Upload 5 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+sr4rs/weights/cesbio_model/sr4rs_sentinel2_bands4328_france2020_savedmodel/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

sr4rs/run.py

@@ -0,0 +1,32 @@
+import torch
+import opensr_test
+import matplotlib.pyplot as plt
+from utils import load_cesbio_sr, run_sr4rs
+
+
+# Load the model
+model = load_cesbio_sr()
+
+
+# Load the dataset
+dataset = opensr_test.load("naip")
+lr_dataset, hr_dataset = dataset["L2A"], dataset["HRharm"]
+
+# Predict a image
+results = run_sr4rs(
+    model=model,
+    lr=lr_dataset[2],
+    hr=hr_dataset[2],
+)
+
+# Display the results
+fig, ax = plt.subplots(1, 3, figsize=(10, 5))
+ax[0].imshow(results["lr"].transpose(1, 2, 0)/3000)
+ax[0].set_title("LR")
+ax[0].axis("off")
+ax[1].imshow(results["sr"].transpose(1, 2, 0)/3000)
+ax[1].set_title("SR")
+ax[1].axis("off")
+ax[2].imshow(results["hr"].transpose(1, 2, 0) / 3000)
+ax[2].set_title("HR")
+plt.show()

sr4rs/utils.py

@@ -0,0 +1,60 @@
+import tensorflow as tf
+import torch
+
+def load_cesbio_sr() -> tf.function:
+    """Prepare the CESBIO model
+
+    Returns:
+        tf.function: A tf.function to get the SR image
+    """
+
+    # read the model
+    model = tf.saved_model.load("weights/cesbio_model/sr4rs_sentinel2_bands4328_france2020_savedmodel")
+
+    # get the signature
+    signature = list(model.signatures.keys())[0]
+
+    # get the function
+    func = model.signatures[signature]
+
+    return func
+
+def run_sr4rs(
+    model: tf.function,
+    lr: tf.Tensor,
+    hr: tf.Tensor,
+) -> dict:
+    """Run the SR4RS model
+
+    Args:
+        model (tf.function): The model to use
+        lr (tf.Tensor): The low resolution image
+        hr (tf.Tensor): The high resolution image
+        cropsize (int, optional): The cropsize. Defaults to 32.
+        overlap (int, optional): The overlap. Defaults to 0.
+
+    Returns:
+        dict: The results
+    """
+    # Run inference
+    Xnp = torch.from_numpy(lr[[3, 2, 1, 7]][None]).permute(0, 2, 3, 1)
+    Xtf = tf.convert_to_tensor(Xnp, dtype=tf.float32)
+    pred = model(Xtf)
+
+    # Save the results
+    pred_np = pred['output_32:0'].numpy()
+    pred_torch = torch.from_numpy(pred_np).permute(0, 3, 1, 2)
+    pred_torch_padded = torch.nn.functional.pad(
+        pred_torch,
+        (32, 32, 32, 32),
+        mode='constant',
+        value=0,
+    ).squeeze().numpy().astype('uint16')
+    
+    results = {
+        "lr": lr[[3, 2, 1]],
+        "sr": pred_torch_padded[0:3],
+        "hr": hr[0:3],
+    }
+
+    return results

sr4rs/weights/cesbio_model/sr4rs_sentinel2_bands4328_france2020_savedmodel/saved_model.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:31562e5cce3bc52576d4cbdb066bb6552b7ffc846f03022d4f9b7a5e6dd6b727
+size 486539011

sr4rs/weights/cesbio_model/sr4rs_sentinel2_bands4328_france2020_savedmodel/variables/variables.data-00000-of-00001

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4045311226af9e908b6c741301399a556b2705f20963e9d80213ce1a1fac81a3
+size 297612052