Merge pull request #1027 from AsakusaRinne/fix_1013

Fix the error when loading VGG19.

src/TensorFlowNET.Core/Keras/ArgsDefinition/AutoSerializeLayerArgs.cs

@@ -9,7 +9,7 @@ namespace Tensorflow.Keras.ArgsDefinition
     /// This class has nothing but the attributes different from `LayerArgs`.
     /// It's used to serialize the model to `tf` format. 
     /// If the `get_config` of a `Layer` in python code of tensorflow contains `super().get_config`,
-    /// then the Arg definition should inherit `utoSerializeLayerArgs` instead of `LayerArgs`.
+    /// then the Arg definition should inherit `AutoSerializeLayerArgs` instead of `LayerArgs`.
     /// </summary>
     public class AutoSerializeLayerArgs: LayerArgs
     {

src/TensorFlowNET.Core/Keras/Common/CustomizedShapeJsonConverter.cs

@@ -7,6 +7,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Common
 {
+    class ShapeInfoFromPython
+    {
+        public string class_name { get; set; }
+        public long?[] items { get; set; }
+    }
     public class CustomizedShapeJsonConverter: JsonConverter
     {
         public override bool CanConvert(Type objectType)
@@ -44,36 +49,23 @@ namespace Tensorflow.Keras.Common
                         dims[i] = shape.dims[i];
                     }
                 }
-                var token = JToken.FromObject(dims);
+                var token = JToken.FromObject(new ShapeInfoFromPython()
+                {
+                    class_name = "__tuple__",
+                    items = dims
+                });
                 token.WriteTo(writer);
             }
         }
 
         public override object? ReadJson(JsonReader reader, Type objectType, object? existingValue, JsonSerializer serializer)
         {
-            long?[] dims;
-            try
-            {
-                dims = serializer.Deserialize(reader, typeof(long?[])) as long?[];
-            }
-            catch (JsonSerializationException ex)
-            {
-                if (reader.Value.Equals("class_name"))
-                {
-                    reader.Read();
-                    reader.Read();
-                    reader.Read();
-                    dims = serializer.Deserialize(reader, typeof(long?[])) as long?[];
-                }
-                else
-                {
-                    throw ex;
-                }
-            }
-            if (dims is null)
+            var shape_info_from_python = serializer.Deserialize<ShapeInfoFromPython>(reader);
+            if (shape_info_from_python is null)
             {
                 return null;
             }
+            long ?[]dims = shape_info_from_python.items;
             long[] convertedDims = new long[dims.Length];
             for(int i = 0; i < dims.Length; i++)
             {

src/TensorFlowNET.Core/Tensorflow.Binding.csproj

@@ -108,7 +108,7 @@ https://tensorflownet.readthedocs.io</Description>
 
   <ItemGroup>
     <PackageReference Include="MethodBoundaryAspect.Fody" Version="2.0.148" />
-    <PackageReference Include="Newtonsoft.Json" Version="13.0.2" />
+    <PackageReference Include="Newtonsoft.Json" Version="13.0.3" />
     <PackageReference Include="OneOf" Version="3.0.223" />
     <PackageReference Include="Protobuf.Text" Version="0.7.0" />
     <PackageReference Include="Serilog.Sinks.Console" Version="4.1.0" />

src/TensorFlowNET.Core/Training/Saving/SavedModel/loader.cs

@@ -563,7 +563,7 @@ namespace Tensorflow
             return proto.KindCase switch
             {
                 SavedObject.KindOneofCase.UserObject => _recreate_user_object(proto.UserObject, node_id),
-                SavedObject.KindOneofCase.Function => _recreate_function(proto.Function, null),
+                SavedObject.KindOneofCase.Function => _recreate_function(proto.Function, dependencies),
                 SavedObject.KindOneofCase.BareConcreteFunction => _recreate_bare_concrete_function(proto.BareConcreteFunction, dependencies),
                 SavedObject.KindOneofCase.Variable => _recreate_variable(proto.Variable),
                 SavedObject.KindOneofCase.CapturedTensor => throw new NotImplementedException(),
@@ -626,7 +626,7 @@ namespace Tensorflow
         }
 
         private (Function, Action<object, object, object>) _recreate_function(SavedFunction proto,
-            Dictionary<OneOf<string, int>, Trackable> dependencies)
+            IDictionary<OneOf<string, int>, Trackable> dependencies)
         {
             var fn = function_deserialization.recreate_function(proto, _concrete_functions);
             foreach (var name in proto.ConcreteFunctions)
@@ -644,6 +644,13 @@ namespace Tensorflow
             return (fn, setattr);
         }
 
+        private (Tensor, Action<object, object, object>) _get_tensor_from_fn(CapturedTensor proto)
+        {
+            var outer_graph = _concrete_functions[proto.ConcreteFunction].func_graph;
+            var captured_tensor = outer_graph.get_tensor_by_name(proto.Name);
+            return (captured_tensor, setattr);
+        }
+
         // TODO: remove this to a common class.
         public static Action<object, object, object> setattr = (x, y, z) =>
         {

src/TensorFlowNET.Keras/Utils/generic_utils.cs

@@ -71,6 +71,9 @@ namespace Tensorflow.Keras.Utils
             var args = deserializationGenericMethod.Invoke(config, null);
             var layer = Assembly.Load("Tensorflow.Keras").CreateInstance($"Tensorflow.Keras.Layers.{class_name}", true, BindingFlags.Default, null, new object[] { args }, null, null);
             Debug.Assert(layer is Layer);
+
+            // TODO(Rinne): _shared_object_loading_scope().set(shared_object_id, deserialized_obj)
+
             return layer as Layer;
         }
 
@@ -82,6 +85,9 @@ namespace Tensorflow.Keras.Utils
                 return null;
             }
             Debug.Assert(layer is Layer);
+
+            // TODO(Rinne): _shared_object_loading_scope().set(shared_object_id, deserialized_obj)
+
             return layer as Layer;
         }
 

test/TensorFlowNET.Keras.UnitTest/SaveModel/SequentialModelLoad.cs

@@ -6,13 +6,13 @@ using Tensorflow.Keras.Optimizers;
 using Tensorflow.Keras.UnitTest.Helpers;
 using Tensorflow.NumPy;
 using static Tensorflow.Binding;
+using static Tensorflow.KerasApi;
 
 namespace TensorFlowNET.Keras.UnitTest.SaveModel;
 
 [TestClass]
 public class SequentialModelLoad
 {
-    [Ignore]
     [TestMethod]
     public void SimpleModelFromAutoCompile()
     {
@@ -80,4 +80,27 @@ public class SequentialModelLoad
 
         model.fit(dataset.Train.Data, dataset.Train.Labels, batch_size, num_epochs);
     }
+
+    [Ignore]
+    [TestMethod]
+    public void VGG19()
+    {
+        var model = tf.keras.models.load_model(@"D:\development\tf.net\models\VGG19");
+        model.summary();
+
+        var classify_model = keras.Sequential(new System.Collections.Generic.List<Tensorflow.Keras.ILayer>()
+        {
+            model,
+            keras.layers.Flatten(),
+            keras.layers.Dense(10),
+        });
+        classify_model.summary();
+
+        classify_model.compile(tf.keras.optimizers.Adam(), tf.keras.losses.SparseCategoricalCrossentropy(), new string[] { "accuracy" });
+
+        var x = np.random.uniform(0, 1, (8, 512, 512, 3));
+        var y = np.ones((8));
+
+        classify_model.fit(x, y, batch_size: 4);
+    }
 }