grad wrt sum net with summarized

src/networks/summarized_net.jl

@@ -49,8 +49,8 @@ function inverse(X::AbstractArray{T, N}, Y::AbstractArray{T, N}, S::SummarizedNe
 end
 
 # Backward pass and compute gradients
-function backward(ΔX::AbstractArray{T, N}, X::AbstractArray{T, N}, Y::AbstractArray{T, N}, S::SummarizedNet; Y_save=nothing) where {T, N}
-	ΔX, X, ΔY = S.cond_net.backward(ΔX,X,similar(Y),Y)
+function backward(ΔX::AbstractArray{T, N}, X::AbstractArray{T, N}, ΔY::AbstractArray{T, N}, Y::AbstractArray{T, N}, S::SummarizedNet; Y_save=nothing) where {T, N}
+	ΔX, X, ΔY = S.cond_net.backward(ΔX,X,ΔY,Y)
     ΔY = S.sum_net.backward(ΔY, Y_save)
     return ΔX, X, ΔY
 end

test/test_networks/test_conditional_glow_network.jl

@@ -95,8 +95,6 @@ end
 
 
 
-
-
 function loss(G, X, Cond)
     Y, ZC, logdet = G.forward(X, Cond)
     f = -log_likelihood(Y) - logdet
@@ -211,10 +209,11 @@ end
 # Gradient test
 function loss_sum(G, X, Cond)
     Y, ZC, logdet = G.forward(X, Cond)
-    f = -log_likelihood(Y) - logdet
+    f = -log_likelihood(Y) -log_likelihood(ZC) - logdet
     ΔY = -∇log_likelihood(Y)
-    ΔX, X_ = G.backward(ΔY, Y,  ZC; Y_save=Cond)
-    return f, ΔX, G.cond_net.CL[1,1].RB.W1.grad, G.cond_net.CL[1,1].C.v1.grad
+    ΔZC = -∇log_likelihood(ZC)
+    ΔX,  X_, ΔC = G.backward(ΔY, Y, ΔZC,  ZC; Y_save=Cond)
+    return f, ΔX, ΔC, G.cond_net.CL[1,1].RB.W1.grad, G.cond_net.CL[1,1].C.v1.grad
 end
 
 # Gradient test w.r.t. input
@@ -223,19 +222,19 @@ Cond = rand(Float32, N..., n_cond, batchsize);
 X0 = rand(Float32, N..., n_in, batchsize);
 Cond0 = rand(Float32, N..., n_cond, batchsize);
 
-dX = X - X0
+dCond = Cond - Cond0
 
-f0, ΔX = loss_sum(G, X0, Cond0)[1:2]
+f0, ΔX, ΔC  = loss_sum(G, X0, Cond0)[1:3]
 h = 0.1f0
 maxiter = 4
 err1 = zeros(Float32, maxiter)
 err2 = zeros(Float32, maxiter)
 
 print("\nGradient test glow: input\n")
 for j=1:maxiter
-    f = loss_sum(G, X0 + h*dX, Cond0)[1]
+    f = loss_sum(G, X0, Cond0 + h*dCond)[1]
     err1[j] = abs(f - f0)
-    err2[j] = abs(f - f0 - h*dot(dX, ΔX))
+    err2[j] = abs(f - f0 - h*dot(dCond, ΔC))
     print(err1[j], "; ", err2[j], "\n")
     global h = h/2f0
 end
@@ -254,7 +253,7 @@ Gini = deepcopy(G0)
 dW = G.cond_net.CL[1,1].RB.W1.data - G0.cond_net.CL[1,1].RB.W1.data
 dv = G.cond_net.CL[1,1].C.v1.data - G0.cond_net.CL[1,1].C.v1.data
 
-f0, ΔX, ΔW, Δv = loss_sum(G0, X, Cond)
+f0, ΔX, ΔC, ΔW, Δv = loss_sum(G0, X, Cond)
 h = 0.1f0
 maxiter = 4
 err3 = zeros(Float32, maxiter)
@@ -388,7 +387,7 @@ X_ = G.inverse(Y,ZCond); # saving the cond is important in split scales because
 @test isapprox(norm(X - X_)/norm(X), 0f0; atol=1f-5)
 
 # Test gradients are set and cleared
-G.backward(Y, Y,ZCond; Y_save=Cond)
+G.backward(Y, Y,ZCond,ZCond; Y_save=Cond)
 
 P = get_params(G)
 gsum = 0
@@ -444,7 +443,7 @@ Gini = deepcopy(G0)
 dW = G.cond_net.CL[1,1].RB.W1.data - G0.cond_net.CL[1,1].RB.W1.data
 dv = G.cond_net.CL[1,1].C.v1.data - G0.cond_net.CL[1,1].C.v1.data
 
-f0, ΔX, ΔW, Δv = loss_sum(G0, X, Cond);
+f0, ΔX,ΔC, ΔW, Δv = loss_sum(G0, X, Cond);
 h = 0.1f0
 maxiter = 4
 err3 = zeros(Float32, maxiter)