fix: fix the bug of repeated progress bar in Model.fit()

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

@@ -14,6 +14,9 @@ public interface ICallback
     void on_predict_batch_end(long end_step, Dictionary<string, Tensors> logs);
     void on_predict_end();
     void on_test_begin();
+    void on_test_end(Dictionary<string, float> logs);
     void on_test_batch_begin(long step);
     void on_test_batch_end(long end_step, Dictionary<string, float> logs);
+
+
 }

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

@@ -60,7 +60,7 @@ void load_weights(string filepath,
         bool skip_mismatch = false, 
         object options = null);
 
-    Dictionary<string, float> evaluate(Tensor x, Tensor y,
+    Dictionary<string, float> evaluate(NDArray x, NDArray y,
             int batch_size = -1,
             int verbose = 1,
             int steps = -1,

src/TensorFlowNET.Keras/Callbacks/CallbackList.cs

@@ -73,4 +73,9 @@ public void on_test_batch_end(long end_step, Dictionary<string, float> logs)
     {
         callbacks.ForEach(x => x.on_test_batch_end(end_step, logs));
     }
+
+    public void on_test_end(Dictionary<string, float> logs)
+    {
+        callbacks.ForEach(x => x.on_test_end(logs));
+    }
 }

src/TensorFlowNET.Keras/Callbacks/Earlystopping.cs

@@ -150,4 +150,8 @@ public bool _is_improvement(float monitor_value, float reference_value)
                 return less_op;
         }
     }
+
+    public void on_test_end(Dictionary<string, float> logs)
+    {
+    }
 }

src/TensorFlowNET.Keras/Callbacks/History.cs

@@ -81,4 +81,8 @@ public void on_test_batch_begin(long step)
     public void on_test_batch_end(long end_step, Dictionary<string, float> logs)
     {
     }
+
+    public void on_test_end(Dictionary<string, float> logs)
+    {
+    }
 }

src/TensorFlowNET.Keras/Callbacks/ProgbarLogger.cs

@@ -118,5 +118,8 @@ public void on_test_batch_end(long end_step, Dictionary<string, float> logs)
             }
         }
 
+        public void on_test_end(Dictionary<string, float> logs)
+        {
+        }
     }
 }

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

@@ -27,7 +27,7 @@ public partial class Model
         /// <param name="use_multiprocessing"></param>
         /// <param name="return_dict"></param>
         /// <param name="is_val"></param>
-        public Dictionary<string, float> evaluate(Tensor x, Tensor y,
+        public Dictionary<string, float> evaluate(NDArray x, NDArray y,
             int batch_size = -1,
             int verbose = 1,
             int steps = -1,
@@ -115,62 +115,53 @@ public Dictionary<string, float> evaluate(IDatasetV2 x, int verbose = 1, bool is
         /// <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)
+        Dictionary<string, float> evaluate(DataHandler data_handler, CallbackList callbacks, bool is_val, Func<DataHandler, OwnedIterator, Dictionary<string, float>> test_func)
         {
             callbacks.on_test_begin();
 
-            var results = new Dictionary<string, float>();
-            var logs = results;
+            var logs = new Dictionary<string, float>();
             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);
-
-                    logs = test_func(data_handler, iterator.next());
-
-                    tf_with(ops.control_dependencies(Array.Empty<object>()), ctl => _train_counter.assign_add(1));
-
+                    logs = test_func(data_handler, iterator);
                     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;
-            }
-
+            callbacks.on_test_end(logs);
+            var results = new Dictionary<string, float>(logs);
             return results;
         }
 
-        Dictionary<string, float> test_function(DataHandler data_handler, Tensor[] data)
+        Dictionary<string, float> test_function(DataHandler data_handler, OwnedIterator iterator)
         {
-            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);
+            var data = iterator.next();
+            var outputs = test_step(data_handler, data[0], data[1]);
+            tf_with(ops.control_dependencies(new object[0]), ctl => _test_counter.assign_add(1));
             return outputs;
         }
 
-        Dictionary<string, float> test_step_multi_inputs_function(DataHandler data_handler, Tensor[] data)
+        Dictionary<string, float> test_step_multi_inputs_function(DataHandler data_handler, OwnedIterator iterator)
         {
+            var data = iterator.next();
             var x_size = data_handler.DataAdapter.GetDataset().FirstInputTensorCount;
-            var outputs = train_step(data_handler, new Tensors(data.Take(x_size).ToArray()), new Tensors(data.Skip(x_size).ToArray()));
-            tf_with(ops.control_dependencies(new object[0]), ctl => _train_counter.assign_add(1));
+            var outputs = test_step(data_handler, data.Take(x_size).ToArray(), data.Skip(x_size).ToArray());
+            tf_with(ops.control_dependencies(new object[0]), ctl => _test_counter.assign_add(1));
             return outputs;
         }
+
+
+        Dictionary<string, float> test_step(DataHandler data_handler, Tensors x, Tensors y)
+        {
+            (x, y) = data_handler.DataAdapter.Expand1d(x, y);
+            var y_pred = Apply(x, training: false);
+            var loss = compiled_loss.Call(y, y_pred);
+            compiled_metrics.update_state(y, y_pred);
+            return metrics.Select(x => (x.Name, x.result())).ToDictionary(x => x.Item1, x => (float)x.Item2);
+        }
     }
 }

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

@@ -266,7 +266,7 @@ History FitInternal(DataHandler data_handler, int epochs, int verbose, List<ICal
                 {
                     // Because evaluate calls call_test_batch_end, this interferes with our output on the screen
                     // so we need to pass a is_val parameter to stop on_test_batch_end
-                    var val_logs = evaluate((Tensor)validation_data.Value.Item1, validation_data.Value.Item2, is_val:true);
+                    var val_logs = evaluate(validation_data.Value.Item1, validation_data.Value.Item2, is_val:true);
                     foreach (var log in val_logs)
                     {
                         logs["val_" + log.Key] = log.Value;