Addition of Siamese Contrastive Loss function ( Updated ) (#1892)

Author: arcAman07

docs/src/models/losses.md

@@ -41,4 +41,5 @@ Flux.Losses.dice_coeff_loss
 Flux.Losses.tversky_loss
 Flux.Losses.binary_focal_loss
 Flux.Losses.focal_loss
+Flux.losses.siamese_contrastive_loss
 ```

src/losses/Losses.jl

@@ -20,7 +20,7 @@ export mse, mae, msle,
     poisson_loss,
     hinge_loss, squared_hinge_loss,
     ctc_loss,
-    binary_focal_loss, focal_loss
+    binary_focal_loss, focal_loss, siamese_contrastive_loss
 
 include("utils.jl")
 include("functions.jl")

src/losses/functions.jl

@@ -527,6 +527,23 @@ function focal_loss(ŷ, y; dims=1, agg=mean, γ=2, ϵ=epseltype(ŷ))
     agg(sum(@. -y * (1 - ŷ)^γ * log(ŷ); dims=dims))
 end
 
+"""
+    siamese_contrastive_loss(ŷ, y; margin = 1, agg = mean)
+                                    
+Return the [contrastive loss](http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf)
+which can be useful for training Siamese Networks. It is given by
+                                    
+    agg(@. (1 - y) * ŷ^2 + y * max(0, margin - ŷ)^2)                           
+                                 
+Specify `margin` to set the baseline for distance at which pairs are dissimilar.
+                                    
+"""
+function siamese_contrastive_loss(ŷ, y; agg = mean, margin::Real = 1)
+    _check_sizes(ŷ, y)
+    margin < 0 && throw(DomainError(margin, "Margin must be non-negative"))
+    return agg(@. (1 - y) * ŷ^2 + y * max(0, margin - ŷ)^2)
+end
+
 ```@meta
 DocTestFilters = nothing
 ```

test/losses.jl

@@ -14,7 +14,7 @@ const ALL_LOSSES = [Flux.Losses.mse, Flux.Losses.mae, Flux.Losses.msle,
                     Flux.Losses.dice_coeff_loss,
                     Flux.Losses.poisson_loss,
                     Flux.Losses.hinge_loss, Flux.Losses.squared_hinge_loss,
-                    Flux.Losses.binary_focal_loss, Flux.Losses.focal_loss]
+                    Flux.Losses.binary_focal_loss, Flux.Losses.focal_loss, Flux.Losses.siamese_contrastive_loss]
 
 
 @testset "xlogx & xlogy" begin
@@ -210,3 +210,41 @@ end
     @test Flux.focal_loss(ŷ1, y1) ≈ 0.45990566879720157
     @test Flux.focal_loss(ŷ, y; γ=0.0) ≈ Flux.crossentropy(ŷ, y)
 end
+  
+@testset "siamese_contrastive_loss" begin
+  y = [1 0
+       0 0
+       0 1]
+  ŷ = [0.4 0.2
+       0.5 0.5
+       0.1 0.3]
+  y1 = [1 0 0 0 1
+        0 1 0 1 0
+        0 0 1 0 0]
+  ŷ1 = softmax(reshape(-7:7, 3, 5) .* 1.0f0)
+  y2 = [1
+        0
+        0
+        1
+        1]
+  ŷ2 = [0.6
+        0.4
+        0.1
+        0.2
+        0.7]
+  @test Flux.siamese_contrastive_loss(ŷ, y) ≈ 0.2333333333333333
+  @test Flux.siamese_contrastive_loss(ŷ, y, margin = 0.5f0) ≈ 0.10000000000000002
+  @test Flux.siamese_contrastive_loss(ŷ, y, margin = 1.5f0) ≈ 0.5333333333333333
+  @test Flux.siamese_contrastive_loss(ŷ1, y1) ≈ 0.32554644f0
+  @test Flux.siamese_contrastive_loss(ŷ1, y1, margin = 0.5f0) ≈ 0.16271012f0
+  @test Flux.siamese_contrastive_loss(ŷ1, y1, margin = 1.5f0) ≈ 0.6532292f0
+  @test Flux.siamese_contrastive_loss(ŷ, y, margin = 1) ≈ Flux.siamese_contrastive_loss(ŷ, y)
+  @test Flux.siamese_contrastive_loss(y, y) ≈ 0.0
+  @test Flux.siamese_contrastive_loss(y1, y1) ≈ 0.0
+  @test Flux.siamese_contrastive_loss(ŷ, y, margin = 0) ≈ 0.09166666666666667
+  @test Flux.siamese_contrastive_loss(ŷ1, y1, margin = 0) ≈ 0.13161165f0
+  @test Flux.siamese_contrastive_loss(ŷ2, y2) ≈ 0.21200000000000005
+  @test Flux.siamese_contrastive_loss(ŷ2, ŷ2) ≈ 0.18800000000000003
+  @test_throws DomainError(-0.5, "Margin must be non-negative") Flux.siamese_contrastive_loss(ŷ1, y1, margin = -0.5)
+  @test_throws DomainError(-1, "Margin must be non-negative") Flux.siamese_contrastive_loss(ŷ, y, margin = -1)
+end