mudancas temporarias

terratorch/datamodules/__init__.py

@@ -39,6 +39,9 @@
 from terratorch.datamodules.biomassters import BioMasstersNonGeoDataModule
 from terratorch.datamodules.forestnet import ForestNetNonGeoDataModule
 
+# miscellaneous datamodules
+from terratorch.datamodules.openearthmap import OpenEarthMapNonGeoDataModule
+
 # Generic classification datamodule
 from terratorch.datamodules.sen4map import Sen4MapLucasDataModule
 

terratorch/datasets/__init__.py

@@ -50,7 +50,7 @@
     "GenericNonGeoSegmentationDataset",
     "GenericNonGeoPixelwiseRegressionDataset",
     "GenericNonGeoClassificationDataset",
-    "GenericNonGeoRegressionDataset",
+    #"GenericNonGeoRegressionDataset",
     "BurnIntensityNonGeo",
     "CarbonFluxNonGeo",
     "Landslide4SenseNonGeo",

terratorch/datasets/fire_scars.py

@@ -198,7 +198,7 @@ def plot(self, sample: dict[str, Tensor], suptitle: str | None = None) -> Figure
         ax[3].imshow(image)
         ax[3].imshow(mask, cmap="jet", alpha=0.3, norm=norm)
 
-        if prediction:
+        if "prediction" in sample:
             ax[4].title.set_text("Predicted Mask")
             ax[4].imshow(prediction, cmap="jet", norm=norm)
 

terratorch/datasets/landslide4sense.py

@@ -137,7 +137,7 @@ def plot(self, sample: dict[str, torch.Tensor], suptitle: str | None = None) ->
         ax[2].axis("off")
 
         if "prediction" in sample:
-            prediction = sample["prediction"].numpy()
+            prediction = sample["prediction"]
             ax[3].imshow(prediction, cmap=cmap, norm=norm)
             ax[3].set_title("Predicted Mask")
             ax[3].axis("off")

terratorch/datasets/multi_temporal_crop_classification.py

@@ -235,37 +235,35 @@ def plot(self, sample: dict[str, Tensor], suptitle: str | None = None) -> Figure
             raise ValueError(msg)
 
         images = sample["image"]
-        if not self.expand_temporal_dimension:
-            images = rearrange(images, "(channels time) h w -> channels time h w", channels=len(self.bands))
+        images = images[rgb_indices, ...]  # Shape: (T, 3, H, W)
 
-        # RGB -> channels-last
-        images = images[rgb_indices, ...].permute(1, 2, 3, 0).numpy()
-        mask = sample["mask"].numpy()
-
-        images = [clip_image(img) for img in images]
+        processed_images = []
+        for t in range(self.time_steps):
+            img = images[t]
+            img = img.permute(1, 2, 0)
+            img = img.numpy()
+            img = clip_image(img)
+            processed_images.append(img)
 
+        mask = sample["mask"].numpy()
         if "prediction" in sample:
-            prediction = sample["prediction"]
             num_images += 1
-        else:
-            prediction = None
-
         fig, ax = plt.subplots(1, num_images, figsize=(12, 5), layout="compressed")
-
         ax[0].axis("off")
 
         norm = mpl.colors.Normalize(vmin=0, vmax=self.num_classes - 1)
+        for i, img in enumerate(processed_images):
+            ax[i + 1].axis("off")
+            ax[i + 1].title.set_text(f"T{i}")
+            ax[i + 1].imshow(img)
 
-        for i, img in enumerate(images):
-            ax[i+1].axis("off")
-            ax[i+1].title.set_text(f"T{i}")
-            ax[i+1].imshow(img)
-
-        ax[self.time_steps+1].axis("off")
-        ax[self.time_steps+1].title.set_text("Ground Truth Mask")
-        ax[self.time_steps+1].imshow(mask, cmap="jet", norm=norm)
+        ax[self.time_steps + 1].axis("off")
+        ax[self.time_steps + 1].title.set_text("Ground Truth Mask")
+        ax[self.time_steps + 1].imshow(mask, cmap="jet", norm=norm)
 
-        if prediction:
+        if "prediction" in sample:
+            prediction = sample["prediction"]
+            ax[self.time_steps + 1].axis("off")
             ax[self.time_steps+2].title.set_text("Predicted Mask")
             ax[self.time_steps+2].imshow(prediction, cmap="jet", norm=norm)
 
@@ -274,6 +272,7 @@ def plot(self, sample: dict[str, Tensor], suptitle: str | None = None) -> Figure
         handles = [Rectangle((0, 0), 1, 1, color=tuple(v for v in c)) for k, c, n in legend_data]
         labels = [n for k, c, n in legend_data]
         ax[0].legend(handles, labels, loc="center")
+
         if suptitle is not None:
             plt.suptitle(suptitle)
 

terratorch/datasets/sen1floods11.py

@@ -228,7 +228,7 @@ def plot(self, sample: dict[str, Tensor], suptitle: str | None = None) -> Figure
         ax[3].imshow(image)
         ax[3].imshow(mask, cmap="jet", alpha=0.3, norm=norm)
 
-        if prediction:
+        if "prediction" in sample:
             ax[4].title.set_text("Predicted Mask")
             ax[4].imshow(prediction, cmap="jet", norm=norm)