updated testing

Author: fwaris

GCN/GCNModel.fs

@@ -6,13 +6,13 @@ open TorchSharp.Fun
 let inline (!>) (x:^a) : ^b = ((^a or ^b) : (static member op_Implicit : ^a -> ^b) x)
 
 let gcnLayer in_features out_features hasBias (adj:TorchTensor) =
-    let weight = Parameter(randName(),Float32Tensor.empty([|in_features; out_features|]))
-    let bias = if hasBias then Parameter(randName(),Float32Tensor.empty([|out_features|])) |> Some else None
+    let weight = Parameter(randName(),Float32Tensor.empty([|in_features; out_features|],requiresGrad=true))
+    let bias = if hasBias then Parameter(randName(),Float32Tensor.empty([|out_features|],requiresGrad=true)) |> Some else None
     let parms = [| yield weight; if hasBias then yield bias.Value|]
     Init.kaiming_uniform(weight.Tensor) |> ignore
 
     Model.create(parms,fun wts t -> 
-        let support = t.mm(wts.[0])
+        use support = t.mm(wts.[0])
         let output = adj.mm(support)
         if hasBias then
            output.add(wts.[1])
@@ -22,14 +22,14 @@ let gcnLayer in_features out_features hasBias (adj:TorchTensor) =
 let create nfeat nhid nclass dropout adj =
     let gc1 = gcnLayer nfeat nhid true adj
     let gc2 = gcnLayer nhid nclass true adj        
-    let relu = ReLU()
-    let logm = LogSoftmax(1L)
+    // let relu = ReLU()
+    // let logm = LogSoftmax(1L)
     let drp = if dropout then Dropout() |> M else Model.nop
 
     fwd3 gc1 gc2 drp (fun t g1 g2 drp -> 
         use t = gc1.forward(t)
-        use t = relu.forward(t)
+        use t = Functions.ReLU(t)
         use t = drp.forward(t)
         use t = gc2.forward(t)
-        let t = logm.forward(t)
+        let t = Functions.LogSoftmax(t, dimension=1L)
         t)

GCN/Program.fs

@@ -43,7 +43,7 @@ module Defs =
     let parse args =
         let parser = ArgumentParser.Create<Args>(programName = "gcn.exe")
         let args = parser.Parse(args)
-        let datafolder = args.GetResult (Args.Datafolder, defaultValue = @"C:\Users\fwaris\Downloads\pygcn-master\data\cora")
+        let datafolder = args.GetResult (Args.Datafolder, defaultValue = @"C:\s\Repos\gcn\data\cora")
         let no_cuda = args.GetResult (Args.No_CUDA, defaultValue=no_cuda)
         let fastmode = args.GetResult (Args.Fastmode, defaultValue=fastmode)
         let epochs = args.GetResult (Args.Epochs, defaultValue=epochs)
@@ -55,7 +55,11 @@ module Defs =
         datafolder,no_cuda,fastmode,epochs,dropout,lr,hidden,seed,weight_decay
 
 [<EntryPoint>]
-    let main args =
-        let runParms = Defs.parse args
+let main args =
+    let runParms = Defs.parse args
+    try
         Train.run runParms
-        0
+    with ex ->
+        printfn "%s" ex.Message
+    System.Console.ReadLine() |> ignore
+    0

GCN/TorchSharp.Fun.fs

@@ -164,13 +164,18 @@ let inline (=>>) m1 (n,m2) = compose (M m1) (Some n, M m2)
 module Tensor = 
     let private _getData<'t> (t:TorchTensor) =
         let s = t.Data<'t>()
-        s.ToArray()
+        let xs = Array.zeroCreate s.Length
+        for i in 0 .. s.Length-1 do
+            xs.[i] <- s.[i]
+        
+        //s.ToArray()
+        xs
 
     let getData<'t> (t:TorchTensor) =
         if t.device_type <> DeviceType.CPU then 
-            use t = t.clone()
-            use t = t.cpu()
-            _getData<'t> t
+            //use t1 = t.clone()
+            use t2 = t.cpu()
+            _getData<'t> t2
         else 
             _getData<'t> t
 

GCN/Train.fs

@@ -59,7 +59,10 @@ let run (datafolder,no_cuda,fastmode,epochs,dropout,lr,hidden,seed,weight_decay)
 
     let t_total = DateTime.Now
     for i in 1 .. epochs-1 do
+        printfn $"epoch {i}"
         train i
     printfn "Optimization done"
     printfn $"Time elapsed: {(DateTime.Now - t_total).TotalMinutes} minutes"
 
+    test()
+