Fix #715

RELEASENOTES.md

@@ -6,11 +6,12 @@ Releases, starting with 9/2/2021, are listed with the most recent release at the
 
 __Fixed Bugs:__
 
-
+#715 How to implement the following code <br/>
 
 __API Changes__:
 
 Add functional normalizations<br/>
+Added torch.utils.tensorboard.SummaryWriter. Support for scalars only.<br/>
 
 
 ## NuGet Version 0.97.3

docfx/articles/modules.md

@@ -230,3 +230,63 @@ Sometimes, a module needs to allocate tensor that are not trainable, i.e. their
 
 Each buffer should be declared as a field of type 'Tensor' (not 'Parameter'). This will ensure that the buffer is registered properly when `RegisterComponents()` is called.
 
+
+## Modules, 'children()' and 'named_children()'
+
+It is sometimes necessary to create a new model from an existing one and discard some of the final layers. The submodules will appear in the 'named_children' list in the same order that they are declared within the module itself, and when constructing a model based on the children, the layers may be reordered unless the submodules are declared in the same order that they are meant to be invoked.
+
+So, for example:
+
+```C#
+        private class TestModule1 : Module
+        {
+            public TestModule1()
+                : base("TestModule1")
+            {
+                lin1 = Linear(100, 10);
+                lin2 = Linear(10, 5);
+                RegisterComponents();
+            }
+
+            public override Tensor forward(Tensor input)
+            {
+                using (var x = lin1.forward(input))
+                return lin2.forward(x);
+            }
+
+            // Correct -- the layers are declared in the same order they are invoked.
+            private Module lin1;
+            private Module lin2;
+        }
+
+        private class TestModule2 : Module
+        {
+            public TestModule2()
+                : base("TestModule2")
+            {
+                lin1 = Linear(100, 10);
+                lin2 = Linear(10, 5);
+                RegisterComponents();
+            }
+
+            public override Tensor forward(Tensor input)
+            {
+                using (var x = lin1.forward(input))
+                return lin2.forward(x);
+            }
+
+            // Incorrect -- the layers are not declared in the same order they are invoked.
+            private Module lin2;
+            private Module lin1;
+        }
+
+        ...
+        TestModule1 mod1 = ...
+        TestModule2 mod2 = ...
+        var seq1 = nn.Sequential(mod1.named_children());
+        seq1.forward(t);                 // Does the same as mod1.forward(t)
+        var seq2 = nn.Sequential(mod2.named_children());
+        seq2.forward(t);                 // This probably blows up.
+```
+
+

src/TorchSharp/TorchVision/models/AlexNet.cs

@@ -60,7 +60,7 @@ namespace Modules
         public class AlexNet : Module
         {
             private readonly Module features;
-            private readonly Module avgPool;
+            private readonly Module avgpool;
             private readonly Module classifier;
 
             public AlexNet(int numClasses, float dropout = 0.5f, string weights_file = null, bool skipfc = true, Device device = null) : base(nameof(AlexNet))
@@ -81,7 +81,7 @@ public AlexNet(int numClasses, float dropout = 0.5f, string weights_file = null,
                     MaxPool2d(kernelSize: 3, stride: 2)
                 );
 
-                avgPool = AdaptiveAvgPool2d(new long[] { 6, 6 });
+                avgpool = AdaptiveAvgPool2d(new long[] { 6, 6 });
 
                 classifier = Sequential(
                     Dropout(probability: dropout),
@@ -108,7 +108,7 @@ public override Tensor forward(Tensor input)
             {
                 using (var _ = NewDisposeScope()) {
                     var f = features.forward(input);
-                    var avg = avgPool.forward(f);
+                    var avg = avgpool.forward(f);
                     var x = avg.flatten(1);
                     return classifier.forward(x).MoveToOuterDisposeScope();
                 }

src/TorchSharp/TorchVision/models/GoogleNet.cs

@@ -71,19 +71,18 @@ public class GoogleNet : Module
 
             private readonly Module conv1;
             private readonly Module maxpool1;
-            private readonly Module maxpool2;
-            private readonly Module maxpool3;
-            private readonly Module maxpool4;
             private readonly Module conv2;
             private readonly Module conv3;
+            private readonly Module maxpool2;
             private readonly Module inception3a;
             private readonly Module inception3b;
-
+            private readonly Module maxpool3;
             private readonly Module inception4a;
             private readonly Module inception4b;
             private readonly Module inception4c;
             private readonly Module inception4d;
             private readonly Module inception4e;
+            private readonly Module maxpool4;
             private readonly Module inception5a;
             private readonly Module inception5b;
             //private readonly Module aux1;

src/TorchSharp/TorchVision/models/InceptionV3.cs

@@ -70,11 +70,11 @@ public class InceptionV3 : Module
             private readonly Module Conv2d_1a_3x3;
             private readonly Module Conv2d_2a_3x3;
             private readonly Module Conv2d_2b_3x3;
+            private readonly Module maxpool1;
             private readonly Module Conv2d_3b_1x1;
             private readonly Module Conv2d_4a_3x3;
-
-            private readonly Module maxpool1;
             private readonly Module maxpool2;
+
             private readonly Module Mixed_5b;
             private readonly Module Mixed_5c;
             private readonly Module Mixed_5d;

src/TorchSharp/TorchVision/models/ResNet.cs

@@ -3,6 +3,7 @@
 using System.Collections.Generic;
 using System.IO;
 using System.Net;
+using TorchSharp.Modules;
 using TorchSharp.torchvision.Modules;
 using static TorchSharp.torch;
 using static TorchSharp.torch.nn;
@@ -218,19 +219,18 @@ public class ResNet : Module
 
             private readonly Module conv1;
             private readonly Module bn1;
-            private readonly Module relu1;
+            private readonly Module relu;
+            private readonly Module maxpool;
 
-            private readonly TorchSharp.Modules.ModuleList layer1 = new TorchSharp.Modules.ModuleList();
-            private readonly TorchSharp.Modules.ModuleList layer2 = new TorchSharp.Modules.ModuleList();
-            private readonly TorchSharp.Modules.ModuleList layer3 = new TorchSharp.Modules.ModuleList();
-            private readonly TorchSharp.Modules.ModuleList layer4 = new TorchSharp.Modules.ModuleList();
+            private readonly Sequential layer1 = Sequential();
+            private readonly Sequential layer2 = Sequential();
+            private readonly Sequential layer3 = Sequential();
+            private readonly Sequential layer4 = Sequential();
 
             private readonly Module avgpool;
-            private readonly Module maxpool;
             private readonly Module flatten;
             private readonly Module fc;
 
-
             private int in_planes = 64;
 
             public static ResNet ResNet18(int numClasses,
@@ -320,7 +320,7 @@ public ResNet(string name,
 
                 conv1 = Conv2d(3, 64, kernelSize: 7, stride: 2, padding: 3, bias: false);
                 bn1 = BatchNorm2d(64);
-                relu1 = ReLU(inPlace: true);
+                relu = ReLU(inPlace: true);
                 maxpool = MaxPool2d(kernelSize: 2, stride: 2, padding: 1);
                 MakeLayer(layer1, block, expansion, 64, num_blocks[0], 1);
                 MakeLayer(layer2, block, expansion, 128, num_blocks[1], 2);
@@ -356,7 +356,7 @@ public ResNet(string name,
                     this.to(device);
             }
 
-            private void MakeLayer(TorchSharp.Modules.ModuleList modules, Func<int, int, int, Module> block, int expansion, int planes, int num_blocks, int stride)
+            private void MakeLayer(Sequential modules, Func<int, int, int, Module> block, int expansion, int planes, int num_blocks, int stride)
             {
                 var strides = new List<int>();
                 strides.Add(stride);
@@ -373,12 +373,12 @@ public override Tensor forward(Tensor input)
             {
                 using (var scope = NewDisposeScope()) {
 
-                    var x = maxpool.forward(relu1.forward(bn1.forward(conv1.forward(input))));
+                    var x = maxpool.forward(relu.forward(bn1.forward(conv1.forward(input))));
 
-                    foreach (var m in layer1) x = m.forward(x);
-                    foreach (var m in layer2) x = m.forward(x);
-                    foreach (var m in layer3) x = m.forward(x);
-                    foreach (var m in layer4) x = m.forward(x);
+                    x = layer1.forward(x);
+                    x = layer2.forward(x);
+                    x = layer3.forward(x);
+                    x = layer4.forward(x);
 
                     var res = fc.forward(flatten.forward(avgpool.forward(x)));
                     scope.MoveToOuter(res);

src/TorchSharp/TorchVision/models/VGG.cs

@@ -334,8 +334,8 @@ public class VGG : Module
             };
 
             private readonly Module features;
-            private readonly Module classifier;
             private readonly Module avgpool;
+            private readonly Module classifier;
 
             public VGG(string name,
                 int numClasses,

test/TorchSharpTest/TestTorchTensorBugs.cs

@@ -5,12 +5,13 @@
 using System.IO;
 
 using System.Threading;
+using System.Runtime.CompilerServices;
 
 using static TorchSharp.torch.nn;
 using Xunit;
 
 using static TorchSharp.torch;
-using System.Runtime.CompilerServices;
+using static TorchSharp.torchvision.models;
 
 #nullable enable
 
@@ -772,5 +773,19 @@ public void ValidateBug679()
             spec = torch.rand(1, 257, 500, dtype: dtype);
             x = torch.istft(spec, 512, 160, 400, null);
         }
+
+        [Fact]
+        public void ValidateBug715()
+        {
+            var resnet = resnet18();
+            var resnetlist = resnet.named_children();
+            var list = resnetlist.Take(6);
+            var bone = nn.Sequential(list);
+
+            var x = torch.zeros(1, 3, 64, 160);
+
+            // This should not blow up.
+            var tmp = bone.forward(x);
+        }
     }
 }