| # SR-GAN examples |
|
|
| These small examples use the supported release layouts in this repository. Install |
| the matching package revision and download the examples alongside the model files: |
|
|
| ```bash |
| python -m pip install 'opensr-srgan @ git+https://github.com/ESAOpenSR/SRGAN.git@dev' |
| hf download simon-donike/SR-GAN --include 'examples/*' --local-dir sr-gan-release |
| cd sr-gan-release/examples |
| ``` |
|
|
| Pass `--revision <commit-or-tag>` to every command if you want |
| `load_inference_model` to resolve a particular Hub revision. The packaged |
| presets already pin their supported release by default, so the flag is optional |
| for the standard release. The resume script resolves its requested revision to |
| one immutable commit before downloading its three linked artifacts and records |
| that commit in its output directory. |
|
|
| ## Tensor inference |
|
|
| `infer_tensor.py` accepts a saved PyTorch tensor with shape `BCHW` (or `CHW`) and |
| saves a `BCHW` tensor. It also accepts a tuple/list such as `(lr, hr)` and a |
| dictionary containing a tensor under `lr`, `lr_imgs`, `input`, `image`, or |
| `x`; in a tuple/list it uses the first tensor, which is conventionally the LR |
| sample. |
|
|
| ```bash |
| python infer_tensor.py --preset SEN2-SR --device cpu --input lr_b04_b03_b02_b08.pt --output sr.pt |
| python infer_tensor.py --preset RGB-NIR --device cuda --input lr.pt --output sr.pt |
| python infer_tensor.py --preset SWIR --device cuda --input swir_lr.pt --output swir_sr.pt |
| ``` |
|
|
| For `SEN2-SR` and `RGB-NIR`, channels must be Sentinel-2 `B04, B03, B02, B08`. |
| The `SWIR` preset expects `B05, B06, B07, B8A, B11, B12` in that order. The |
| normal mode calls `predict_step`: raw Sentinel-2 reflectance/DN values in the |
| usual `0..10000` range produce raw-DN output. Inputs already in `0..1` are |
| recognized as normalized reflectance and retain that normalized convention in |
| the output. The script does not clip the returned values. The published |
| sample batch is a mapping with `lr` and `hr` tensors; passing it directly uses |
| its normalized `lr` entry. |
|
|
| `--raw` is deliberately a network-only mode. It calls `model.forward`; supply |
| the model-ready normalized reflectance values (normally `0..1`) yourself. Its |
| saved output remains in that network convention and skips `predict_step` |
| normalization, denormalization, and post-processing. |
|
|
| `--smoke` creates a small zero tensor to check installation and tensor shapes. |
| It is not imagery and must not be used to assess image quality. |
|
|
| ```bash |
| python infer_tensor.py --preset SEN2-SR --device cpu --smoke --output smoke_sr.pt |
| python infer_tensor.py --preset SEN2-SR --input normalized.pt --raw --output normalized_sr.pt |
| ``` |
|
|
| ## A small GeoTIFF |
|
|
| `infer_geotiff.py` reads one small, already co-registered raster into memory. |
| It is useful for a cutout, not for a whole Sentinel-2 scene: it does not tile, |
| overlap, or manage memory for large rasters. |
|
|
| Choose source band indices explicitly with `--bands`; indices are one-based as |
| in Rasterio/GDAL. The default is `1,2,...` only when the input raster was |
| already stacked in the model's required order. |
|
|
| ```bash |
| # A four-band GeoTIFF stacked B04,B03,B02,B08 in bands 1,2,3,4. |
| python infer_geotiff.py --preset SEN2-SR --input cutout.tif --bands 1,2,3,4 --output cutout_sr.tif |
| |
| # A six-band GeoTIFF stacked B05,B06,B07,B8A,B11,B12 in bands 3,5,2,1,7,4. |
| python infer_geotiff.py --preset SWIR --input cutout.tif --bands 3,5,2,1,7,4 --output cutout_swir_sr.tif |
| ``` |
|
|
| The output uses float32 values from `predict_step`, retains the source CRS, and |
| derives its affine transform from the actual output shape. With raw-DN input, |
| the output is raw-DN; a `0..1` input retains normalized-reflectance output. |
| This makes output pixel size the input pixel size divided by the model's actual |
| scale factor (4 for `SEN2-SR`/`RGB-NIR`, 8 for the legacy `SWIR` preset). |
|
|
| ## Continue the Sen2 SR run |
|
|
| The full Sen2 SR checkpoint contains optimizer, scheduler, and EMA state. The |
| resume example downloads the canonical training config, split, and checkpoint |
| from the Hub; it never fetches data itself. Point `--dataset` to the local |
| SEN2NAIP/TACO data file expected by the recipe. |
|
|
| ```bash |
| python resume_training.py \ |
| --dataset /data/SEN2NAIPv2/sen2naipv2-crosssensor.taco \ |
| --output-dir ./sen2-sr-resume \ |
| --max-steps 328584 |
| ``` |
|
|
| The command above only writes a fully resolved `config.yaml` and copies the |
| downloaded release inputs into the new output directory. Inspect it before |
| training, then launch that prepared configuration directly: |
|
|
| ```bash |
| python -m opensr_srgan.train --config ./sen2-sr-resume/config.yaml |
| ``` |
|
|
| `--max-steps` is an absolute Lightning optimizer-step limit, so it must exceed |
| the checkpoint's recorded step (308584) for a continuation to perform updates. |
| Install the packaged data extra before a resume, then use a fresh output |
| directory for each attempt: |
|
|
| ```bash |
| python -m pip install 'opensr-srgan[sen2naip] @ git+https://github.com/ESAOpenSR/SRGAN.git@dev' |
| ``` |
|
|
| Use `--run` only when preparing and launching in one command against a fresh |
| output directory. |
|
|
