jpcbertoldo · February 8, 2024 17:56 · Feb 8, 2024
diff --git a/gistfile1.txt b/gistfile1.txt
@@ -0,0 +1,60 @@
+# %%
+# Scratch of spro on a batch of images
+
+import torch
+
+# ---- Dummy data ----
+
+# a batch of predictions for two images
+# shape: (2, 1, 256, 256)
+predictions = torch.rand(2, 1, 256, 256)
+
+# masks as list[list[Tensor]]
+# external list is for the batch
+# internal lists are for the multiple masks of the image
+masks = [
+    # each internal list corresponds to one image (index in `preds`)
+    [
+        # each tensor corresponds to one file (ie. one spro curve / mask)
+        torch.rand(256, 256) > .99,
+        torch.rand(256, 256) > .90,
+    ],
+    [
+        torch.rand(256, 256) > .80,
+    ],
+]
+
+# ---- Concat masks & repeat predictions ----
+
+# Concatenate all masks into a single tensor
+# shape: (3, 256, 256)
+masks_concatenated = torch.cat([torch.cat(image_masks) for image_masks in masks])
+
+# make a predictions tensor where the prediction of an image with N masks is repeated N times
+# (ie. the same prediction is repeated for each mask of the image)
+# shape: (3, 1, 256, 256)
+predictions_repeated = torch.cat([
+    pred.repeat(len(image_masks), 1, 1, 1)
+    for pred, image_masks in zip(predictions, masks)
+])
+# so the indexes in `predictions_repeated` match the indexes in `masks_concatenated`
+# but does NOT the indexes in `predictions`
+# mapping would be
+# `predictions_repeated[0]` -> `predictions[0]`
+# `predictions_repeated[1]` -> `predictions[0]`
+# `predictions_repeated[2]` -> `predictions[1]`
+
+# ---- SPRO ----
+# ...
+
+# a fake result
+# 10_000 is the number of points (thresholds) in the spro curve
+spro_curves = torch.rand(3, 10_000)
+
+#...
+# this should happen with all curves from all batches (NOT per batch), Eq. 1 in the paper
+spro = spro_curves.mean(dim=0)
+
+
+
+# something weird happens here: batch size of the input is 2, but there are 3 curves