Remove editor module

src/training/progressive_pooling_loss.py

@@ -0,0 +1,38 @@
+from typing import List
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class ProgressivePoolingLoss(nn.Module):
+    def __init__(self, target_sizes: List[int], damping: float):
+        super(ProgressivePoolingLoss, self).__init__()
+        self._target_sizes = target_sizes
+        self._damping = damping
+
+    def forward(self, tensor_a, tensor_b):
+        assert (
+            tensor_a.size() == tensor_b.size()
+        ), f"Input tensors must have the same size, got {tensor_a.size()} and {tensor_b.size()}"
+
+        assert (
+            len(tensor_a.size()) == 5
+        ), f"Input tensors must have 5 dimensions, got {tensor_a.size()}"
+
+        _minibatch_size, _channels, depth, height, width = tensor_a.size()
+        assert depth == height == width, "Input tensors must be cubes."
+
+        loss = 0.0
+        weight = 1
+
+        for target_size in self._target_sizes:
+            pool_size = depth // target_size
+            pooled_a = F.avg_pool3d(tensor_a, pool_size) * (pool_size**3)
+            pooled_b = F.avg_pool3d(tensor_b, pool_size) * (pool_size**3)
+
+            diff = torch.abs(pooled_a - pooled_b)
+
+            loss += diff.mean() * weight
+            weight *= self._damping
+
+        return loss