Update Model.Evaluate.cs

Fix my bad:

Bad handling between test_function and test_step_multi_inputs_function.

Author: DevNullx64

src/TensorFlowNET.Keras/Engine/Model.Evaluate.cs

@@ -1,51 +1,19 @@
-using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Linq;
+using Tensorflow;
 using Tensorflow.Keras.ArgsDefinition;
+using Tensorflow.Keras.Callbacks;
 using Tensorflow.Keras.Engine.DataAdapters;
-using static Tensorflow.Binding;
 using Tensorflow.Keras.Layers;
 using Tensorflow.Keras.Utils;
-using Tensorflow;
-using Tensorflow.Keras.Callbacks;
+using Tensorflow.NumPy;
+using static Tensorflow.Binding;
 
 namespace Tensorflow.Keras.Engine
 {
     public partial class Model
     {
-        protected Dictionary<string, float> evaluate(CallbackList callbacks, DataHandler data_handler, bool is_val)
-        {
-            callbacks.on_test_begin();
-
-            //Dictionary<string, float>? logs = null;
-            var logs = new Dictionary<string, float>();
-            int x_size = data_handler.DataAdapter.GetDataset().FirstInputTensorCount;
-            foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
-            {
-                reset_metrics();
-                callbacks.on_epoch_begin(epoch);
-                // data_handler.catch_stop_iteration();
-
-                foreach (var step in data_handler.steps())
-                {
-                    callbacks.on_test_batch_begin(step);
-
-                    var data = iterator.next();
-
-                    logs = train_step(data_handler, new Tensors(data.Take(x_size)), new Tensors(data.Skip(x_size)));
-                    tf_with(ops.control_dependencies(Array.Empty<object>()), ctl => _test_counter.assign_add(1));
-
-                    var end_step = step + data_handler.StepIncrement;
-
-                    if (!is_val)
-                        callbacks.on_test_batch_end(end_step, logs);
-                }
-            }
-
-            return logs;
-        }
-
         /// <summary>
         /// Returns the loss value & metrics values for the model in test mode.
         /// </summary>
@@ -97,7 +65,7 @@ public Dictionary<string, float> evaluate(Tensor x, Tensor y,
                 Steps = data_handler.Inferredsteps
             });
 
-            return evaluate(callbacks, data_handler, is_val);
+            return evaluate(data_handler, callbacks, is_val, test_function);
         }
 
         public Dictionary<string, float> evaluate(IEnumerable<Tensor> x, Tensor y, int verbose = 1, bool is_val = false)
@@ -117,10 +85,9 @@ public Dictionary<string, float> evaluate(IEnumerable<Tensor> x, Tensor y, int v
                 Steps = data_handler.Inferredsteps
             });
 
-            return evaluate(callbacks, data_handler, is_val);
+            return evaluate(data_handler, callbacks, is_val, test_step_multi_inputs_function);
         }
 
-
         public Dictionary<string, float> evaluate(IDatasetV2 x, int verbose = 1, bool is_val = false)
         {
             var data_handler = new DataHandler(new DataHandlerArgs
@@ -137,7 +104,74 @@ public Dictionary<string, float> evaluate(IDatasetV2 x, int verbose = 1, bool is
                 Steps = data_handler.Inferredsteps
             });
 
-            return evaluate(callbacks, data_handler, is_val);
+            return evaluate(data_handler, callbacks, is_val, test_function);
+        }
+
+        /// <summary>
+        /// Internal bare implementation of evaluate function.
+        /// </summary>
+        /// <param name="data_handler">Interations handling objects</param>
+        /// <param name="callbacks"></param>
+        /// <param name="test_func">The function to be called on each batch of data.</param>
+        /// <param name="is_val">Whether it is validation or test.</param>
+        /// <returns></returns>
+        Dictionary<string, float> evaluate(DataHandler data_handler, CallbackList callbacks, bool is_val, Func<DataHandler, Tensor[], Dictionary<string, float>> test_func)
+        {
+            callbacks.on_test_begin();
+
+            var results = new Dictionary<string, float>();
+            var logs = results;
+            foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
+            {
+                reset_metrics();
+                callbacks.on_epoch_begin(epoch);
+                // data_handler.catch_stop_iteration();
+
+                foreach (var step in data_handler.steps())
+                {
+                    callbacks.on_test_batch_begin(step);
+
+                    var data = iterator.next();
+
+                    logs = test_func(data_handler, iterator.next());
+
+                    tf_with(ops.control_dependencies(Array.Empty<object>()), ctl => _train_counter.assign_add(1));
+
+                    var end_step = step + data_handler.StepIncrement;
+                    if (!is_val)
+                        callbacks.on_test_batch_end(end_step, logs);
+                }
+
+                if (!is_val)
+                    callbacks.on_epoch_end(epoch, logs);
+            }
+
+            foreach (var log in logs)
+            {
+                results[log.Key] = log.Value;
+            }
+
+            return results;
+        }
+
+        Dictionary<string, float> test_function(DataHandler data_handler, Tensor[] data)
+        {
+            var (x, y) = data_handler.DataAdapter.Expand1d(data[0], data[1]);
+
+            var y_pred = Apply(x, training: false);
+            var loss = compiled_loss.Call(y, y_pred);
+
+            compiled_metrics.update_state(y, y_pred);
+
+            var outputs = metrics.Select(x => (x.Name, x.result())).ToDictionary(x => x.Name, x => (float)x.Item2);
+            return outputs;
+        }
+
+        Dictionary<string, float> test_step_multi_inputs_function(DataHandler data_handler, Tensor[] data)
+        {
+            var x_size = data_handler.DataAdapter.GetDataset().FirstInputTensorCount;
+            var outputs = train_step(data_handler, new Tensors(data.Take(x_size)), new Tensors(data.Skip(x_size)));
+            return outputs;
         }
     }
-}
+}