Fix model.evaluate don't have output

Author: Wanglongzhi2001

src/TensorFlowNET.Core/Keras/Engine/ICallback.cs

@@ -12,4 +12,6 @@ public interface ICallback
     void on_predict_batch_begin(long step);
     void on_predict_batch_end(long end_step, Dictionary<string, Tensors> logs);
     void on_predict_end();
+    void on_test_begin();
+    void on_test_batch_end(long end_step, IEnumerable<(string, Tensor)> logs);
 }

src/TensorFlowNET.Keras/Callbacks/CallbackList.cs

@@ -20,7 +20,10 @@ public void on_train_begin()
     {
         callbacks.ForEach(x => x.on_train_begin());
     }
-
+    public void on_test_begin()
+    {
+        callbacks.ForEach(x => x.on_test_begin());
+    }
     public void on_epoch_begin(int epoch)
     {
         callbacks.ForEach(x => x.on_epoch_begin(epoch));
@@ -60,4 +63,13 @@ public void on_predict_end()
     {
         callbacks.ForEach(x => x.on_predict_end());
     }
+
+    public void on_test_batch_begin(long step)
+    {
+        callbacks.ForEach(x => x.on_train_batch_begin(step));
+    }
+    public void on_test_batch_end(long end_step, IEnumerable<(string, Tensor)> logs)
+    {
+        callbacks.ForEach(x => x.on_test_batch_end(end_step, logs));
+    }
 }

src/TensorFlowNET.Keras/Callbacks/History.cs

@@ -18,15 +18,19 @@ public void on_train_begin()
         epochs = new List<int>();
         history = new Dictionary<string, List<float>>();
     }
-
+    public void on_test_begin()
+    {
+        epochs = new List<int>();
+        history = new Dictionary<string, List<float>>();
+    }
     public void on_epoch_begin(int epoch)
     {
 
     }
 
     public void on_train_batch_begin(long step)
     {
-        
+
     }
 
     public void on_train_batch_end(long end_step, Dictionary<string, float> logs)
@@ -55,16 +59,25 @@ public void on_predict_begin()
 
     public void on_predict_batch_begin(long step)
     {
-        
+
     }
 
     public void on_predict_batch_end(long end_step, Dictionary<string, Tensors> logs)
     {
-        
+
     }
 
     public void on_predict_end()
     {
-        
+
+    }
+
+    public void on_test_batch_begin(long step)
+    {
+
+    }
+
+    public void on_test_batch_end(long end_step, IEnumerable<(string, Tensor)> logs)
+    {
     }
 }

src/TensorFlowNET.Keras/Callbacks/ProgbarLogger.cs

@@ -22,7 +22,10 @@ public void on_train_begin()
             _called_in_fit = true;
             _sw = new Stopwatch();
         }
-
+        public void on_test_begin()
+        {
+            _sw = new Stopwatch();
+        }
         public void on_epoch_begin(int epoch)
         {
             _reset_progbar();
@@ -44,7 +47,7 @@ public void on_train_batch_end(long end_step, Dictionary<string, float> logs)
             var progress = "";
             var length = 30.0 / _parameters.Steps;
             for (int i = 0; i < Math.Floor(end_step * length - 1); i++)
-                    progress += "=";
+                progress += "=";
             if (progress.Length < 28)
                 progress += ">";
             else
@@ -84,17 +87,35 @@ public void on_predict_begin()
 
         public void on_predict_batch_begin(long step)
         {
-            
+
         }
 
         public void on_predict_batch_end(long end_step, Dictionary<string, Tensors> logs)
         {
-            
+
         }
 
         public void on_predict_end()
         {
-            
+
+        }
+
+        public void on_test_batch_begin(long step)
+        {
+            _sw.Restart();
         }
+        public void on_test_batch_end(long end_step, IEnumerable<(string, Tensor)> logs)
+        {
+            _sw.Stop();
+            var elapse = _sw.ElapsedMilliseconds;
+            var results = string.Join(" - ", logs.Select(x => $"{x.Item1}: {(float)x.Item2.numpy():F6}"));
+
+            Binding.tf_output_redirect.Write($"{end_step + 1:D4}/{_parameters.Steps:D4} - {elapse}ms/step - {results}");
+            if (!Console.IsOutputRedirected)
+            {
+                Console.CursorLeft = 0;
+            }
+        }
+
     }
 }

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

@@ -5,6 +5,10 @@
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine.DataAdapters;
 using static Tensorflow.Binding;
+using Tensorflow.Keras.Layers;
+using Tensorflow.Keras.Utils;
+using Tensorflow;
+using Tensorflow.Keras.Callbacks;
 
 namespace Tensorflow.Keras.Engine
 {
@@ -31,6 +35,11 @@ public void evaluate(NDArray x, NDArray y,
             bool use_multiprocessing = false,
             bool return_dict = false)
         {
+            if (x.dims[0] != y.dims[0])
+            {
+                throw new InvalidArgumentError(
+                    $"The array x and y should have same value at dim 0, but got {x.dims[0]} and {y.dims[0]}");
+            }
             var data_handler = new DataHandler(new DataHandlerArgs
             {
                 X = x,
@@ -46,18 +55,31 @@ public void evaluate(NDArray x, NDArray y,
                 StepsPerExecution = _steps_per_execution
             });
 
+            var callbacks = new CallbackList(new CallbackParams
+            {
+                Model = this,
+                Verbose = verbose,
+                Steps = data_handler.Inferredsteps
+            });
+            callbacks.on_test_begin();
+
             foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
             {
                 reset_metrics();
-                // callbacks.on_epoch_begin(epoch)
+                //callbacks.on_epoch_begin(epoch);
                 // data_handler.catch_stop_iteration();
-                IEnumerable<(string, Tensor)> results = null;
+                IEnumerable<(string, Tensor)> logs = null;
+
                 foreach (var step in data_handler.steps())
                 {
-                    // callbacks.on_train_batch_begin(step)
-                    results = test_function(data_handler, iterator);
+                    callbacks.on_train_batch_begin(step);
+                    logs = test_function(data_handler, iterator);
+                    var end_step = step + data_handler.StepIncrement;
+                    callbacks.on_test_batch_end(end_step, logs);
                 }
             }
+            GC.Collect();
+            GC.WaitForPendingFinalizers();
         }
 
         public KeyValuePair<string, float>[] evaluate(IDatasetV2 x)
@@ -75,7 +97,8 @@ public KeyValuePair<string, float>[] evaluate(IDatasetV2 x)
                 reset_metrics();
                 // callbacks.on_epoch_begin(epoch)
                 // data_handler.catch_stop_iteration();
-                
+
+
                 foreach (var step in data_handler.steps())
                 {
                     // callbacks.on_train_batch_begin(step)