeida <joao.l.sa.9.3@gmail.com>
---
docs/architecture.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/docs/architecture.md b/docs/architecture.md
index ce8275cc..492ff162 100644
--- a/docs/architecture.md
+++ b/docs/architecture.md
@@ -26,7 +26,7 @@ One of the most important design decisions was delegating the model construction
- Prefers composition over inheritance
- Allows new models to be easily added by introducing new factories
-Models are expected to be `torch.nn.Module`s and implement the [Model][terratorch.models.model.Model] interface, providing:
+Models are expected to be `torch.nn.Module` and implement the [Model][terratorch.models.model.Model] interface, providing:
- `freeze_encoder()`
- `freeze_decoder()`
From e8dffef3e02676431cad9f85ade63b944b3e9c61 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o=20Lucas=20de=20Sousa=20Almeida?=
<joao.l.sa.9.3@gmail.com>
Date: Thu, 13 Feb 2025 15:04:32 -0300
Subject: [PATCH 12/25] code snippet for encoder
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Signed-off-by: João Lucas de Sousa Almeida <joao.l.sa.9.3@gmail.com>
---
docs/encoder_decoder_factory.md | 49 ++++++++++++++++++++++++++++++---
1 file changed, 45 insertions(+), 4 deletions(-)
diff --git a/docs/encoder_decoder_factory.md b/docs/encoder_decoder_factory.md
index 581f8626..78b36179 100644
--- a/docs/encoder_decoder_factory.md
+++ b/docs/encoder_decoder_factory.md
@@ -25,10 +25,51 @@ The [EncoderDecoderFactory][terratorch.models.encoder_decoder_factory.EncoderDec
## Encoders
-To be a valid encoder, an object must be an `nn.Module` with an additional attribute `out_channels` which is a list of the channel dimension of the features it returns.
-
-It's forward method should return a list of `torch.Tensor`.
-
+To be a valid encoder, an object must be an `nn.Module` and contain an attribute `out_channels`, basically a list of the channel dimensions corresponding to
+the features it returns.
+The forward method of any encoder should return a list of `torch.Tensor`.
+
+```sh
+In [19]: backbone = BACKBONE_REGISTRY.build("prithvi_eo_v2_300", pretrained=True)
+
+In [20]: import numpy as np
+
+In [21]: import torch
+
+In [22]: input_image = torch.tensor(np.random.rand(1,6,224,224).astype("float32"))
+
+In [23]: output = backbone.forward(input_image)
+
+In [24]: [item.shape for item in output]
+
+Out[24]:
+
+[torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024]),
+ torch.Size([1, 197, 1024])]
+
+```
## Necks
From 9c3bba6da6129266eeb51b53b1d33ced26f506a2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o=20Lucas=20de=20Sousa=20Almeida?=
<joao.l.sa.9.3@gmail.com>
Date: Thu, 13 Feb 2025 15:43:57 -0300
Subject: [PATCH 13/25] fixing typo
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Signed-off-by: João Lucas de Sousa Almeida <joao.l.sa.9.3@gmail.com>
---
docs/encoder_decoder_factory.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/docs/encoder_decoder_factory.md b/docs/encoder_decoder_factory.md
index 78b36179..d9a37d09 100644
--- a/docs/encoder_decoder_factory.md
+++ b/docs/encoder_decoder_factory.md
@@ -99,7 +99,7 @@ Necks are `nn.Modules`, with an additional method `process_channel_list` which i
## Decoders
-To be a valid decoder, an object must be an `nn.Module` with an additional attribute `out_channels` which is an `int` with the channel dimension of the output.
+To be a valid decoder, an object must be an `nn.Module` with an attribute `out_channels`, an `int` representing the channel dimension of the output.
The first argument to its constructor will be a list of channel dimensions it should expect as input.
From 29ccc53f61ca20113467563d380b435704fbffa9 Mon Sep 17 00:00:00 2001
From: Pedro Henrique Conrado <pedrohenriqueconrado@usp.br>
Date: Mon, 17 Feb 2025 08:56:51 -0500
Subject: [PATCH 14/25] documentation changes datasets
---
terratorch/datasets/biomassters.py | 2 +-
terratorch/datasets/burn_intensity.py | 2 +-
terratorch/datasets/carbonflux.py | 2 +-
terratorch/datasets/fire_scars.py | 2 +-
terratorch/datasets/forestnet.py | 3 +-
terratorch/datasets/landslide4sense.py | 3 +-
terratorch/datasets/m_SA_crop_type.py | 2 +-
terratorch/datasets/m_bigearthnet.py | 2 +-
terratorch/datasets/m_brick_kiln.py | 2 +-
terratorch/datasets/m_cashew_plantation.py | 2 +-
terratorch/datasets/m_chesapeake_landcover.py | 2 +-
terratorch/datasets/m_eurosat.py | 2 +-
terratorch/datasets/m_forestnet.py | 2 +-
terratorch/datasets/m_neontree.py | 2 +-
terratorch/datasets/m_nz_cattle.py | 2 +-
terratorch/datasets/m_pv4ger.py | 2 +-
terratorch/datasets/m_pv4ger_seg.py | 2 +-
terratorch/datasets/m_so2sat.py | 2 +-
.../multi_temporal_crop_classification.py | 2 +-
terratorch/datasets/open_sentinel_map.py | 6 +-
terratorch/datasets/openearthmap.py | 27 +++++
terratorch/datasets/pastis.py | 6 +-
terratorch/datasets/sen1floods11.py | 5 +-
terratorch/datasets/sen4agrinet.py | 2 +-
terratorch/datasets/sen4map.py | 50 ++++++++-
terratorch/datasets/transforms.py | 102 ++++++++++++++++--
26 files changed, 199 insertions(+), 39 deletions(-)
diff --git a/terratorch/datasets/biomassters.py b/terratorch/datasets/biomassters.py
index 323fcc6c..482dfcc0 100644
--- a/terratorch/datasets/biomassters.py
+++ b/terratorch/datasets/biomassters.py
@@ -21,7 +21,7 @@
class BioMasstersNonGeo(BioMassters):
- """BioMassters Dataset for Aboveground Biomass prediction.
+ """[BioMassters Dataset](https://huggingface.co/datasets/ibm-nasa-geospatial/BioMassters) for Aboveground Biomass prediction.
Dataset intended for Aboveground Biomass (AGB) prediction
over Finnish forests based on Sentinel 1 and 2 data with
diff --git a/terratorch/datasets/burn_intensity.py b/terratorch/datasets/burn_intensity.py
index 999cc663..be418403 100644
--- a/terratorch/datasets/burn_intensity.py
+++ b/terratorch/datasets/burn_intensity.py
@@ -19,7 +19,7 @@
class BurnIntensityNonGeo(NonGeoDataset):
- """Dataset implementation for Burn Intensity classification."""
+ """Dataset implementation for [Burn Intensity classification](https://huggingface.co/datasets/ibm-nasa-geospatial/burn_intensity)."""
all_band_names = (
"BLUE", "GREEN", "RED", "NIR", "SWIR_1", "SWIR_2",
diff --git a/terratorch/datasets/carbonflux.py b/terratorch/datasets/carbonflux.py
index a7185cd6..f0ad0115 100644
--- a/terratorch/datasets/carbonflux.py
+++ b/terratorch/datasets/carbonflux.py
@@ -19,7 +19,7 @@
class CarbonFluxNonGeo(NonGeoDataset):
- """Dataset for Carbon Flux regression from HLS images and MERRA data."""
+ """Dataset for [Carbon Flux](https://huggingface.co/datasets/ibm-nasa-geospatial/hls_merra2_gppFlux) regression from HLS images and MERRA data."""
all_band_names = (
"BLUE", "GREEN", "RED", "NIR", "SWIR_1", "SWIR_2",
diff --git a/terratorch/datasets/fire_scars.py b/terratorch/datasets/fire_scars.py
index f5b65516..8c4c092a 100644
--- a/terratorch/datasets/fire_scars.py
+++ b/terratorch/datasets/fire_scars.py
@@ -24,7 +24,7 @@
class FireScarsNonGeo(NonGeoDataset):
- """NonGeo dataset implementation for fire scars."""
+ """NonGeo dataset implementation for [fire scars](https://huggingface.co/datasets/ibm-nasa-geospatial/hls_burn_scars)."""
all_band_names = (
"BLUE",
"GREEN",
diff --git a/terratorch/datasets/forestnet.py b/terratorch/datasets/forestnet.py
index 86dd37be..821fe089 100644
--- a/terratorch/datasets/forestnet.py
+++ b/terratorch/datasets/forestnet.py
@@ -21,8 +21,7 @@
class ForestNetNonGeo(NonGeoDataset):
- """NonGeo dataset implementation for ForestNet."""
-
+ """NonGeo dataset implementation for [ForestNet](https://huggingface.co/datasets/ibm-nasa-geospatial/ForestNet)."""
all_band_names = (
"RED", "GREEN", "BLUE", "NIR", "SWIR_1", "SWIR_2"
diff --git a/terratorch/datasets/landslide4sense.py b/terratorch/datasets/landslide4sense.py
index 5c949ded..a024ce75 100644
--- a/terratorch/datasets/landslide4sense.py
+++ b/terratorch/datasets/landslide4sense.py
@@ -15,8 +15,7 @@
class Landslide4SenseNonGeo(NonGeoDataset):
- """NonGeo dataset implementation for Landslide4Sense."""
-
+ """NonGeo dataset implementation for [Landslide4Sense](https://huggingface.co/datasets/ibm-nasa-geospatial/Landslide4sense)."""
all_band_names = (
"COASTAL AEROSOL",
"BLUE",
diff --git a/terratorch/datasets/m_SA_crop_type.py b/terratorch/datasets/m_SA_crop_type.py
index c62f8f07..e822dd51 100644
--- a/terratorch/datasets/m_SA_crop_type.py
+++ b/terratorch/datasets/m_SA_crop_type.py
@@ -18,7 +18,7 @@
class MSACropTypeNonGeo(NonGeoDataset):
- """NonGeo dataset implementation for M-SA-Crop-Type."""
+ """NonGeo dataset implementation for [M-SA-Crop-Type](https://github.com/ServiceNow/geo-bench?tab=readme-ov-file)."""
all_band_names = (
"COASTAL_AEROSOL",
"BLUE",
diff --git a/terratorch/datasets/m_bigearthnet.py b/terratorch/datasets/m_bigearthnet.py
index 5d25f334..3ec26b15 100644
--- a/terratorch/datasets/m_bigearthnet.py
+++ b/terratorch/datasets/m_bigearthnet.py
@@ -18,7 +18,7 @@
class MBigEarthNonGeo(NonGeoDataset):
- """NonGeo dataset implementation for M-BigEarthNet."""
+ """NonGeo dataset implementation for [M-BigEarthNet](https://github.com/ServiceNow/geo-bench?tab=readme-ov-file)."""
all_band_names = (
"COASTAL_AEROSOL",
"BLUE",
diff --git a/terratorch/datasets/m_brick_kiln.py b/terratorch/datasets/m_brick_kiln.py
index 7ca3d8a9..69246b82 100644
--- a/terratorch/datasets/m_brick_kiln.py
+++ b/terratorch/datasets/m_brick_kiln.py
@@ -20,7 +20,7 @@
class MBrickKilnNonGeo(NonGeoDataset):
- """NonGeo dataset implementation for M-BrickKiln."""
+ """NonGeo dataset implementation for [M-BrickKiln](https://github.com/ServiceNow/geo-bench?tab=readme-ov-file)."""
all_band_names = (
"COASTAL_AEROSOL",
"BLUE",
diff --git a/terratorch/datasets/m_cashew_plantation.py b/terratorch/datasets/m_cashew_plantation.py
index e6f46660..3d244f5a 100644
--- a/terratorch/datasets/m_cashew_plantation.py
+++ b/terratorch/datasets/m_cashew_plantation.py
@@ -20,7 +20,7 @@
class MBeninSmallHolderCashewsNonGeo(NonGeoDataset):
- """NonGeo dataset implementation for M-BeninSmallHolderCashews."""
+ """NonGeo dataset implementation for [M-BeninSmallHolderCashews](https://github.com/ServiceNow/geo-bench?tab=readme-ov-file)."""
all_band_names = (
"COASTAL_AEROSOL",
"BLUE",
diff --git a/terratorch/datasets/m_chesapeake_landcover.py b/terratorch/datasets/m_chesapeake_landcover.py
index 8dcb3226..4b7111e5 100644
--- a/terratorch/datasets/m_chesapeake_landcover.py
+++ b/terratorch/datasets/m_chesapeake_landcover.py
@@ -18,7 +18,7 @@
class MChesapeakeLandcoverNonGeo(NonGeoDataset):
- """NonGeo dataset implementation for M-ChesapeakeLandcover."""
+ """NonGeo dataset implementation for [M-ChesapeakeLandcover](https://github.com/ServiceNow/geo-bench?tab=readme-ov-file)."""
all_band_names = ("BLUE", "GREEN", "NIR", "RED")
rgb_bands = ("RED", "GREEN", "BLUE")
diff --git a/terratorch/datasets/m_eurosat.py b/terratorch/datasets/m_eurosat.py
index 0151f92d..d91b974a 100644
--- a/terratorch/datasets/m_eurosat.py
+++ b/terratorch/datasets/m_eurosat.py
@@ -17,7 +17,7 @@
class MEuroSATNonGeo(NonGeoDataset):
- """NonGeo dataset implementation for M-EuroSAT."""
+ """NonGeo dataset implementation for [M-EuroSAT](https://github.com/ServiceNow/geo-bench?tab=readme-ov-file)."""
all_band_names = (
"COASTAL_AEROSOL",
"BLUE",
diff --git a/terratorch/datasets/m_forestnet.py b/terratorch/datasets/m_forestnet.py
index cbdba016..31beb998 100644
--- a/terratorch/datasets/m_forestnet.py
+++ b/terratorch/datasets/m_forestnet.py
@@ -21,7 +21,7 @@
class MForestNetNonGeo(NonGeoDataset):
- """NonGeo dataset implementation for M-ForestNet."""
+ """NonGeo dataset implementation for [M-ForestNet](https://github.com/ServiceNow/geo-bench?tab=readme-ov-file)."""
all_band_names = (
"BLUE",
"GREEN",
diff --git a/terratorch/datasets/m_neontree.py b/terratorch/datasets/m_neontree.py
index 3f08b4ea..43fb87cb 100644
--- a/terratorch/datasets/m_neontree.py
+++ b/terratorch/datasets/m_neontree.py
@@ -18,7 +18,7 @@
class MNeonTreeNonGeo(NonGeoDataset):
- """NonGeo dataset implementation for M-NeonTree."""
+ """NonGeo dataset implementation for [M-NeonTree](https://github.com/ServiceNow/geo-bench?tab=readme-ov-file)."""
all_band_names = ("BLUE", "CANOPY_HEIGHT_MODEL", "GREEN", "NEON", "RED")
rgb_bands = ("RED", "GREEN", "BLUE")
diff --git a/terratorch/datasets/m_nz_cattle.py b/terratorch/datasets/m_nz_cattle.py
index ad4f6ccd..5f1203fa 100644
--- a/terratorch/datasets/m_nz_cattle.py
+++ b/terratorch/datasets/m_nz_cattle.py
@@ -20,7 +20,7 @@
class MNzCattleNonGeo(NonGeoDataset):
- """NonGeo dataset implementation for M-NZ-Cattle."""
+ """NonGeo dataset implementation for [M-NZ-Cattle](https://github.com/ServiceNow/geo-bench?tab=readme-ov-file)."""
all_band_names = ("BLUE", "GREEN", "RED")
rgb_bands = ("RED", "GREEN", "BLUE")
diff --git a/terratorch/datasets/m_pv4ger.py b/terratorch/datasets/m_pv4ger.py
index e8abb717..10be1b22 100644
--- a/terratorch/datasets/m_pv4ger.py
+++ b/terratorch/datasets/m_pv4ger.py
@@ -20,7 +20,7 @@
class MPv4gerNonGeo(NonGeoDataset):
- """NonGeo dataset implementation for M-PV4GER."""
+ """NonGeo dataset implementation for [M-PV4GER](https://github.com/ServiceNow/geo-bench?tab=readme-ov-file)."""
all_band_names = ("BLUE", "GREEN", "RED")
rgb_bands = ("RED", "GREEN", "BLUE")
diff --git a/terratorch/datasets/m_pv4ger_seg.py b/terratorch/datasets/m_pv4ger_seg.py
index 5db3aa86..ff6464ca 100644
--- a/terratorch/datasets/m_pv4ger_seg.py
+++ b/terratorch/datasets/m_pv4ger_seg.py
@@ -19,7 +19,7 @@
class MPv4gerSegNonGeo(NonGeoDataset):
- """NonGeo dataset implementation for M-PV4GER-SEG."""
+ """NonGeo dataset implementation for [M-PV4GER-SEG](https://github.com/ServiceNow/geo-bench?tab=readme-ov-file)."""
all_band_names = ("BLUE", "GREEN", "RED")
rgb_bands = ("RED", "GREEN", "BLUE")
diff --git a/terratorch/datasets/m_so2sat.py b/terratorch/datasets/m_so2sat.py
index 1c981269..5507afee 100644
--- a/terratorch/datasets/m_so2sat.py
+++ b/terratorch/datasets/m_so2sat.py
@@ -20,7 +20,7 @@
class MSo2SatNonGeo(NonGeoDataset):
- """NonGeo dataset implementation for M-So2Sat."""
+ """NonGeo dataset implementation for [M-So2Sat](https://github.com/ServiceNow/geo-bench?tab=readme-ov-file)."""
all_band_names = (
"VH_REAL",
"BLUE",
diff --git a/terratorch/datasets/multi_temporal_crop_classification.py b/terratorch/datasets/multi_temporal_crop_classification.py
index 709800d4..7fa13e0a 100644
--- a/terratorch/datasets/multi_temporal_crop_classification.py
+++ b/terratorch/datasets/multi_temporal_crop_classification.py
@@ -22,7 +22,7 @@
class MultiTemporalCropClassification(NonGeoDataset):
- """NonGeo dataset implementation for multi-temporal crop classification."""
+ """NonGeo dataset implementation for [multi-temporal crop classification](https://huggingface.co/datasets/ibm-nasa-geospatial/multi-temporal-crop-classification)."""
all_band_names = (
"BLUE",
diff --git a/terratorch/datasets/open_sentinel_map.py b/terratorch/datasets/open_sentinel_map.py
index fb95cc1b..4fc88869 100644
--- a/terratorch/datasets/open_sentinel_map.py
+++ b/terratorch/datasets/open_sentinel_map.py
@@ -19,6 +19,10 @@
MAX_TEMPORAL_IMAGE_SIZE = (192, 192)
class OpenSentinelMap(NonGeoDataset):
+ """
+ Pytorch Dataset class to load samples from the [OpenSentinelMap](https://visionsystemsinc.github.io/open-sentinel-map/) dataset, supporting
+ multiple bands and temporal sampling strategies.
+ """
def __init__(
self,
data_root: str,
@@ -32,8 +36,6 @@ def __init__(
pick_random_pair: bool = True, # noqa: FBT002, FBT001
) -> None:
"""
- Pytorch Dataset class to load samples from the OpenSentinelMap dataset, supporting
- multiple bands and temporal sampling strategies.
Args:
data_root (str): Path to the root directory of the dataset.
diff --git a/terratorch/datasets/openearthmap.py b/terratorch/datasets/openearthmap.py
index bad3bbcd..21dc78b2 100644
--- a/terratorch/datasets/openearthmap.py
+++ b/terratorch/datasets/openearthmap.py
@@ -13,6 +13,15 @@
class OpenEarthMapNonGeo(NonGeoDataset):
+ """
+ [OpenEarthMapNonGeo](https://open-earth-map.org/) Dataset for non-georeferenced imagery.
+
+ This dataset class handles non-georeferenced image data from the OpenEarthMap dataset.
+ It supports configurable band sets and transformations, and performs cropping operations
+ to ensure that the images conform to the required input dimensions. The dataset is split
+ into "train", "test", and "val" subsets based on the provided split parameter.
+ """
+
all_band_names = ("BLUE","GREEN","RED")
@@ -26,6 +35,24 @@ def __init__(self, data_root: str,
split="train",
crop_size: int = 256,
random_crop: bool = True) -> None:
+ """
+ Initialize a new instance of the OpenEarthMapNonGeo dataset.
+
+ Args:
+ data_root (str): The root directory containing the dataset files.
+ bands (Sequence[str], optional): A list of band names to be used. Default is BAND_SETS["all"].
+ transform (A.Compose or None, optional): A transformation pipeline to be applied to the data.
+ If None, a default transform converting the data to a tensor is applied.
+ split (str, optional): The dataset split to use ("train", "test", or "val"). Default is "train".
+ crop_size (int, optional): The size (in pixels) of the crop to apply to images. Must be greater than 0.
+ Default is 256.
+ random_crop (bool, optional): If True, performs a random crop; otherwise, performs a center crop.
+ Default is True.
+
+ Raises:
+ Exception: If the provided split is not one of "train", "test", or "val".
+ AssertionError: If crop_size is not greater than 0.
+ """
super().__init__()
if split not in ["train", "test", "val"]:
msg = "Split must be one of train, test, val."
diff --git a/terratorch/datasets/pastis.py b/terratorch/datasets/pastis.py
index 351bdaae..445d3c76 100644
--- a/terratorch/datasets/pastis.py
+++ b/terratorch/datasets/pastis.py
@@ -14,6 +14,10 @@
class PASTIS(NonGeoDataset):
+ """"
+ Pytorch Dataset class to load samples from the [PASTIS](https://github.com/VSainteuf/pastis-benchmark) dataset,
+ for semantic and panoptic segmentation.
+ """
def __init__(
self,
data_root,
@@ -29,8 +33,6 @@ def __init__(
satellites=["S2"], # noqa: B006
):
"""
- Pytorch Dataset class to load samples from the PASTIS dataset, for semantic and
- panoptic segmentation.
Args:
data_root (str): Path to the dataset.
diff --git a/terratorch/datasets/sen1floods11.py b/terratorch/datasets/sen1floods11.py
index 690ded1a..3cae31ed 100644
--- a/terratorch/datasets/sen1floods11.py
+++ b/terratorch/datasets/sen1floods11.py
@@ -22,7 +22,7 @@
class Sen1Floods11NonGeo(NonGeoDataset):
- """NonGeo dataset implementation for sen1floods11."""
+ """NonGeo dataset implementation for [sen1floods11](https://github.com/cloudtostreet/Sen1Floods11)."""
all_band_names = (
"COASTAL_AEROSOL",
@@ -66,8 +66,7 @@ def __init__(
split (str): one of 'train', 'val' or 'test'.
bands (list[str]): Bands that should be output by the dataset. Defaults to all bands.
transform (A.Compose | None): Albumentations transform to be applied.
- Should end with ToTensorV2(). If used through the corresponding data module,
- should not include normalization. Defaults to None, which applies ToTensorV2().
+ Should end with ToTensorV2(). Defaults to None, which applies ToTensorV2().
constant_scale (float): Factor to multiply image values by. Defaults to 0.0001.
no_data_replace (float | None): Replace nan values in input images with this value.
If None, does no replacement. Defaults to 0.
diff --git a/terratorch/datasets/sen4agrinet.py b/terratorch/datasets/sen4agrinet.py
index 203d9be5..ca13a530 100644
--- a/terratorch/datasets/sen4agrinet.py
+++ b/terratorch/datasets/sen4agrinet.py
@@ -45,7 +45,7 @@ def __init__(
seed: int = 42,
):
"""
- Pytorch Dataset class to load samples from the Sen4AgriNet dataset, supporting
+ Pytorch Dataset class to load samples from the [Sen4AgriNet](https://github.com/Orion-AI-Lab/S4A) dataset, supporting
multiple scenarios for splitting the data.
Args:
diff --git a/terratorch/datasets/sen4map.py b/terratorch/datasets/sen4map.py
index 89fee9d2..79f256c0 100644
--- a/terratorch/datasets/sen4map.py
+++ b/terratorch/datasets/sen4map.py
@@ -13,7 +13,28 @@
class Sen4MapDatasetMonthlyComposites(Dataset):
- # This dictionary maps the LUCAS classes to Land-cover classes.
+ """[Sen4Map](https://gitlab.jsc.fz-juelich.de/sdlrs/sen4map-benchmark-dataset) Dataset for Monthly Composites.
+
+ Dataset intended for land-cover and crop classification tasks based on monthly composites
+ derived from multi-temporal satellite data stored in HDF5 files.
+
+ Dataset Format:
+
+ * HDF5 files containing multi-temporal acquisitions with spectral bands (e.g., B2, B3, …, B12)
+ * Composite images computed as the median across available acquisitions for each month.
+ * Classification labels provided via HDF5 attributes (e.g., 'lc1') with mappings defined for:
+ - Land-cover: using `land_cover_classification_map`
+ - Crops: using `crop_classification_map`
+
+ Dataset Features:
+
+ * Supports two classification tasks: "land-cover" (default) and "crops".
+ * Pre-processing options include center cropping, reverse tiling, and resizing.
+ * Option to save the keys HDF5 for later filtering.
+ * Input channel selection via a mapping between available bands and input bands.
+
+
+ """
land_cover_classification_map={'A10':0, 'A11':0, 'A12':0, 'A13':0,
'A20':0, 'A21':0, 'A30':0,
'A22':1, 'F10':1, 'F20':1,
@@ -70,6 +91,33 @@ def __init__(
save_keys_path = None,
classification_map = "land-cover"
):
+ """Initialize a new instance of Sen4MapDatasetMonthlyComposites.
+
+ This dataset loads data from an HDF5 file object containing multi-temporal satellite data and computes
+ monthly composite images by aggregating acquisitions (via median).
+
+ Args:
+ h5py_file_object: An open h5py.File object containing the dataset.
+ h5data_keys: Optional list of keys to select a subset of data samples from the HDF5 file.
+ If None, all keys are used.
+ crop_size: Optional integer specifying the square crop size for the output image.
+ dataset_bands: Optional list of bands available in the dataset.
+ input_bands: Optional list of bands to be used as input channels.
+ Must be provided along with `dataset_bands`.
+ resize: Boolean flag indicating whether the image should be resized. Default is False.
+ resize_to: Target dimensions [height, width] for resizing. Default is [224, 224].
+ resize_interpolation: Interpolation mode used for resizing. Default is InterpolationMode.BILINEAR.
+ resize_antialiasing: Boolean flag to apply antialiasing during resizing. Default is True.
+ reverse_tile: Boolean flag indicating whether to apply reverse tiling to the image. Default is False.
+ reverse_tile_size: Kernel size for the reverse tiling operation. Must be an odd number >= 3. Default is 3.
+ save_keys_path: Optional file path to save the list of dataset keys.
+ classification_map: String specifying the classification mapping to use ("land-cover" or "crops").
+ Default is "land-cover".
+
+ Raises:
+ ValueError: If `input_bands` is provided without specifying `dataset_bands`.
+ ValueError: If an invalid `classification_map` is provided.
+ """
self.h5data = h5py_file_object
if h5data_keys is None:
if classification_map == "crops": print(f"Crop classification task chosen but no keys supplied. Will fail unless dataset hdf5 files have been filtered. Either filter dataset files or create a filtered set of keys.")
diff --git a/terratorch/datasets/transforms.py b/terratorch/datasets/transforms.py
index b1b7dd45..49f62b7b 100644
--- a/terratorch/datasets/transforms.py
+++ b/terratorch/datasets/transforms.py
@@ -26,9 +26,16 @@ def default_non_image_transform(array):
return array
class FlattenTemporalIntoChannels(ImageOnlyTransform):
- """Flatten the temporal dimension into channels"""
+ """
+ FlattenTemporalIntoChannels is an image transformation that flattens the temporal dimension into the channel dimension.
+ This transform rearranges an input tensor with a temporal dimension into one where the time and channel dimensions
+ are merged. It expects the input to have a fixed number of dimensions defined by N_DIMS_FOR_TEMPORAL.
+ """
def __init__(self):
+ """
+ Initialize the FlattenTemporalIntoChannels transform.
+ """
super().__init__(True, 1)
def apply(self, img, **params):
@@ -43,11 +50,25 @@ def get_transform_init_args_names(self):
class UnflattenTemporalFromChannels(ImageOnlyTransform):
- """Unflatten the temporal dimension from the channel dimension
- Assumes channels first (usually should be run after ToTensorV2)"""
+ """
+ UnflattenTemporalFromChannels is an image transformation that restores the temporal dimension from the channel dimension.
+
+ This transform is typically applied after converting images to a channels-first format (e.g., after ToTensorV2)
+ and rearranges the flattened temporal information back into separate time and channel dimensions.
+ """
def __init__(self, n_timesteps: int | None = None, n_channels: int | None = None):
super().__init__(True, 1)
+ """
+ Initialize the UnflattenTemporalFromChannels transform.
+
+ Args:
+ n_timesteps (int | None): The number of time steps. Must be provided if n_channels is not provided.
+ n_channels (int | None): The number of channels per time step. Must be provided if n_timesteps is not provided.
+
+ Raises:
+ Exception: If neither n_timesteps nor n_channels is provided.
+ """
if n_timesteps is None and n_channels is None:
msg = "One of n_timesteps or n_channels must be provided"
raise Exception(msg)
@@ -68,9 +89,19 @@ def get_transform_init_args_names(self):
return ("n_timesteps", "n_channels")
class FlattenSamplesIntoChannels(ImageOnlyTransform):
- """Flatten the sample and optional temporal dimension into channels"""
+ """
+ FlattenSamplesIntoChannels is an image transformation that merges the sample (and optionally temporal) dimensions into the channel dimension.
+ This transform rearranges an input tensor by flattening the sample dimension, and if specified, also the temporal dimension,
+ thereby concatenating these dimensions into a single channel dimension.
+ """
def __init__(self, time_dim: bool = True):
+ """
+ Initialize the FlattenSamplesIntoChannels transform.
+
+ Args:
+ time_dim (bool): If True, the temporal dimension is included in the flattening process. Default is True.
+ """
super().__init__(True, 1)
self.time_dim = time_dim
@@ -87,9 +118,12 @@ def get_transform_init_args_names(self):
class UnflattenSamplesFromChannels(ImageOnlyTransform):
- """Unflatten the sample and optional the temporal dimension from the channel dimension
- Assumes channels first (usually should be run after ToTensorV2)"""
+ """
+ UnflattenSamplesFromChannels is an image transformation that restores the sample (and optionally temporal) dimensions from the channel dimension.
+ This transform is designed to reverse the flattening performed by FlattenSamplesIntoChannels and is typically applied
+ after converting images to a channels-first format.
+ """
def __init__(
self,
time_dim: bool = True,
@@ -97,6 +131,19 @@ def __init__(
n_timesteps: int | None = None,
n_channels: int | None = None
):
+ """
+ Initialize the UnflattenSamplesFromChannels transform.
+
+ Args:
+ time_dim (bool): If True, the temporal dimension is considered during unflattening.
+ n_samples (int | None): The number of samples.
+ n_timesteps (int | None): The number of time steps.
+ n_channels (int | None): The number of channels per time step.
+
+ Raises:
+ Exception: If time_dim is True and fewer than two of n_channels, n_timesteps, and n_samples are provided.
+ Exception: If time_dim is False and neither n_channels nor n_samples is provided.
+ """
super().__init__(True, 1)
self.time_dim = time_dim
@@ -133,11 +180,24 @@ def get_transform_init_args_names(self):
class Rearrange(ImageOnlyTransform):
- """Flatten the temporal dimension into channels"""
+ """
+ Rearrange is a generic image transformation that reshapes an input tensor using a custom einops pattern.
+
+ This transform allows flexible reordering of tensor dimensions based on the provided pattern and arguments.
+ """
def __init__(
self, rearrange: str, rearrange_args: dict[str, int] | None = None, always_apply: bool = True, p: float = 1
):
+ """
+ Initialize the Rearrange transform.
+
+ Args:
+ rearrange (str): The einops rearrangement pattern to apply.
+ rearrange_args (dict[str, int] | None): Additional arguments for the rearrangement pattern.
+ always_apply (bool): Whether to always apply this transform. Default is True.
+ p (float): The probability of applying the transform. Default is 1.
+ """
super().__init__(always_apply, p)
self.rearrange = rearrange
self.vars = rearrange_args if rearrange_args else {}
@@ -150,9 +210,19 @@ def get_transform_init_args_names(self):
class SelectBands(ImageOnlyTransform):
- """Select a subset of the input bands"""
+ """
+ SelectBands is an image transformation that selects a subset of bands (channels) from an input image.
+
+ This transform uses specified band indices to filter and output only the desired channels from the image tensor.
+ """
def __init__(self, band_indices: list[int]):
+ """
+ Initialize the SelectBands transform.
+
+ Args:
+ band_indices (list[int]): A list of indices specifying which bands to select.
+ """
super().__init__(True, 1)
self.band_indices = band_indices
@@ -171,7 +241,12 @@ def default_non_image_transform(array):
class MultimodalTransforms:
- """Applies albumentations transforms to multiple images"""
+ """
+ MultimodalTransforms applies albumentations transforms to multiple image modalities.
+
+ This class supports both shared transformations across modalities and separate transformations for each modality.
+ It also handles non-image modalities by applying a specified non-image transform.
+ """
def __init__(
self,
transforms: dict | A.Compose,
@@ -179,6 +254,15 @@ def __init__(
non_image_modalities: list[str] | None = None,
non_image_transform: object | None = None,
):
+ """
+ Initialize the MultimodalTransforms.
+
+ Args:
+ transforms (dict or A.Compose): The transformation(s) to apply to the data.
+ shared (bool): If True, the same transform is applied to all modalities; if False, separate transforms are used.
+ non_image_modalities (list[str] | None): List of keys corresponding to non-image modalities.
+ non_image_transform (object | None): A transform to apply to non-image modalities. If None, a default transform is used.
+ """
self.transforms = transforms
self.shared = shared
self.non_image_modalities = non_image_modalities
From 3e8ae9d8ccd982064c017e875ae562f979a07c58 Mon Sep 17 00:00:00 2001
From: Pedro <pedrohenriqueconrado@usp.br>
Date: Mon, 17 Feb 2025 21:18:15 -0300
Subject: [PATCH 15/25] improves data docs
---
docs/data.md | 56 +++++++++++++++++--
terratorch/datamodules/biomassters.py | 36 +++++++++++-
terratorch/datamodules/burn_intensity.py | 25 ++++++++-
terratorch/datamodules/carbonflux.py | 24 +++++++-
terratorch/datamodules/fire_scars.py | 25 ++++++++-
terratorch/datamodules/forestnet.py | 25 ++++++++-
terratorch/datamodules/landslide4sense.py | 22 +++++++-
terratorch/datamodules/m_SA_crop_type.py | 17 ++++++
terratorch/datamodules/m_bigearthnet.py | 17 ++++++
terratorch/datamodules/m_brick_kiln.py | 17 ++++++
terratorch/datamodules/m_cashew_plantation.py | 18 ++++++
.../datamodules/m_chesapeake_landcover.py | 17 ++++++
terratorch/datamodules/m_eurosat.py | 17 ++++++
terratorch/datamodules/m_forestnet.py | 18 ++++++
terratorch/datamodules/m_neontree.py | 17 ++++++
terratorch/datamodules/m_nz_cattle.py | 18 ++++++
terratorch/datamodules/m_pv4ger.py | 18 ++++++
terratorch/datamodules/m_pv4ger_seg.py | 18 ++++++
terratorch/datamodules/m_so2sat.py | 17 ++++++
.../multi_temporal_crop_classification.py | 29 +++++++++-
terratorch/datamodules/open_sentinel_map.py | 27 +++++++++
terratorch/datamodules/openearthmap.py | 21 +++++++
terratorch/datamodules/pastis.py | 24 ++++++++
terratorch/datamodules/sen1floods11.py | 26 ++++++++-
terratorch/datamodules/sen4agrinet.py | 29 ++++++++++
terratorch/datamodules/sen4map.py | 38 ++++++++++++-
26 files changed, 603 insertions(+), 13 deletions(-)
diff --git a/docs/data.md b/docs/data.md
index 8010ee07..f8babecf 100644
--- a/docs/data.md
+++ b/docs/data.md
@@ -10,6 +10,8 @@ TorchGeo provides `GeoDataset` and `NonGeoDataset`.
- If your data is already nicely tiled and ready for consumption by a neural network, you can inherit from `NonGeoDataset`. This is essentially a wrapper of a regular torch dataset.
- If your data consists of large GeoTiffs you would like to sample from during training, you can leverage the powerful `GeoDataset` from torch. This will automatically align your input data and labels and enable a variety of geo-aware samplers.
+For additional examples on fine-tuning a Terratorch model using these components, please refer to the [Prithvi EO Examples](https://github.com/NASA-IMPACT/Prithvi-EO-2.0) repository.
+
## Using Datasets already implemented in TorchGeo
Using existing TorchGeo DataModules is very easy! Just plug them in!
@@ -73,21 +75,67 @@ For the `NonGeoDataset` case, we also provide "generic" datasets and datamodules
## Custom datasets and data modules
-Below is a documented example of how a custom dataset and data module class can be implemented.
+Our custom datasets and data modules are crafted to handle specific data, offering enhanced control and flexibility throughout the workflow.
+In case you want to use Terratorch on your specific data, we invite you to develop your own dataset and data module classes by following the examples below.
### Datasets
-
-#### :::terratorch.datasets.fire_scars
#### :::terratorch.datasets.biomassters
#### :::terratorch.datasets.burn_intensity
#### :::terratorch.datasets.carbonflux
#### :::terratorch.datasets.forestnet
+#### :::terratorch.datasets.fire_scars
+#### :::terratorch.datasets.landslide4sense
+#### :::terratorch.datasets.m_eurosat
+#### :::terratorch.datasets.m_bigearthnet
+#### :::terratorch.datasets.m_brick_kiln
+#### :::terratorch.datasets.m_forestnet
+#### :::terratorch.datasets.m_so2sat
+#### :::terratorch.datasets.m_pv4ger
+#### :::terratorch.datasets.m_cashew_plantation
+#### :::terratorch.datasets.m_nz_cattle
+#### :::terratorch.datasets.m_chesapeake_landcover
+#### :::terratorch.datasets.m_pv4ger_seg
+#### :::terratorch.datasets.m_SA_crop_type
+#### :::terratorch.datasets.m_neontree
+#### :::terratorch.datasets.multi_temporal_crop_classification
+#### :::terratorch.datasets.open_sentinel_map
+#### :::terratorch.datasets.openearthmap
+#### :::terratorch.datasets.pastis
+#### :::terratorch.datasets.sen1floods11
+#### :::terratorch.datasets.sen4agrinet
+#### :::terratorch.datasets.sen4map
### Datamodules
-#### :::terratorch.datamodules.fire_scars
#### :::terratorch.datamodules.biomassters
#### :::terratorch.datamodules.burn_intensity
#### :::terratorch.datamodules.carbonflux
#### :::terratorch.datamodules.forestnet
+#### :::terratorch.datamodules.fire_scars
+#### :::terratorch.datamodules.landslide4sense
+#### :::terratorch.datamodules.m_eurosat
+#### :::terratorch.datamodules.m_bigearthnet
+#### :::terratorch.datamodules.m_brick_kiln
+#### :::terratorch.datamodules.m_forestnet
+#### :::terratorch.datamodules.m_so2sat
+#### :::terratorch.datamodules.m_pv4ger
+#### :::terratorch.datamodules.m_cashew_plantation
+#### :::terratorch.datamodules.m_nz_cattle
+#### :::terratorch.datamodules.m_chesapeake_landcover
+#### :::terratorch.datamodules.m_pv4ger_seg
+#### :::terratorch.datamodules.m_SA_crop_type
+#### :::terratorch.datamodules.m_neontree
+#### :::terratorch.datamodules.multi_temporal_crop_classification
+#### :::terratorch.datamodules.open_sentinel_map
+#### :::terratorch.datamodules.openearthmap
+#### :::terratorch.datamodules.pastis
+#### :::terratorch.datamodules.sen1floods11
+#### :::terratorch.datamodules.sen4agrinet
+#### :::terratorch.datamodules.sen4map
+
+## Transforms
+The transforms module provides a set of specialized image transformations designed to manipulate spatial, temporal, and multimodal data efficiently.
+These transformations allow for greater flexibility when working with multi-temporal, multi-channel, and multi-modal datasets, ensuring that data can be formatted appropriately for different model architectures.
+
+### :::terratorch.datasets.transforms
diff --git a/terratorch/datamodules/biomassters.py b/terratorch/datamodules/biomassters.py
index 4e2cc05d..3c1093c9 100644
--- a/terratorch/datamodules/biomassters.py
+++ b/terratorch/datamodules/biomassters.py
@@ -61,7 +61,7 @@
}
class BioMasstersNonGeoDataModule(NonGeoDataModule):
- """NonGeo datamodule implementation for BioMassters."""
+ """NonGeo LightningDataModule implementation for BioMassters datamodule."""
default_metadata_filename = "The_BioMassters_-_features_metadata.csv.csv"
@@ -88,6 +88,35 @@ def __init__(
use_four_frames: bool = False,
**kwargs: Any,
) -> None:
+ """
+ Initializes the DataModule for the non-geospatial BioMassters datamodule.
+
+ Args:
+ data_root (str): Root directory containing the dataset.
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 4.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ bands (dict[str, Sequence[str]] | Sequence[str], optional): Band configuration; either a dict mapping sensors to bands or a list for the first sensor.
+ Defaults to BioMasstersNonGeo.all_band_names
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training data.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation data.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing data.
+ predict_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for prediction data.
+ aug (AugmentationSequential, optional): Augmentation or normalization to apply. Defaults to normalization if not provided.
+ drop_last (bool, optional): Whether to drop the last incomplete batch. Defaults to True.
+ sensors (Sequence[str], optional): List of sensors to use (e.g., ["S1", "S2"]). Defaults to ["S1", "S2"].
+ as_time_series (bool, optional): Whether to treat data as a time series. Defaults to False.
+ metadata_filename (str, optional): Metadata filename. Defaults to "The_BioMassters_-_features_metadata.csv.csv".
+ max_cloud_percentage (float | None, optional): Maximum allowed cloud percentage. Defaults to None.
+ max_red_mean (float | None, optional): Maximum allowed red band mean. Defaults to None.
+ include_corrupt (bool, optional): Whether to include corrupt data. Defaults to True.
+ subset (float, optional): Fraction of the dataset to use. Defaults to 1.
+ seed (int, optional): Random seed for reproducibility. Defaults to 42.
+ use_four_frames (bool, optional): Whether to use a four frames configuration. Defaults to False.
+ **kwargs: Additional keyword arguments.
+
+ Returns:
+ None.
+ """
super().__init__(BioMasstersNonGeo, batch_size, num_workers, **kwargs)
self.data_root = data_root
self.sensors = sensors
@@ -124,6 +153,11 @@ def __init__(
self.use_four_frames = use_four_frames
def setup(self, stage: str) -> None:
+ """Set up datasets.
+
+ Args:
+ stage: Either fit, validate, test, or predict.
+ """
if stage in ["fit"]:
self.train_dataset = self.dataset_class(
split="train",
diff --git a/terratorch/datamodules/burn_intensity.py b/terratorch/datamodules/burn_intensity.py
index 4c371fcb..e577cf5e 100644
--- a/terratorch/datamodules/burn_intensity.py
+++ b/terratorch/datamodules/burn_intensity.py
@@ -26,7 +26,7 @@
}
class BurnIntensityNonGeoDataModule(NonGeoDataModule):
- """NonGeo datamodule implementation for BurnIntensity."""
+ """NonGeo LightningDataModule implementation for BurnIntensity datamodule."""
def __init__(
self,
@@ -44,6 +44,24 @@ def __init__(
use_metadata: bool = False,
**kwargs: Any,
) -> None:
+ """
+ Initializes the DataModule for the BurnIntensity non-geospatial datamodule.
+
+ Args:
+ data_root (str): Root directory of the dataset.
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 4.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ bands (Sequence[str], optional): List of bands to use. Defaults to BurnIntensityNonGeo.all_band_names.
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing.
+ predict_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for prediction.
+ use_full_data (bool, optional): Whether to use the full dataset or data with less than 25 percent zeros. Defaults to True.
+ no_data_replace (float | None, optional): Value to replace missing data. Defaults to 0.0001.
+ no_label_replace (int | None, optional): Value to replace missing labels. Defaults to -1.
+ use_metadata (bool): Whether to return metadata info (time and location).
+ **kwargs: Additional keyword arguments.
+ """
super().__init__(BurnIntensityNonGeo, batch_size, num_workers, **kwargs)
self.data_root = data_root
@@ -61,6 +79,11 @@ def __init__(
self.use_metadata = use_metadata
def setup(self, stage: str) -> None:
+ """Set up datasets.
+
+ Args:
+ stage: Either fit, validate, test, or predict.
+ """
if stage in ["fit"]:
self.train_dataset = self.dataset_class(
split="train",
diff --git a/terratorch/datamodules/carbonflux.py b/terratorch/datamodules/carbonflux.py
index 7697cc27..0fc9f58d 100644
--- a/terratorch/datamodules/carbonflux.py
+++ b/terratorch/datamodules/carbonflux.py
@@ -39,7 +39,7 @@
class CarbonFluxNonGeoDataModule(NonGeoDataModule):
- """NonGeo datamodule implementation for Landslide4Sense."""
+ """NonGeo LightningDataModule implementation for Carbon FLux dataset."""
def __init__(
self,
@@ -56,6 +56,23 @@ def __init__(
use_metadata: bool = False,
**kwargs: Any,
) -> None:
+ """
+ Initializes the CarbonFluxNonGeoDataModule.
+
+ Args:
+ data_root (str): Root directory of the dataset.
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 4.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ bands (Sequence[str], optional): List of bands to use. Defaults to CarbonFluxNonGeo.all_band_names.
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training data.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation data.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing data.
+ predict_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for prediction data.
+ aug (AugmentationSequential, optional): Augmentation sequence; if None, applies multimodal normalization.
+ no_data_replace (float | None, optional): Value to replace missing data. Defaults to 0.0001.
+ use_metadata (bool): Whether to return metadata info.
+ **kwargs: Additional keyword arguments.
+ """
super().__init__(CarbonFluxNonGeo, batch_size, num_workers, **kwargs)
self.data_root = data_root
@@ -79,6 +96,11 @@ def __init__(
self.use_metadata = use_metadata
def setup(self, stage: str) -> None:
+ """Set up datasets.
+
+ Args:
+ stage: Either fit, validate, test, or predict.
+ """
if stage in ["fit"]:
self.train_dataset = self.dataset_class(
split="train",
diff --git a/terratorch/datamodules/fire_scars.py b/terratorch/datamodules/fire_scars.py
index 0938f8d6..e5e8d839 100644
--- a/terratorch/datamodules/fire_scars.py
+++ b/terratorch/datamodules/fire_scars.py
@@ -35,7 +35,7 @@
class FireScarsNonGeoDataModule(NonGeoDataModule):
- """NonGeo datamodule implementation for Fire Scars"""
+ """NonGeo LightningDataModule implementation for Fire Scars dataset."""
def __init__(
self,
@@ -53,6 +53,24 @@ def __init__(
use_metadata: bool = False,
**kwargs: Any,
) -> None:
+ """
+ Initializes the FireScarsNonGeoDataModule.
+
+ Args:
+ data_root (str): Root directory of the dataset.
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 4.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ bands (Sequence[str], optional): List of band names. Defaults to FireScarsNonGeo.all_band_names.
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing.
+ predict_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for prediction.
+ drop_last (bool, optional): Whether to drop the last incomplete batch. Defaults to True.
+ no_data_replace (float | None, optional): Replacement value for missing data. Defaults to 0.
+ no_label_replace (int | None, optional): Replacement value for missing labels. Defaults to -1.
+ use_metadata (bool): Whether to return metadata info.
+ **kwargs: Additional keyword arguments.
+ """
super().__init__(FireScarsNonGeo, batch_size, num_workers, **kwargs)
self.data_root = data_root
@@ -70,6 +88,11 @@ def __init__(
self.use_metadata = use_metadata
def setup(self, stage: str) -> None:
+ """Set up datasets.
+
+ Args:
+ stage: Either fit, validate, test, or predict.
+ """
if stage in ["fit"]:
self.train_dataset = self.dataset_class(
split="train",
diff --git a/terratorch/datamodules/forestnet.py b/terratorch/datamodules/forestnet.py
index f46dd567..e1f15ac5 100644
--- a/terratorch/datamodules/forestnet.py
+++ b/terratorch/datamodules/forestnet.py
@@ -30,7 +30,7 @@
class ForestNetNonGeoDataModule(NonGeoDataModule):
- """NonGeo datamodule implementation for Landslide4Sense."""
+ """NonGeo LightningDataModule implementation for Landslide4Sense dataset."""
def __init__(
self,
@@ -48,6 +48,24 @@ def __init__(
use_metadata: bool = False,
**kwargs: Any,
) -> None:
+ """
+ Initializes the ForestNetNonGeoDataModule.
+
+ Args:
+ data_root (str): Directory containing the dataset.
+ batch_size (int, optional): Batch size for data loaders. Defaults to 4.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ label_map (dict[str, int], optional): Mapping of labels to integers. Defaults to ForestNetNonGeo.default_label_map.
+ bands (Sequence[str], optional): List of band names to use. Defaults to ForestNetNonGeo.all_band_names.
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training data.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation data.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing data.
+ predict_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for prediction.
+ fraction (float, optional): Fraction of data to use. Defaults to 1.0.
+ aug (AugmentationSequential, optional): Augmentation/normalization pipeline; if None, uses Normalize.
+ use_metadata (bool): Whether to return metadata info.
+ **kwargs (Any): Additional keyword arguments.
+ """
super().__init__(ForestNetNonGeo, batch_size, num_workers, **kwargs)
self.data_root = data_root
@@ -64,6 +82,11 @@ def __init__(
self.use_metadata = use_metadata
def setup(self, stage: str) -> None:
+ """Set up datasets.
+
+ Args:
+ stage: Either fit, validate, test, or predict.
+ """
if stage in ["fit"]:
self.train_dataset = self.dataset_class(
split="train",
diff --git a/terratorch/datamodules/landslide4sense.py b/terratorch/datamodules/landslide4sense.py
index 84df0188..f4d662ef 100644
--- a/terratorch/datamodules/landslide4sense.py
+++ b/terratorch/datamodules/landslide4sense.py
@@ -45,7 +45,7 @@
class Landslide4SenseNonGeoDataModule(NonGeoDataModule):
- """NonGeo datamodule implementation for Landslide4Sense."""
+ """NonGeo LightningDataModule implementation for Landslide4Sense dataset."""
def __init__(
self,
@@ -60,6 +60,21 @@ def __init__(
aug: AugmentationSequential = None,
**kwargs: Any,
) -> None:
+ """
+ Initializes the Landslide4SenseNonGeoDataModule.
+
+ Args:
+ data_root (str): Root directory of the dataset.
+ batch_size (int, optional): Batch size for data loaders. Defaults to 4.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ bands (Sequence[str], optional): List of band names to use. Defaults to Landslide4SenseNonGeo.all_band_names.
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training data.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation data.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing data.
+ predict_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for prediction data.
+ aug (AugmentationSequential, optional): Augmentation pipeline; if None, applies normalization using computed means and stds.
+ **kwargs (Any): Additional keyword arguments.
+ """
super().__init__(Landslide4SenseNonGeo, batch_size, num_workers, **kwargs)
self.data_root = data_root
@@ -75,6 +90,11 @@ def __init__(
)
def setup(self, stage: str) -> None:
+ """Set up datasets.
+
+ Args:
+ stage: Either fit, validate, test, or predict.
+ """
if stage in ["fit"]:
self.train_dataset = self.dataset_class(
split="train",
diff --git a/terratorch/datamodules/m_SA_crop_type.py b/terratorch/datamodules/m_SA_crop_type.py
index b3b3db74..81183c81 100644
--- a/terratorch/datamodules/m_SA_crop_type.py
+++ b/terratorch/datamodules/m_SA_crop_type.py
@@ -41,6 +41,8 @@
class MSACropTypeNonGeoDataModule(GeobenchDataModule):
+ """NonGeo LightningDataModule implementation for M-SA-CropType dataset."""
+
def __init__(
self,
batch_size: int = 8,
@@ -54,6 +56,21 @@ def __init__(
partition: str = "default",
**kwargs: Any,
) -> None:
+ """
+ Initializes the MSACropTypeNonGeoDataModule for the MSACropTypeNonGeo dataset.
+
+ Args:
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 8.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ data_root (str, optional): Root directory of the dataset. Defaults to "./".
+ bands (Sequence[str] | None, optional): List of bands to use. Defaults to None.
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing.
+ aug (AugmentationSequential, optional): Augmentation/normalization pipeline. Defaults to None.
+ partition (str, optional): Partition size. Defaults to "default".
+ **kwargs (Any): Additional keyword arguments.
+ """
super().__init__(
MSACropTypeNonGeo,
MEANS,
diff --git a/terratorch/datamodules/m_bigearthnet.py b/terratorch/datamodules/m_bigearthnet.py
index 854c7483..1a80ace2 100644
--- a/terratorch/datamodules/m_bigearthnet.py
+++ b/terratorch/datamodules/m_bigearthnet.py
@@ -39,6 +39,8 @@
class MBigEarthNonGeoDataModule(GeobenchDataModule):
+ """NonGeo LightningDataModule implementation for M-BigEarthNet dataset."""
+
def __init__(
self,
batch_size: int = 8,
@@ -52,6 +54,21 @@ def __init__(
partition: str = "default",
**kwargs: Any,
) -> None:
+ """
+ Initializes the MBigEarthNonGeoDataModule for the M-BigEarthNet dataset.
+
+ Args:
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 8.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ data_root (str, optional): Root directory of the dataset. Defaults to "./".
+ bands (Sequence[str] | None, optional): List of bands to use. Defaults to None.
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing.
+ aug (AugmentationSequential, optional): Augmentation/normalization pipeline. Defaults to None.
+ partition (str, optional): Partition size. Defaults to "default".
+ **kwargs (Any): Additional keyword arguments.
+ """
super().__init__(
MBigEarthNonGeo,
MEANS,
diff --git a/terratorch/datamodules/m_brick_kiln.py b/terratorch/datamodules/m_brick_kiln.py
index 57f9efef..b8a7e8bc 100644
--- a/terratorch/datamodules/m_brick_kiln.py
+++ b/terratorch/datamodules/m_brick_kiln.py
@@ -41,6 +41,8 @@
class MBrickKilnNonGeoDataModule(GeobenchDataModule):
+ """NonGeo LightningDataModule implementation for M-BrickKiln dataset."""
+
def __init__(
self,
batch_size: int = 8,
@@ -54,6 +56,21 @@ def __init__(
partition: str = "default",
**kwargs: Any,
) -> None:
+ """
+ Initializes the MBrickKilnNonGeoDataModule for the M-BrickKilnNonGeo dataset.
+
+ Args:
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 8.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ data_root (str, optional): Root directory of the dataset. Defaults to "./".
+ bands (Sequence[str] | None, optional): List of bands to use. Defaults to None.
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing.
+ aug (AugmentationSequential, optional): Augmentation/normalization pipeline. Defaults to None.
+ partition (str, optional): Partition size. Defaults to "default".
+ **kwargs (Any): Additional keyword arguments.
+ """
super().__init__(
MBrickKilnNonGeo,
MEANS,
diff --git a/terratorch/datamodules/m_cashew_plantation.py b/terratorch/datamodules/m_cashew_plantation.py
index af718fef..94d3e67c 100644
--- a/terratorch/datamodules/m_cashew_plantation.py
+++ b/terratorch/datamodules/m_cashew_plantation.py
@@ -41,6 +41,8 @@
class MBeninSmallHolderCashewsNonGeoDataModule(GeobenchDataModule):
+ """NonGeo LightningDataModule implementation for M-Cashew Plantation dataset."""
+
def __init__(
self,
batch_size: int = 8,
@@ -55,6 +57,22 @@ def __init__(
use_metadata: bool = False, # noqa: FBT002, FBT001
**kwargs: Any,
) -> None:
+ """
+ Initializes the MBeninSmallHolderCashewsNonGeoDataModule for the M-BeninSmallHolderCashewsNonGeo dataset.
+
+ Args:
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 8.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ data_root (str, optional): Root directory of the dataset. Defaults to "./".
+ bands (Sequence[str] | None, optional): List of bands to use. Defaults to None.
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing.
+ aug (AugmentationSequential, optional): Augmentation/normalization pipeline. Defaults to None.
+ partition (str, optional): Partition size. Defaults to "default".
+ use_metadata (bool): Whether to return metadata info.
+ **kwargs (Any): Additional keyword arguments.
+ """
super().__init__(
MBeninSmallHolderCashewsNonGeo,
MEANS,
diff --git a/terratorch/datamodules/m_chesapeake_landcover.py b/terratorch/datamodules/m_chesapeake_landcover.py
index e104adef..bf049d99 100644
--- a/terratorch/datamodules/m_chesapeake_landcover.py
+++ b/terratorch/datamodules/m_chesapeake_landcover.py
@@ -13,6 +13,8 @@
class MChesapeakeLandcoverNonGeoDataModule(GeobenchDataModule):
+ """NonGeo LightningDataModule implementation for M-ChesapeakeLandcover dataset."""
+
def __init__(
self,
batch_size: int = 8,
@@ -26,6 +28,21 @@ def __init__(
partition: str = "default",
**kwargs: Any,
) -> None:
+ """
+ Initializes the MChesapeakeLandcoverNonGeoDataModule for the M-BigEarthNet dataset.
+
+ Args:
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 8.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ data_root (str, optional): Root directory of the dataset. Defaults to "./".
+ bands (Sequence[str] | None, optional): List of bands to use. Defaults to None.
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing.
+ aug (AugmentationSequential, optional): Augmentation/normalization pipeline. Defaults to None.
+ partition (str, optional): Partition size. Defaults to "default".
+ **kwargs (Any): Additional keyword arguments.
+ """
super().__init__(
MChesapeakeLandcoverNonGeo,
MEANS,
diff --git a/terratorch/datamodules/m_eurosat.py b/terratorch/datamodules/m_eurosat.py
index 6f1114bb..837e8d5c 100644
--- a/terratorch/datamodules/m_eurosat.py
+++ b/terratorch/datamodules/m_eurosat.py
@@ -41,6 +41,8 @@
class MEuroSATNonGeoDataModule(GeobenchDataModule):
+ """NonGeo LightningDataModule implementation for M-EuroSAT dataset."""
+
def __init__(
self,
batch_size: int = 8,
@@ -54,6 +56,21 @@ def __init__(
partition: str = "default",
**kwargs: Any,
) -> None:
+ """
+ Initializes the MEuroSATNonGeoDataModule for the MEuroSATNonGeo dataset.
+
+ Args:
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 8.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ data_root (str, optional): Root directory of the dataset. Defaults to "./".
+ bands (Sequence[str] | None, optional): List of bands to use. Defaults to None.
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing.
+ aug (AugmentationSequential, optional): Augmentation/normalization pipeline. Defaults to None.
+ partition (str, optional): Partition size. Defaults to "default".
+ **kwargs (Any): Additional keyword arguments.
+ """
super().__init__(
MEuroSATNonGeo,
MEANS,
diff --git a/terratorch/datamodules/m_forestnet.py b/terratorch/datamodules/m_forestnet.py
index 01756c12..c143b168 100644
--- a/terratorch/datamodules/m_forestnet.py
+++ b/terratorch/datamodules/m_forestnet.py
@@ -27,6 +27,8 @@
class MForestNetNonGeoDataModule(GeobenchDataModule):
+ """NonGeo LightningDataModule implementation for M-ForestNet dataset."""
+
def __init__(
self,
batch_size: int = 8,
@@ -41,6 +43,22 @@ def __init__(
use_metadata: bool = False, # noqa: FBT002, FBT001
**kwargs: Any,
) -> None:
+ """
+ Initializes the MForestNetNonGeoDataModule for the MForestNetNonGeo dataset.
+
+ Args:
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 8.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ data_root (str, optional): Root directory of the dataset. Defaults to "./".
+ bands (Sequence[str] | None, optional): List of bands to use. Defaults to None.
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing.
+ aug (AugmentationSequential, optional): Augmentation/normalization pipeline. Defaults to None.
+ partition (str, optional): Partition size. Defaults to "default".
+ use_metadata (bool): Whether to return metadata info.
+ **kwargs (Any): Additional keyword arguments.
+ """
super().__init__(
MForestNetNonGeo,
MEANS,
diff --git a/terratorch/datamodules/m_neontree.py b/terratorch/datamodules/m_neontree.py
index ce7a89a9..161fed5d 100644
--- a/terratorch/datamodules/m_neontree.py
+++ b/terratorch/datamodules/m_neontree.py
@@ -25,6 +25,8 @@
class MNeonTreeNonGeoDataModule(GeobenchDataModule):
+ """NonGeo LightningDataModule implementation for M-NeonTree dataset."""
+
def __init__(
self,
batch_size: int = 8,
@@ -38,6 +40,21 @@ def __init__(
partition: str = "default",
**kwargs: Any,
) -> None:
+ """
+ Initializes the MNeonTreeNonGeoDataModule for the MNeonTreeNonGeo dataset.
+
+ Args:
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 8.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ data_root (str, optional): Root directory of the dataset. Defaults to "./".
+ bands (Sequence[str] | None, optional): List of bands to use. Defaults to None.
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing.
+ aug (AugmentationSequential, optional): Augmentation/normalization pipeline. Defaults to None.
+ partition (str, optional): Partition size. Defaults to "default".
+ **kwargs (Any): Additional keyword arguments.
+ """
super().__init__(
MNeonTreeNonGeo,
MEANS,
diff --git a/terratorch/datamodules/m_nz_cattle.py b/terratorch/datamodules/m_nz_cattle.py
index 3ce03eb9..1a3004f7 100644
--- a/terratorch/datamodules/m_nz_cattle.py
+++ b/terratorch/datamodules/m_nz_cattle.py
@@ -13,6 +13,8 @@
class MNzCattleNonGeoDataModule(GeobenchDataModule):
+ """NonGeo LightningDataModule implementation for M-NZCattle dataset."""
+
def __init__(
self,
batch_size: int = 8,
@@ -27,6 +29,22 @@ def __init__(
use_metadata: bool = False, # noqa: FBT002, FBT001
**kwargs: Any,
) -> None:
+ """
+ Initializes the MNzCattleNonGeoDataModule for the MNzCattleNonGeo dataset.
+
+ Args:
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 8.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ data_root (str, optional): Root directory of the dataset. Defaults to "./".
+ bands (Sequence[str] | None, optional): List of bands to use. Defaults to None.
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing.
+ aug (AugmentationSequential, optional): Augmentation/normalization pipeline. Defaults to None.
+ partition (str, optional): Partition size. Defaults to "default".
+ use_metadata (bool): Whether to return metadata info.
+ **kwargs (Any): Additional keyword arguments.
+ """
super().__init__(
MNzCattleNonGeo,
MEANS,
diff --git a/terratorch/datamodules/m_pv4ger.py b/terratorch/datamodules/m_pv4ger.py
index a88af2bb..d1cf8af8 100644
--- a/terratorch/datamodules/m_pv4ger.py
+++ b/terratorch/datamodules/m_pv4ger.py
@@ -17,6 +17,8 @@
class MPv4gerNonGeoDataModule(GeobenchDataModule):
+ """NonGeo LightningDataModule implementation for M-Pv4ger dataset."""
+
def __init__(
self,
batch_size: int = 8,
@@ -31,6 +33,22 @@ def __init__(
use_metadata: bool = False, # noqa: FBT002, FBT001
**kwargs: Any,
) -> None:
+ """
+ Initializes the MPv4gerNonGeoDataModule for the MPv4gerNonGeo dataset.
+
+ Args:
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 8.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ data_root (str, optional): Root directory of the dataset. Defaults to "./".
+ bands (Sequence[str] | None, optional): List of bands to use. Defaults to None.
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing.
+ aug (AugmentationSequential, optional): Augmentation/normalization pipeline. Defaults to None.
+ partition (str, optional): Partition size. Defaults to "default".
+ use_metadata (bool): Whether to return metadata info.
+ **kwargs (Any): Additional keyword arguments.
+ """
super().__init__(
MPv4gerNonGeo,
MEANS,
diff --git a/terratorch/datamodules/m_pv4ger_seg.py b/terratorch/datamodules/m_pv4ger_seg.py
index 1bb2d73a..4bdba30a 100644
--- a/terratorch/datamodules/m_pv4ger_seg.py
+++ b/terratorch/datamodules/m_pv4ger_seg.py
@@ -13,6 +13,8 @@
class MPv4gerSegNonGeoDataModule(GeobenchDataModule):
+ """NonGeo LightningDataModule implementation for M-Pv4gerSeg dataset."""
+
def __init__(
self,
batch_size: int = 8,
@@ -27,6 +29,22 @@ def __init__(
use_metadata: bool = False, # noqa: FBT002, FBT001
**kwargs: Any,
) -> None:
+ """
+ Initializes the MPv4gerNonGeoDataModule for the MPv4gerSegNonGeo dataset.
+
+ Args:
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 8.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ data_root (str, optional): Root directory of the dataset. Defaults to "./".
+ bands (Sequence[str] | None, optional): List of bands to use. Defaults to None.
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing.
+ aug (AugmentationSequential, optional): Augmentation/normalization pipeline. Defaults to None.
+ partition (str, optional): Partition size. Defaults to "default".
+ use_metadata (bool): Whether to return metadata info.
+ **kwargs (Any): Additional keyword arguments.
+ """
super().__init__(
MPv4gerSegNonGeo,
MEANS,
diff --git a/terratorch/datamodules/m_so2sat.py b/terratorch/datamodules/m_so2sat.py
index 023d3744..24449552 100644
--- a/terratorch/datamodules/m_so2sat.py
+++ b/terratorch/datamodules/m_so2sat.py
@@ -51,6 +51,8 @@
class MSo2SatNonGeoDataModule(GeobenchDataModule):
+ """NonGeo LightningDataModule implementation for M-So2Sat dataset."""
+
def __init__(
self,
batch_size: int = 8,
@@ -64,6 +66,21 @@ def __init__(
partition: str = "default",
**kwargs: Any,
) -> None:
+ """
+ Initializes the MSo2SatNonGeoDataModule for the MSo2SatNonGeo dataset.
+
+ Args:
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 8.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ data_root (str, optional): Root directory of the dataset. Defaults to "./".
+ bands (Sequence[str] | None, optional): List of bands to use. Defaults to None.
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing.
+ aug (AugmentationSequential, optional): Augmentation/normalization pipeline. Defaults to None.
+ partition (str, optional): Partition size. Defaults to "default".
+ **kwargs (Any): Additional keyword arguments.
+ """
super().__init__(
MSo2SatNonGeo,
MEANS,
diff --git a/terratorch/datamodules/multi_temporal_crop_classification.py b/terratorch/datamodules/multi_temporal_crop_classification.py
index 14452af4..4b699a8a 100644
--- a/terratorch/datamodules/multi_temporal_crop_classification.py
+++ b/terratorch/datamodules/multi_temporal_crop_classification.py
@@ -30,7 +30,7 @@
class MultiTemporalCropClassificationDataModule(NonGeoDataModule):
- """NonGeo datamodule implementation for multi-temporal crop classification."""
+ """NonGeo LightningDataModule implementation for multi-temporal crop classification."""
def __init__(
self,
@@ -50,6 +50,28 @@ def __init__(
use_metadata: bool = False,
**kwargs: Any,
) -> None:
+ """
+ Initializes the MultiTemporalCropClassificationDataModule for multi-temporal crop classification.
+
+ Args:
+ data_root (str): Directory containing the dataset.
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 4.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ bands (Sequence[str], optional): List of bands to use. Defaults to MultiTemporalCropClassification.all_band_names.
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training data.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation data.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing data.
+ predict_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for prediction data.
+ drop_last (bool, optional): Whether to drop the last incomplete batch during training. Defaults to True.
+ no_data_replace (float | None, optional): Replacement value for missing data. Defaults to 0.
+ no_label_replace (int | None, optional): Replacement value for missing labels. Defaults to -1.
+ expand_temporal_dimension (bool, optional): Go from shape (time*channels, h, w) to (channels, time, h, w).
+ Defaults to True.
+ reduce_zero_label (bool, optional): Subtract 1 from all labels. Useful when labels start from 1 instead of the
+ expected 0. Defaults to True.
+ use_metadata (bool): Whether to return metadata info (time and location).
+ **kwargs: Additional keyword arguments.
+ """
super().__init__(MultiTemporalCropClassification, batch_size, num_workers, **kwargs)
self.data_root = data_root
@@ -69,6 +91,11 @@ def __init__(
self.use_metadata = use_metadata
def setup(self, stage: str) -> None:
+ """Set up datasets.
+
+ Args:
+ stage: Either fit, validate, test, or predict.
+ """
if stage in ["fit"]:
self.train_dataset = self.dataset_class(
split="train",
diff --git a/terratorch/datamodules/open_sentinel_map.py b/terratorch/datamodules/open_sentinel_map.py
index 36365b21..c50f0f71 100644
--- a/terratorch/datamodules/open_sentinel_map.py
+++ b/terratorch/datamodules/open_sentinel_map.py
@@ -8,6 +8,8 @@
class OpenSentinelMapDataModule(NonGeoDataModule):
+ """NonGeo LightningDataModule implementation for Open Sentinel Map."""
+
def __init__(
self,
bands: list[str] | None = None,
@@ -23,6 +25,26 @@ def __init__(
truncate_image: int | None = None,
**kwargs: Any,
) -> None:
+ """
+ Initializes the OpenSentinelMapDataModule for the Open Sentinel Map dataset.
+
+ Args:
+ bands (list[str] | None, optional): List of bands to use. Defaults to None.
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 8.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ data_root (str, optional): Root directory of the dataset. Defaults to "./".
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training data.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation data.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing data.
+ predict_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for prediction data.
+ spatial_interpolate_and_stack_temporally (bool, optional): If True, the bands are interpolated and concatenated over time.
+ Default is True.
+ pad_image (int | None, optional): Number of timesteps to pad the time dimension of the image.
+ If None, no padding is applied.
+ truncate_image (int | None, optional): Number of timesteps to truncate the time dimension of the image.
+ If None, no truncation is performed.
+ **kwargs: Additional keyword arguments.
+ """
super().__init__(
OpenSentinelMap,
batch_size=batch_size,
@@ -41,6 +63,11 @@ def __init__(
self.kwargs = kwargs
def setup(self, stage: str) -> None:
+ """Set up datasets.
+
+ Args:
+ stage: Either fit, validate, test, or predict.
+ """
if stage in ["fit"]:
self.train_dataset = OpenSentinelMap(
split="train",
diff --git a/terratorch/datamodules/openearthmap.py b/terratorch/datamodules/openearthmap.py
index c4869ef3..41fd170d 100644
--- a/terratorch/datamodules/openearthmap.py
+++ b/terratorch/datamodules/openearthmap.py
@@ -21,6 +21,8 @@
}
class OpenEarthMapNonGeoDataModule(NonGeoDataModule):
+ """NonGeo LightningDataModule implementation for Open Earth Map."""
+
def __init__(
self,
batch_size: int = 8,
@@ -33,6 +35,20 @@ def __init__(
aug: AugmentationSequential = None,
**kwargs: Any
) -> None:
+ """
+ Initializes the OpenEarthMapNonGeoDataModule for the Open Earth Map dataset.
+
+ Args:
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 8.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ data_root (str, optional): Root directory of the dataset. Defaults to "./".
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training data.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation data.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for test data.
+ predict_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for prediction data.
+ aug (AugmentationSequential, optional): Augmentation pipeline; if None, defaults to normalization using computed means and stds.
+ **kwargs: Additional keyword arguments. Can include 'bands' (list[str]) to specify the bands; defaults to OpenEarthMapNonGeo.all_band_names if not provided.
+ """
super().__init__(OpenEarthMapNonGeo, batch_size, num_workers, **kwargs)
bands = kwargs.get("bands", OpenEarthMapNonGeo.all_band_names)
@@ -46,6 +62,11 @@ def __init__(
self.aug = AugmentationSequential(K.Normalize(self.means, self.stds), data_keys=["image"]) if aug is None else aug
def setup(self, stage: str) -> None:
+ """Set up datasets.
+
+ Args:
+ stage: Either fit, validate, test, or predict.
+ """
if stage in ["fit"]:
self.train_dataset = self.dataset_class(
split="train", data_root=self.data_root, transform=self.train_transform, **self.kwargs
diff --git a/terratorch/datamodules/pastis.py b/terratorch/datamodules/pastis.py
index 7b3743c3..16f7b11d 100644
--- a/terratorch/datamodules/pastis.py
+++ b/terratorch/datamodules/pastis.py
@@ -8,6 +8,8 @@
class PASTISDataModule(NonGeoDataModule):
+ """NonGeo LightningDataModule implementation for PASTIS."""
+
def __init__(
self,
batch_size: int = 8,
@@ -21,6 +23,23 @@ def __init__(
predict_transform: A.Compose | None | list[A.BasicTransform] = None,
**kwargs: Any,
) -> None:
+ """
+ Initializes the PASTISDataModule for the PASTIS dataset.
+
+ Args:
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 8.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ data_root (str, optional): Directory containing the dataset. Defaults to "./".
+ truncate_image (int, optional): Truncate the time dimension of the image to
+ a specified number of timesteps. If None, no truncation is performed.
+ pad_image (int, optional): Pad the time dimension of the image to a specified
+ number of timesteps. If None, no padding is applied.
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training data.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation data.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for testing data.
+ predict_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for prediction data.
+ **kwargs: Additional keyword arguments.
+ """
super().__init__(
PASTIS,
batch_size=batch_size,
@@ -37,6 +56,11 @@ def __init__(
self.kwargs = kwargs
def setup(self, stage: str) -> None:
+ """Set up datasets.
+
+ Args:
+ stage: Either fit, validate, test, or predict.
+ """
if stage in ["fit"]:
self.train_dataset = PASTIS(
folds=[1, 2, 3],
diff --git a/terratorch/datamodules/sen1floods11.py b/terratorch/datamodules/sen1floods11.py
index b64902fe..38afed65 100644
--- a/terratorch/datamodules/sen1floods11.py
+++ b/terratorch/datamodules/sen1floods11.py
@@ -46,7 +46,7 @@
}
class Sen1Floods11NonGeoDataModule(NonGeoDataModule):
- """NonGeo Fire Scars data module implementation"""
+ """NonGeo LightningDataModule implementation for Fire Scars."""
def __init__(
self,
@@ -65,6 +65,25 @@ def __init__(
use_metadata: bool = False,
**kwargs: Any,
) -> None:
+ """
+ Initializes the Sen1Floods11NonGeoDataModule.
+
+ Args:
+ data_root (str): Root directory of the dataset.
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 4.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ bands (Sequence[str], optional): List of bands to use. Defaults to Sen1Floods11NonGeo.all_band_names.
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training data.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation data.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for test data.
+ predict_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for prediction data.
+ drop_last (bool, optional): Whether to drop the last incomplete batch. Defaults to True.
+ constant_scale (float, optional): Scale constant applied to the dataset. Defaults to 0.0001.
+ no_data_replace (float | None, optional): Replacement value for missing data. Defaults to 0.
+ no_label_replace (int | None, optional): Replacement value for missing labels. Defaults to -1.
+ use_metadata (bool): Whether to return metadata info (time and location).
+ **kwargs: Additional keyword arguments.
+ """
super().__init__(Sen1Floods11NonGeo, batch_size, num_workers, **kwargs)
self.data_root = data_root
@@ -83,6 +102,11 @@ def __init__(
self.use_metadata = use_metadata
def setup(self, stage: str) -> None:
+ """Set up datasets.
+
+ Args:
+ stage: Either fit, validate, test, or predict.
+ """
if stage in ["fit"]:
self.train_dataset = self.dataset_class(
split="train",
diff --git a/terratorch/datamodules/sen4agrinet.py b/terratorch/datamodules/sen4agrinet.py
index 9fd67739..05558d74 100644
--- a/terratorch/datamodules/sen4agrinet.py
+++ b/terratorch/datamodules/sen4agrinet.py
@@ -8,6 +8,8 @@
class Sen4AgriNetDataModule(NonGeoDataModule):
+ """NonGeo LightningDataModule implementation for Sen4AgriNet."""
+
def __init__(
self,
bands: list[str] | None = None,
@@ -25,6 +27,28 @@ def __init__(
linear_encoder: dict = None,
**kwargs: Any,
) -> None:
+ """
+ Initializes the Sen4AgriNetDataModule for the Sen4AgriNet dataset.
+
+ Args:
+ bands (list[str] | None, optional): List of bands to use. Defaults to None.
+ batch_size (int, optional): Batch size for DataLoaders. Defaults to 8.
+ num_workers (int, optional): Number of workers for data loading. Defaults to 0.
+ data_root (str, optional): Root directory of the dataset. Defaults to "./".
+ train_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for training data.
+ val_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for validation data.
+ test_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for test data.
+ predict_transform (A.Compose | None | list[A.BasicTransform], optional): Transformations for prediction data.
+ seed (int, optional): Random seed for reproducibility. Defaults to 42.
+ scenario (str): Defines the splitting scenario to use. Options are:
+ - 'random': Random split of the data.
+ - 'spatial': Split by geographical regions (Catalonia and France).
+ - 'spatio-temporal': Split by region and year (France 2019 and Catalonia 2020).
+ requires_norm (bool, optional): Whether normalization is required. Defaults to True.
+ binary_labels (bool, optional): Whether to use binary labels. Defaults to False.
+ linear_encoder (dict, optional): Mapping for label encoding. Defaults to None.
+ **kwargs: Additional keyword arguments.
+ """
super().__init__(
Sen4AgriNet,
batch_size=batch_size,
@@ -45,6 +69,11 @@ def __init__(
self.kwargs = kwargs
def setup(self, stage: str) -> None:
+ """Set up datasets.
+
+ Args:
+ stage: Either fit, validate, test, or predict.
+ """
if stage in ["fit"]:
self.train_dataset = Sen4AgriNet(
split="train",
diff --git a/terratorch/datamodules/sen4map.py b/terratorch/datamodules/sen4map.py
index 0c68e615..983e5c3a 100644
--- a/terratorch/datamodules/sen4map.py
+++ b/terratorch/datamodules/sen4map.py
@@ -8,6 +8,8 @@
class Sen4MapLucasDataModule(pl.LightningDataModule):
+ """NonGeo LightningDataModule implementation for Sen4map."""
+
def __init__(
self,
batch_size,
@@ -23,7 +25,36 @@ def __init__(
val_hdf5_keys_path = None,
**kwargs
):
-
+ """
+ Initializes the Sen4MapLucasDataModule for handling Sen4Map monthly composites.
+
+ Args:
+ batch_size (int): Batch size for DataLoaders.
+ num_workers (int): Number of worker processes for data loading.
+ prefetch_factor (int, optional): Number of samples to prefetch per worker. Defaults to 0.
+ train_hdf5_path (str, optional): Path to the training HDF5 file.
+ train_hdf5_keys_path (str, optional): Path to the training HDF5 keys file.
+ test_hdf5_path (str, optional): Path to the testing HDF5 file.
+ test_hdf5_keys_path (str, optional): Path to the testing HDF5 keys file.
+ val_hdf5_path (str, optional): Path to the validation HDF5 file.
+ val_hdf5_keys_path (str, optional): Path to the validation HDF5 keys file.
+ train_hdf5_keys_save_path (str, optional): (from kwargs) Path to save generated train keys.
+ test_hdf5_keys_save_path (str, optional): (from kwargs) Path to save generated test keys.
+ val_hdf5_keys_save_path (str, optional): (from kwargs) Path to save generated validation keys.
+ shuffle (bool, optional): Global shuffle flag.
+ train_shuffle (bool, optional): Shuffle flag for training data; defaults to global shuffle if unset.
+ val_shuffle (bool, optional): Shuffle flag for validation data.
+ test_shuffle (bool, optional): Shuffle flag for test data.
+ train_data_fraction (float, optional): Fraction of training data to use. Defaults to 1.0.
+ val_data_fraction (float, optional): Fraction of validation data to use. Defaults to 1.0.
+ test_data_fraction (float, optional): Fraction of test data to use. Defaults to 1.0.
+ all_hdf5_data_path (str, optional): General HDF5 data path for all splits. If provided, overrides specific paths.
+ resize (bool, optional): Whether to resize images. Defaults to False.
+ resize_to (int or tuple, optional): Target size for resizing images.
+ resize_interpolation (str, optional): Interpolation mode for resizing ('bilinear', 'bicubic', etc.).
+ resize_antialiasing (bool, optional): Whether to apply antialiasing during resizing. Defaults to True.
+ **kwargs: Additional keyword arguments.
+ """
self.prepare_data_per_node = False
self._log_hyperparams = None
self.allow_zero_length_dataloader_with_multiple_devices = False
@@ -103,6 +134,11 @@ def _load_hdf5_keys_from_path(self, path, fraction=1.0):
return keys[:int(fraction*len(keys))]
def setup(self, stage: str):
+ """Set up datasets.
+
+ Args:
+ stage: Either fit, test.
+ """
if stage == "fit":
train_keys = self._load_hdf5_keys_from_path(self.train_hdf5_keys_path, fraction=self.train_data_fraction)
val_keys = self._load_hdf5_keys_from_path(self.val_hdf5_keys_path, fraction=self.val_data_fraction)
From 5615b4155116c45f16e65768cff268fd5e773e33 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o=20Lucas=20de=20Sousa=20Almeida?=
<joao.l.sa.9.3@gmail.com>
Date: Tue, 18 Feb 2025 14:17:41 -0300
Subject: [PATCH 16/25] search
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Signed-off-by: João Lucas de Sousa Almeida <joao.l.sa.9.3@gmail.com>
---
mkdocs.yml | 1 +
1 file changed, 1 insertion(+)
diff --git a/mkdocs.yml b/mkdocs.yml
index 5bab8e8a..0ff5cc3a 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -8,6 +8,7 @@ theme:
- content.code.select
- navigation.tabs
plugins:
+ - search
- autorefs
- mkdocstrings:
handlers:
From c50423e8abb2a6a93ab05ad9237efb821403d3db Mon Sep 17 00:00:00 2001
From: Pedro <pedrohenriqueconrado@usp.br>
Date: Wed, 19 Feb 2025 10:04:22 -0300
Subject: [PATCH 17/25] changes docs data processing
---
docs/data.md | 28 ++++++++++++++++++++--------
1 file changed, 20 insertions(+), 8 deletions(-)
diff --git a/docs/data.md b/docs/data.md
index f8babecf..00b800c5 100644
--- a/docs/data.md
+++ b/docs/data.md
@@ -1,16 +1,28 @@
# Data Processing
-We rely on TorchGeo for the implementation of datasets and data modules.
-Check out [the TorchGeo tutorials on datasets](https://torchgeo.readthedocs.io/en/stable/tutorials/custom_raster_dataset.html) for more in depth information.
+In our workflow, we leverage TorchGeo to implement datasets and data modules, ensuring robust and flexible data handling. For a deeper dive into working with datasets using TorchGeo, please refer to the [TorchGeo tutorials on datasets](https://torchgeo.readthedocs.io/en/stable/tutorials/custom_raster_dataset.html).
-In general, it is reccomended you create a TorchGeo dataset specifically for your dataset. This gives you complete control and flexibility on how data is loaded, what transforms are done over it, and even how it is plotted if you log with tools like TensorBoard.
+In most cases, it’s best to create a custom TorchGeo dataset tailored to your specific data. Doing so gives you complete control over:
+- Data Loading: Customize how your data is read and organized.
+- Transforms: Decide which preprocessing or augmentation steps to apply.
+- Visualization: Define custom plotting methods (for example, when logging with TensorBoard).
-TorchGeo provides `GeoDataset` and `NonGeoDataset`.
+TorchGeo offers two primary classes to suit different data formats:
+- `NonGeoDataset`:
+ Use this if your dataset is already split into neatly tiled pieces ready for neural network consumption. Essentially, `NonGeoDataset` is a wrapper around a standard PyTorch dataset, making it straightforward to integrate into your pipeline.
+- `GeoDataset`:
+ Opt for this class if your data comes in the form of large GeoTiff files from which you need to sample during training. `GeoDataset` automatically aligns your input data with corresponding labels and supports a range of geo-aware sampling techniques.
-- If your data is already nicely tiled and ready for consumption by a neural network, you can inherit from `NonGeoDataset`. This is essentially a wrapper of a regular torch dataset.
-- If your data consists of large GeoTiffs you would like to sample from during training, you can leverage the powerful `GeoDataset` from torch. This will automatically align your input data and labels and enable a variety of geo-aware samplers.
-For additional examples on fine-tuning a Terratorch model using these components, please refer to the [Prithvi EO Examples](https://github.com/NASA-IMPACT/Prithvi-EO-2.0) repository.
+In addition to these specialized TorchGeo datasets, TerraTorch offers generic datasets and data modules designed to work with directory-based data structures, similar to those used in MMLab libraries. These generic tools simplify data loading when your data is organized in conventional file directories:
+- The Generic Pixel-wise Dataset is ideal for tasks where each pixel represents a sample (e.g., segmentation or dense prediction problems).
+- The Generic Scalar Label Dataset is best suited for classification tasks where each sample is associated with a single label.
+
+TerraTorch also provides corresponding generic data modules that bundle the dataset with training, validation, and testing splits, integrating seamlessly with PyTorch Lightning. This arrangement makes it easy to manage data loading, batching, and preprocessing with minimal configuration.
+
+While generic datasets offer a quick start for common data structures, many projects require more tailored solutions. Custom datasets and data modules give you complete control over the entire data handling process—from fine-tuned data loading and specific transformations to enhanced visualization. By developing your own dataset and data module classes, you ensure that every step—from data ingestion to final model input—is optimized for your particular use case. TerraTorch’s examples provide an excellent starting point to build these custom components and integrate them seamlessly into your training pipeline.
+
+For additional examples on fine-tuning a TerraTorch model using these components, please refer to the [Prithvi EO Examples](https://github.com/NASA-IMPACT/Prithvi-EO-2.0) repository.
## Using Datasets already implemented in TorchGeo
@@ -76,7 +88,7 @@ For the `NonGeoDataset` case, we also provide "generic" datasets and datamodules
## Custom datasets and data modules
Our custom datasets and data modules are crafted to handle specific data, offering enhanced control and flexibility throughout the workflow.
-In case you want to use Terratorch on your specific data, we invite you to develop your own dataset and data module classes by following the examples below.
+In case you want to use TerraTorch on your specific data, we invite you to develop your own dataset and data module classes by following the examples below.
### Datasets
#### :::terratorch.datasets.biomassters
From d5d447a17752fe383c57d620a116b78a32170e9b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o=20Lucas=20de=20Sousa=20Almeida?=
<joao.l.sa.9.3@gmail.com>
Date: Wed, 26 Feb 2025 18:01:59 -0300
Subject: [PATCH 18/25] permalink
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Signed-off-by: João Lucas de Sousa Almeida <joao.l.sa.9.3@gmail.com>
---
mkdocs.yml | 3 +++
1 file changed, 3 insertions(+)
diff --git a/mkdocs.yml b/mkdocs.yml
index 0ff5cc3a..38727f38 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -27,6 +27,7 @@ nav:
- Getting Started: quick_start.md
- Examples: examples.md
+ - Glossary: glossary.md
- License: license.md
- Components:
- Architecture Overview: architecture.md
@@ -38,6 +39,8 @@ nav:
#- FAQ: faq.md
markdown_extensions:
+ - toc:
+ permalink: "#"
- pymdownx.highlight:
anchor_linenums: true
line_spans: __span
From ce06f7611e15b88976c98a125e25238b2df67e4e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o=20Lucas=20de=20Sousa=20Almeida?=
<joao.l.sa.9.3@gmail.com>
Date: Wed, 26 Feb 2025 18:02:13 -0300
Subject: [PATCH 19/25] basic glossary
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Signed-off-by: João Lucas de Sousa Almeida <joao.l.sa.9.3@gmail.com>
---
docs/encoder_decoder_factory.md | 8 ++------
1 file changed, 2 insertions(+), 6 deletions(-)
diff --git a/docs/encoder_decoder_factory.md b/docs/encoder_decoder_factory.md
index d9a37d09..1cc57f91 100644
--- a/docs/encoder_decoder_factory.md
+++ b/docs/encoder_decoder_factory.md
@@ -1,8 +1,7 @@
# EncoderDecoderFactory
-## The Factory
-
-A special factory provided by terratorch is the [EncoderDecoderFactory][terratorch.models.encoder_decoder_factory.EncoderDecoderFactory].
+The [EncoderDecoderFactory][terratorch.models.encoder_decoder_factory.EncoderDecoderFactory] is the main class
+used to instantiate and compose models for general tasks.
This factory leverages the `BACKBONE_REGISTRY`, `DECODER_REGISTRY` and `NECK_REGISTRY` to compose models formed as encoder + decoder, with some optional glue in between provided by the necks.
As most current models work this way, this is a particularly important factory, allowing for great flexibility in combining encoders and decoders from different sources.
@@ -94,9 +93,6 @@ Necks are `nn.Modules`, with an additional method `process_channel_list` which i
### :::terratorch.models.necks.LearnedInterpolateToPyramidal
-
-
-
## Decoders
To be a valid decoder, an object must be an `nn.Module` with an attribute `out_channels`, an `int` representing the channel dimension of the output.
From b7759c4e6b8be46e1028d226c902f42d71b7faed Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o=20Lucas=20de=20Sousa=20Almeida?=
<joao.l.sa.9.3@gmail.com>
Date: Wed, 26 Feb 2025 18:04:34 -0300
Subject: [PATCH 20/25] more references
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Signed-off-by: João Lucas de Sousa Almeida <joao.l.sa.9.3@gmail.com>
---
docs/encoder_decoder_factory.md | 2 +-
docs/index.md | 12 ++++++------
2 files changed, 7 insertions(+), 7 deletions(-)
diff --git a/docs/encoder_decoder_factory.md b/docs/encoder_decoder_factory.md
index 1cc57f91..9fc9eb0b 100644
--- a/docs/encoder_decoder_factory.md
+++ b/docs/encoder_decoder_factory.md
@@ -1,5 +1,5 @@
# EncoderDecoderFactory
-
+Check the [Glossary][glossary.md] for more information about the terms used in this page.
The [EncoderDecoderFactory][terratorch.models.encoder_decoder_factory.EncoderDecoderFactory] is the main class
used to instantiate and compose models for general tasks.
diff --git a/docs/index.md b/docs/index.md
index 8466750b..c3f1b44b 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -13,17 +13,17 @@ This library provides:
- All the functionality in TorchGeo.
- Easy access to Prithvi, timm and smp backbones.
- Flexible trainers for Image Segmentation, Pixel Wise Regression and Classification (more in progress).
-- Launching of fine-tuning tasks through powerful configuration files.
+ - Launching of fine-tuning tasks through powerful configuration files.
-A good starting place is familiarization with [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/), which this project is built on.
-[TorchGeo](https://torchgeo.readthedocs.io/en/stable/) is also an important complementary reference.
+ A good starting place is familiarization with [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/), which this project is built on.
+ [TorchGeo](https://torchgeo.readthedocs.io/en/stable/) is also an important complementary reference.
-Check out the [architecture overview](architecture.md) for a general description about how TerraTorch is
-organized.
+ Check out the [architecture overview](architecture.md) for a general description about how TerraTorch is
+ organized.
## Quick start
-To get started, check out the [quick start guide](quick_start.md)
+ To get started, check out the [quick start guide](quick_start.md)
## License
TerraTorch is distributed under the terms of License Apache 2.0, see [here](license.md) for more details.
From 9893d669b31341e26f8b8553caa944accf098e37 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o=20Lucas=20de=20Sousa=20Almeida?=
<joao.l.sa.9.3@gmail.com>
Date: Wed, 26 Feb 2025 18:08:11 -0300
Subject: [PATCH 21/25] fix typos
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Signed-off-by: João Lucas de Sousa Almeida <joao.l.sa.9.3@gmail.com>
---
docs/encoder_decoder_factory.md | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/docs/encoder_decoder_factory.md b/docs/encoder_decoder_factory.md
index 9fc9eb0b..6527db8e 100644
--- a/docs/encoder_decoder_factory.md
+++ b/docs/encoder_decoder_factory.md
@@ -1,5 +1,6 @@
# EncoderDecoderFactory
-Check the [Glossary][glossary.md] for more information about the terms used in this page.
+*Check the [Glossary](glossary.md) for more information about the terms used in this page.*
+
The [EncoderDecoderFactory][terratorch.models.encoder_decoder_factory.EncoderDecoderFactory] is the main class
used to instantiate and compose models for general tasks.
From ddced60b01943ef89af29e03ee9d865684e4bc8e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o=20Lucas=20de=20Sousa=20Almeida?=
<joao.l.sa.9.3@gmail.com>
Date: Thu, 27 Feb 2025 10:03:46 -0300
Subject: [PATCH 22/25] fix typos
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Signed-off-by: João Lucas de Sousa Almeida <joao.l.sa.9.3@gmail.com>
---
docs/index.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/docs/index.md b/docs/index.md
index c3f1b44b..9f171f5f 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -23,7 +23,7 @@ This library provides:
## Quick start
- To get started, check out the [quick start guide](quick_start.md)
+To get started, check out the [quick start guide](quick_start.md)
## License
TerraTorch is distributed under the terms of License Apache 2.0, see [here](license.md) for more details.
From 083989e0f47e36cc1ef8c832b0dceae85b4d0bac Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o=20Lucas=20de=20Sousa=20Almeida?=
<joao.l.sa.9.3@gmail.com>
Date: Fri, 28 Feb 2025 13:07:10 -0300
Subject: [PATCH 23/25] example for inference
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Signed-off-by: João Lucas de Sousa Almeida <joao.l.sa.9.3@gmail.com>
---
docs/examples.md | 32 +++++++++++++++++++++++++++++++-
1 file changed, 31 insertions(+), 1 deletion(-)
diff --git a/docs/examples.md b/docs/examples.md
index 1ceea2cb..25265a37 100644
--- a/docs/examples.md
+++ b/docs/examples.md
@@ -1,4 +1,34 @@
-# Examples
+# Performing an inference task with TerraTorch
+
+## Step 1: Download the test case from HuggingFace
+We will use the burn scars identification test case, in which we are interested in estimating the area
+affected by wildfires using a finetuned model (Prithvi-EO backbone + CNN decoder). To download the complete
+example, do:
+```sh
+git clone https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-300M-BurnScars/
+```
+## Step 2: Run the default inference case
+The example you download already contains some sample images to be used as input, so you just need to go to
+the local repository and create a directory to save the outputs:
+```sh
+cd Prithvi-EO-2.0-300M-BurnScars
+mkdir outputs
+```
+and to execute a command line like:
+```sh
+terratorch predict -c burn_scars_config.yaml --predict_output_dir outputs/ --data.init_args.predict_data_root examples/ --ckpt_path Prithvi_EO_V2_300M_BurnScars.pt
+```
+You will see the outputs being saved in the `outputs` directory.
+
+### Input image (RGB components)
+
+
+
+### Predicted mask
+
+}
+
+# More examples
For some examples of training using the existing tasks, check out the following pages on our github repo:
From 515576bb4838027d9fc82cbbbdee74b997ee84c8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o=20Lucas=20de=20Sousa=20Almeida?=
<joao.l.sa.9.3@gmail.com>
Date: Tue, 11 Mar 2025 13:57:27 -0300
Subject: [PATCH 24/25] Fixing annotation
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Signed-off-by: João Lucas de Sousa Almeida <joao.l.sa.9.3@gmail.com>
---
terratorch/models/encoder_decoder_factory.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/terratorch/models/encoder_decoder_factory.py b/terratorch/models/encoder_decoder_factory.py
index efd018c3..849f41ec 100644
--- a/terratorch/models/encoder_decoder_factory.py
+++ b/terratorch/models/encoder_decoder_factory.py
@@ -88,7 +88,7 @@ def build_model(
`backbone_`, `decoder_` and `head_` respectively.
Args:
- task (str): Task to be performed. Currently supports "segmentation" and "regression".
+ task (str): Task to be performed. Currently supports "segmentation", "regression" and "classification".
backbone (str, nn.Module): Backbone to be used. If a string, will look for such models in the different
registries supported (internal terratorch registry, timm, ...). If a torch nn.Module, will use it
directly. The backbone should have and `out_channels` attribute and its `forward` should return a list[Tensor].
From 10b5a6e874005915657d10cad8d4fc0d54f65b98 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jo=C3=A3o=20Lucas=20de=20Sousa=20Almeida?=
<joao.l.sa.9.3@gmail.com>
Date: Tue, 11 Mar 2025 14:36:52 -0300
Subject: [PATCH 25/25] fixing typos
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Signed-off-by: João Lucas de Sousa Almeida <joao.l.sa.9.3@gmail.com>
---
docs/encoder_decoder_factory.md | 4 ++--
docs/figs/input.png | Bin 0 -> 766143 bytes
docs/figs/mask.png | Bin 0 -> 5920 bytes
docs/glossary.md | 24 ++++++++++++++++++++++++
4 files changed, 26 insertions(+), 2 deletions(-)
create mode 100644 docs/figs/input.png
create mode 100644 docs/figs/mask.png
create mode 100644 docs/glossary.md
diff --git a/docs/encoder_decoder_factory.md b/docs/encoder_decoder_factory.md
index 6527db8e..8091a5aa 100644
--- a/docs/encoder_decoder_factory.md
+++ b/docs/encoder_decoder_factory.md
@@ -73,7 +73,7 @@ Out[24]:
## Necks
-Necks are the glue between encoder and decoder. They can perform operations such as selecting elements from the output of the encoder ([SelectIndices][terratorch.models.necks.SelectIndices]), reshaping the outputs of ViTs so they are compatible with CNNs ([ReshapeTokensToImage][terratorch.models.necks.ReshapeTokensToImage]), amongst others.
+Necks are the connectors between encoder and decoder. They can perform operations such as selecting elements from the output of the encoder ([SelectIndices][terratorch.models.necks.SelectIndices]), reshaping the outputs of ViTs so they are compatible with CNNs ([ReshapeTokensToImage][terratorch.models.necks.ReshapeTokensToImage]), amongst others.
Necks are `nn.Modules`, with an additional method `process_channel_list` which informs the [EncoderDecoderFactory][terratorch.models.encoder_decoder_factory.EncoderDecoderFactory] about how it will alter the channel list provided by `encoder.out_channels`.
@@ -107,7 +107,7 @@ It's forward method should accept a list of embeddings.
Most decoders require a final head to be added for a specific task (e.g. semantic segmentation vs pixel wise regression).
Those registries producing decoders that dont require a head must expose the attribute `includes_head=True` so that a head is not added.
-Decoders passed as `nn.Modules` which dont require a head must expose the same attribute themselves.
+Decoders passed as `nn.Modules` which do not require a head must expose the same attribute themselves.
## :::terratorch.models.heads.classification_head.ClassificationHead
diff --git a/docs/figs/input.png b/docs/figs/input.png
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+# Glossary of terms used in this Documentation and in the Geospatial AI area
+
+## Encoder
+The neural network used to map between the inputs and the intermdiary stage (usually referred as embedding
+or sometimes as latent space) of the forward step. The encoder is also frequently called backbone and, for
+finetuning tasks, it is usually the part of the model which is not updated/trained.
+
+## Decoder
+The neural network employed to map between the intermediary stage (embedding/latent space) and the target
+output. For finetuning tasks, the decoder is the most essential part, since it is trained to map the embedding
+produced by a previoulsy trained encoder to a new task.
+
+## Head
+A network, usually very small when compared to the encoder and decoder, which is used as final step to adapt
+the decoder output to a specific task, for example, by applying a determined activation to it.
+
+## Neck
+Necks are operations placed between the encoder and the decoder stages aimed at adjusting possible
+discrepancies, as incompatible shapes, or applying some specific transform, as a normalization required for the task being executed.
+
+## Factory
+A Factory is a class which organizes the instantiation of a complete model, as a backbone-neck-decoder-head
+architecture. A class is intended to receive lists and dictionaries containing the required arguments used to
+build the model and returns a new instance already ready to be used.