refactor: Model.eval

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(NDArray x, NDArray y,
+    Dictionary<string, float> evaluate(Tensor x, Tensor y,
             int batch_size = -1,
             int verbose = 1,
             int steps = -1,

src/TensorFlowNET.Core/Tensors/Tensors.cs

@@ -90,73 +90,73 @@ public T[] ToArray<T>() where T: unmanaged
         }
 
         #region Explicit Conversions
-        public unsafe static explicit operator bool(Tensors tensor)
+        public static explicit operator bool(Tensors tensor)
         {
             EnsureSingleTensor(tensor, "explicit conversion to bool");
             return (bool)tensor[0];
         }
 
-        public unsafe static explicit operator sbyte(Tensors tensor)
+        public static explicit operator sbyte(Tensors tensor)
         {
             EnsureSingleTensor(tensor, "explicit conversion to sbyte");
             return (sbyte)tensor[0];
         }
 
-        public unsafe static explicit operator byte(Tensors tensor)
+        public static explicit operator byte(Tensors tensor)
         {
             EnsureSingleTensor(tensor, "explicit conversion to byte");
             return (byte)tensor[0];
         }
 
-        public unsafe static explicit operator ushort(Tensors tensor)
+        public static explicit operator ushort(Tensors tensor)
         {
             EnsureSingleTensor(tensor, "explicit conversion to ushort");
             return (ushort)tensor[0];
         }
 
-        public unsafe static explicit operator short(Tensors tensor)
+        public static explicit operator short(Tensors tensor)
         {
             EnsureSingleTensor(tensor, "explicit conversion to short");
             return (short)tensor[0];
         }
 
-        public unsafe static explicit operator int(Tensors tensor)
+        public static explicit operator int(Tensors tensor)
         {
             EnsureSingleTensor(tensor, "explicit conversion to int");
             return (int)tensor[0];
         }
 
-        public unsafe static explicit operator uint(Tensors tensor)
+        public static explicit operator uint(Tensors tensor)
         {
             EnsureSingleTensor(tensor, "explicit conversion to uint");
             return (uint)tensor[0];
         }
 
-        public unsafe static explicit operator long(Tensors tensor)
+        public static explicit operator long(Tensors tensor)
         {
             EnsureSingleTensor(tensor, "explicit conversion to long");
             return (long)tensor[0];
         }
 
-        public unsafe static explicit operator ulong(Tensors tensor)
+        public static explicit operator ulong(Tensors tensor)
         {
             EnsureSingleTensor(tensor, "explicit conversion to ulong");
             return (ulong)tensor[0];
         }
 
-        public unsafe static explicit operator float(Tensors tensor)
+        public static explicit operator float(Tensors tensor)
         {
             EnsureSingleTensor(tensor, "explicit conversion to byte");
             return (byte)tensor[0];
         }
 
-        public unsafe static explicit operator double(Tensors tensor)
+        public static explicit operator double(Tensors tensor)
         {
             EnsureSingleTensor(tensor, "explicit conversion to double");
             return (double)tensor[0];
         }
 
-        public unsafe static explicit operator string(Tensors tensor)
+        public static explicit operator string(Tensors tensor)
         {
             EnsureSingleTensor(tensor, "explicit conversion to string");
             return (string)tensor[0];

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

@@ -1,14 +1,14 @@
-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
 {
@@ -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(NDArray x, NDArray y,
+        public Dictionary<string, float> evaluate(Tensor x, Tensor y,
             int batch_size = -1,
             int verbose = 1,
             int steps = -1,
@@ -64,34 +64,11 @@ public Dictionary<string, float> evaluate(NDArray x, NDArray y,
                 Verbose = verbose,
                 Steps = data_handler.Inferredsteps
             });
-            callbacks.on_test_begin();
-
-            //Dictionary<string, float>? logs = null;
-            var logs = new Dictionary<string, float>();
-            foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
-            {
-                reset_metrics();
-                // data_handler.catch_stop_iteration();
-
-                foreach (var step in data_handler.steps())
-                {
-                    callbacks.on_test_batch_begin(step);
-                    logs = test_function(data_handler, iterator);
-                    var end_step = step + data_handler.StepIncrement;
-                    if (is_val == false)
-                        callbacks.on_test_batch_end(end_step, logs);
-                }
-            }
 
-            var results = new Dictionary<string, float>();
-            foreach (var log in logs)
-            {
-                results[log.Key] = log.Value;
-            }
-            return results;
+            return evaluate(data_handler, callbacks, is_val, test_function);
         }
 
-        public Dictionary<string, float> evaluate(IEnumerable<Tensor> x, NDArray y, int verbose = 1, bool is_val = false)
+        public Dictionary<string, float> evaluate(IEnumerable<Tensor> x, Tensor y, int verbose = 1, bool is_val = false)
         {
             var data_handler = new DataHandler(new DataHandlerArgs
             {
@@ -107,34 +84,10 @@ public Dictionary<string, float> evaluate(IEnumerable<Tensor> x, NDArray y, int
                 Verbose = verbose,
                 Steps = data_handler.Inferredsteps
             });
-            callbacks.on_test_begin();
 
-            Dictionary<string, float> logs = null;
-            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_step_multi_inputs_function(data_handler, iterator);
-                    var end_step = step + data_handler.StepIncrement;
-                    if (is_val == false)
-                        callbacks.on_test_batch_end(end_step, logs);
-                }
-            }
-
-            var results = new Dictionary<string, float>();
-            foreach (var log in logs)
-            {
-                results[log.Key] = log.Value;
-            }
-            return results;
+            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
@@ -150,9 +103,24 @@ public Dictionary<string, float> evaluate(IDatasetV2 x, int verbose = 1, bool is
                 Verbose = verbose,
                 Steps = data_handler.Inferredsteps
             });
+
+            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();
 
-            Dictionary<string, float> logs = null;
+            var results = new Dictionary<string, float>();
+            var logs = results;
             foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
             {
                 reset_metrics();
@@ -162,45 +130,46 @@ public Dictionary<string, float> evaluate(IDatasetV2 x, int verbose = 1, bool is
                 foreach (var step in data_handler.steps())
                 {
                     callbacks.on_test_batch_begin(step);
-                    logs = test_function(data_handler, iterator);
+
+                    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 == false)
+                    if (!is_val)
                         callbacks.on_test_batch_end(end_step, logs);
                 }
+
+                if (!is_val)
+                    callbacks.on_epoch_end(epoch, logs);
             }
 
-            var results = new Dictionary<string, float>();
             foreach (var log in logs)
             {
                 results[log.Key] = log.Value;
             }
+
             return results;
         }
 
-        Dictionary<string, float> test_function(DataHandler data_handler, OwnedIterator iterator)
+        Dictionary<string, float> test_function(DataHandler data_handler, Tensor[] data)
         {
-            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));
+            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, OwnedIterator iterator)
+
+        Dictionary<string, float> test_step_multi_inputs_function(DataHandler data_handler, Tensor[] data)
         {
-            var data = iterator.next();
             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)));
-            tf_with(ops.control_dependencies(new object[0]), ctl => _train_counter.assign_add(1));
             return outputs;
         }
-        Dictionary<string, float> test_step(DataHandler data_handler, Tensor x, Tensor 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(validation_data.Value.Item1, validation_data.Value.Item2, is_val:true);
+                    var val_logs = evaluate((Tensor)validation_data.Value.Item1, validation_data.Value.Item2, is_val:true);
                     foreach (var log in val_logs)
                     {
                         logs["val_" + log.Key] = log.Value;