functional.rb in torch-rb-0.17.0

- old
+ new

@@ -132,11 +132,11 @@
         def pad(input, pad, mode: "constant", value: 0)
           raise ArgumentError, "Padding length must be divisible by 2" unless pad.size % 2 == 0
           raise ArgumentError, "Padding length too large" unless pad.size / 2 <= input.dim
 
           if mode == "constant"
-            return Torch.constant_pad_nd(input, pad, value)
+            Torch.constant_pad_nd(input, pad, value)
           else
             raise ArgumentError, "Padding mode doesn't take in value argument" unless value == 0
 
             if input.dim == 3
               raise ArgumentError, "3D tensors expect 2 values for padding" unless pad.size == 2
@@ -477,9 +477,19 @@
           NN.smooth_l1_loss(input, target, to_reduction(reduction))
         end
 
         def triplet_margin_loss(anchor, positive, negative, margin: 1.0, p: 2, eps: 1e-06, swap: false, reduction: "mean")
           Torch.triplet_margin_loss(anchor, positive, negative, margin, p, eps, swap, to_reduction(reduction))
+        end
+
+        def normalize(input, p: 2.0, dim: 1, eps: 1e-12, out: nil)
+          if out.nil?
+            denom = input.norm(p, dim, keepdim: true).clamp_min(eps).expand_as(input)
+            input / denom
+          else
+            denom = input.norm(p, dim, keepdim: true).clamp_min!(eps).expand_as(input)
+            Torch.div(input, denom, out: out)
+          end
         end
 
         # vision
 
         def interpolate(input, size: nil, scale_factor: nil, mode: "nearest", align_corners: nil, recompute_scale_factor: nil)