Inference

This walkthrough covers the fastest path from zero to an end-to-end super-resolution run on a full Sentinel-2 tile. It uses the published opensr-srgan package to grab the pretrained RGB-NIR preset and hands the model to opensr-utils for windowed tiling, stitching, and export.

1. Install the runtime dependencies

pip install opensr-srgan

• opensr-srgan exposes helpers that reconstruct Lightning checkpoints from YAML configs or download ready-to-run presets.
• The optional huggingface extra adds huggingface-hub, which load_inference_model uses internally when fetching preset weights from the Hub.
• opensr-utils provides the tiling/mosaicking pipeline that can super-resolve whole Sentinel-2 SAFE folders, GeoTIFFs, or other large rasters.

2.1 Instantiate a Preset

from opensr_srgan import load_inference_model

model = load_inference_model("RGB-NIR", map_location="cuda")

load_inference_model retrieves the configuration and checkpoint that correspond to the selected preset (here the four-band RGB-NIR model), restores the Lightning module, and switches it to evaluation mode so that it is ready for inference.【F:opensr_srgan/_factory.py†L107-L150】 If you run on CPU, change map_location="cuda" to map_location="cpu".

2.2 Instanciate your own Model

from opensr_srgan import load_from_config
model = load_from_config(config_path="YOUR_CONFIG_PATH",
                         checkpoint_uri="YOUR_CKPT_PATH")

Using the path to your trained model as well as the config file that was used to train your model, you can load your model for inference.

3.1 Run SR on your Images

After the model has been created, please use the pytorch-lightning native predict_step function. Sticking to the pytorch-lightning workflow includes the selected normalization procedures that the model was trained on, and also seamlessly enables multi-GPU processing and other tweaks. Be aware that this only SRs raw tensors, so the patching and stitching needs to be done manually.

sr = model.predict_step(lr)

3.2 Super-resolve a full tile with OpenSR-Utils

You can build on top of out utility package in order to do all of the stitching, georeferencing and patching automatically (Note: this currently only works for RGB-NIR Sentinel-2 images)

import opensr_utils

sen2_path = "opensr_srgan/data/S2A_MSIL2A_20230901T104031_N0509_R137_T31TFJ_20230901T130204.SAFE"
sr_runner = opensr_utils.large_file_processing(
    root=sen2_path,
    model=model,
    window_size=(128, 128),
    factor=4,
    overlap=12,
    eliminate_border_px=2,
    device="cuda",
    gpus=[0],
    save_preview=True,
    debug=False,
)
sr_runner.start_super_resolution()

large_file_processing orchestrates the windowed inference workflow: it slides a (128 × 128) LR window over the scene, feeds each crop through the SRGAN, blends overlapping predictions (12 px overlap with 2 px border trimming), and optionally stores both previews and georeferenced outputs. The helper understands either directory-style SAFE products or single GeoTIFFs, and it accepts GPU IDs for accelerated execution. Adjust the paths, window size, and overlap to match your dataset or hardware constraints.