tf.PriorityQueue, Protobuf.TextFormat

docs/source/Queue.md

@@ -62,6 +62,33 @@ A FIFOQueue that supports batching variable-sized tensors by padding. A `Padding
 
 A queue implementation that dequeues elements in prioritized order. A `PriorityQueue` has bounded capacity; supports multiple concurrent producers and consumers; and provides exactly-once delivery. A `PriorityQueue` holds a list of up to `capacity` elements. Each element is a fixed-length tuple of tensors whose dtypes are described by `types`, and whose shapes are optionally described by the `shapes` argument.
 
+```csharp
+[TestMethod]
+public void PriorityQueue()
+{
+	var queue = tf.PriorityQueue(3, tf.@string);
+	var init = queue.enqueue_many(new[] { 2L, 4L, 3L }, new[] { "p1", "p2", "p3" });
+	var x = queue.dequeue();
+
+	using (var sess = tf.Session())
+	{
+		init.run();
+
+		// output will 2, 3, 4
+		var result = sess.run(x);
+		Assert.AreEqual(result[0].GetInt64(), 2L);
+
+		result = sess.run(x);
+		Assert.AreEqual(result[0].GetInt64(), 3L);
+
+		result = sess.run(x);
+		Assert.AreEqual(result[0].GetInt64(), 4L);
+	}
+}
+```
+
+
+
 #### RandomShuffleQueue
 
 A queue implementation that dequeues elements in a random order. A `RandomShuffleQueue` has bounded capacity; supports multiple concurrent producers and consumers; and provides exactly-once delivery. A `RandomShuffleQueue` holds a list of up to `capacity` elements. Each element is a fixed-length tuple of tensors whose dtypes are described by `dtypes`, and whose shapes are optionally described by the `shapes` argument.

src/TensorFlowNET.Core/APIs/tf.queue.cs

@@ -50,7 +50,7 @@ namespace Tensorflow
             string shared_name = null,
             string name = "padding_fifo_queue")
             => new PaddingFIFOQueue(capacity,
-                new [] { dtype },
+                new[] { dtype },
                 new[] { shape },
                 shared_name: shared_name,
                 name: name);
@@ -86,7 +86,26 @@ namespace Tensorflow
             => new FIFOQueue(capacity,
                 new[] { dtype },
                 new[] { shape ?? new TensorShape() },
-                new[] { name },
+                shared_name: shared_name,
+                name: name);
+
+        /// <summary>
+        /// Creates a queue that dequeues elements in a first-in first-out order.
+        /// </summary>
+        /// <param name="capacity"></param>
+        /// <param name="dtype"></param>
+        /// <param name="shape"></param>
+        /// <param name="shared_name"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public PriorityQueue PriorityQueue(int capacity,
+            TF_DataType dtype,
+            TensorShape shape = null,
+            string shared_name = null,
+            string name = "priority_queue")
+            => new PriorityQueue(capacity,
+                new[] { dtype },
+                new[] { shape ?? new TensorShape() },
                 shared_name: shared_name,
                 name: name);
     }

src/TensorFlowNET.Core/Operations/Queues/PriorityQueue.cs

@@ -0,0 +1,66 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+using static Tensorflow.Binding;
+
+namespace Tensorflow.Queues
+{
+    public class PriorityQueue : QueueBase
+    {
+        public PriorityQueue(int capacity,
+            TF_DataType[] dtypes,
+            TensorShape[] shapes,
+            string[] names = null,
+            string shared_name = null,
+            string name = "priority_queue")
+        : base(dtypes: dtypes, shapes: shapes, names: names)
+        {
+            _queue_ref = gen_data_flow_ops.priority_queue_v2(
+                component_types: dtypes,
+                shapes: shapes,
+                capacity: capacity,
+                shared_name: shared_name,
+                name: name);
+
+            _name = _queue_ref.op.name.Split('/').Last();
+
+            var dtypes1 = dtypes.ToList();
+            dtypes1.Insert(0, TF_DataType.TF_INT64);
+            _dtypes = dtypes1.ToArray();
+
+            var shapes1 = shapes.ToList();
+            shapes1.Insert(0, new TensorShape());
+            _shapes = shapes1.ToArray();
+        }
+
+        public Operation enqueue_many<T>(long[] indexes, T[] vals, string name = null)
+        {
+            return tf_with(ops.name_scope(name, $"{_name}_EnqueueMany", vals), scope =>
+            {
+                var vals_tensor1 = _check_enqueue_dtypes(indexes); 
+                var vals_tensor2 = _check_enqueue_dtypes(vals);
+
+                var tensors = new List<Tensor>();
+                tensors.AddRange(vals_tensor1);
+                tensors.AddRange(vals_tensor2);
+
+                return gen_data_flow_ops.queue_enqueue_many_v2(_queue_ref, tensors.ToArray(), name: scope);
+            });
+        }
+
+        public Tensor[] dequeue(string name = null)
+        {
+            Tensor[] ret;
+            if (name == null)
+                name = $"{_name}_Dequeue";
+
+            if (_queue_ref.dtype == TF_DataType.TF_RESOURCE)
+                ret = gen_data_flow_ops.queue_dequeue_v2(_queue_ref, _dtypes, name: name);
+            else
+                ret = gen_data_flow_ops.queue_dequeue(_queue_ref, _dtypes, name: name);
+
+            return ret;
+        }
+    }
+}

src/TensorFlowNET.Core/Operations/Queues/QueueBase.cs

@@ -42,7 +42,7 @@ namespace Tensorflow.Queues
             });
         }
 
-        private Tensor[] _check_enqueue_dtypes(object vals)
+        protected Tensor[] _check_enqueue_dtypes(object vals)
         {
             var tensors = new List<Tensor>();
 
@@ -56,12 +56,10 @@ namespace Tensorflow.Queues
                     }
                     break;
 
-                case int[] vals1:
-                    tensors.Add(ops.convert_to_tensor(vals1, dtype: _dtypes[0], name: $"component_0"));
-                    break;
-
                 default:
-                    throw new NotImplementedException("");
+                    var dtype1 = GetType().Name == "PriorityQueue" ? _dtypes[1] : _dtypes[0];
+                    tensors.Add(ops.convert_to_tensor(vals, dtype: dtype1, name: $"component_0"));
+                    break;
             }
 
             return tensors.ToArray();

src/TensorFlowNET.Core/Operations/Queues/RandomShuffleQueue.cs

@@ -0,0 +1,28 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+
+namespace Tensorflow.Queues
+{
+    public class RandomShuffleQueue : QueueBase
+    {
+        public RandomShuffleQueue(int capacity,
+            TF_DataType[] dtypes,
+            TensorShape[] shapes,
+            string[] names = null,
+            string shared_name = null,
+            string name = "randomshuffle_fifo_queue")
+            : base(dtypes: dtypes, shapes: shapes, names: names)
+        {
+            _queue_ref = gen_data_flow_ops.padding_fifo_queue_v2(
+                component_types: dtypes,
+                shapes: shapes,
+                capacity: capacity,
+                shared_name: shared_name,
+                name: name);
+
+            _name = _queue_ref.op.name.Split('/').Last();
+        }
+    }
+}

src/TensorFlowNET.Core/Operations/gen_data_flow_ops.cs

@@ -77,6 +77,22 @@ namespace Tensorflow
             return _op.output;
         }
 
+        public static Tensor priority_queue_v2(TF_DataType[] component_types, TensorShape[] shapes,
+            int capacity = -1, string container = "", string shared_name = "",
+            string name = null)
+        {
+            var _op = _op_def_lib._apply_op_helper("PriorityQueueV2", name, new
+            {
+                component_types,
+                shapes,
+                capacity,
+                container,
+                shared_name
+            });
+
+            return _op.output;
+        }
+
         public static Operation queue_enqueue(Tensor handle, Tensor[] components, int timeout_ms = -1, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("QueueEnqueue", name, new

src/TensorFlowNET.Models/ObjectDetection/Builders/ModelBuilder.cs

@@ -10,11 +10,12 @@ namespace Tensorflow.Models.ObjectDetection
     {
         ImageResizerBuilder _image_resizer_builder;
         FasterRCNNFeatureExtractor _feature_extractor;
-        AnchorGeneratorBuilder anchor_generator_builder;
+        AnchorGeneratorBuilder _anchor_generator_builder;
 
         public ModelBuilder()
         {
             _image_resizer_builder = new ImageResizerBuilder();
+            _anchor_generator_builder = new AnchorGeneratorBuilder();
         }
 
         /// <summary>
@@ -51,7 +52,7 @@ namespace Tensorflow.Models.ObjectDetection
                 inplace_batchnorm_update: frcnn_config.InplaceBatchnormUpdate);
 
             var number_of_stages = frcnn_config.NumberOfStages;
-            var first_stage_anchor_generator = anchor_generator_builder.build(frcnn_config.FirstStageAnchorGenerator);
+            var first_stage_anchor_generator = _anchor_generator_builder.build(frcnn_config.FirstStageAnchorGenerator);
             var first_stage_atrous_rate = frcnn_config.FirstStageAtrousRate;
 
             return new FasterRCNNMetaArch(new FasterRCNNInitArgs

src/TensorFlowNET.Models/ObjectDetection/Models/faster_rcnn_resnet101_voc07.config

@@ -1,143 +1,133 @@
-{
-  "model": {
-    "fasterRcnn": {
-      "numClasses": 20,
-      "imageResizer": {
-        "keepAspectRatioResizer": {
-          "minDimension": 600,
-          "maxDimension": 1024
+# Faster R-CNN with Resnet-101 (v1), configured for Pascal VOC Dataset.
+# Users should configure the fine_tune_checkpoint field in the train config as
+# well as the label_map_path and input_path fields in the train_input_reader and
+# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
+# should be configured.
+
+model {
+  faster_rcnn {
+    num_classes: 20
+    image_resizer {
+      keep_aspect_ratio_resizer {
+        min_dimension: 600
+        max_dimension: 1024
+      }
+    }
+    feature_extractor {
+      type: 'faster_rcnn_resnet101'
+      first_stage_features_stride: 16
+    }
+    first_stage_anchor_generator {
+      grid_anchor_generator {
+        scales: [0.25, 0.5, 1.0, 2.0]
+        aspect_ratios: [0.5, 1.0, 2.0]
+        height_stride: 16
+        width_stride: 16
+      }
+    }
+    first_stage_box_predictor_conv_hyperparams {
+      op: CONV
+      regularizer {
+        l2_regularizer {
+          weight: 0.0
         }
-      },
-      "featureExtractor": {
-        "type": "faster_rcnn_resnet101",
-        "firstStageFeaturesStride": 16
-      },
-      "firstStageAnchorGenerator": {
-        "gridAnchorGenerator": {
-          "heightStride": 16,
-          "widthStride": 16,
-          "scales": [
-            0.25,
-            0.5,
-            1.0,
-            2.0
-          ],
-          "aspectRatios": [
-            0.5,
-            1.0,
-            2.0
-          ]
+      }
+      initializer {
+        truncated_normal_initializer {
+          stddev: 0.01
         }
-      },
-      "firstStageBoxPredictorConvHyperparams": {
-        "op": "CONV",
-        "regularizer": {
-          "l2Regularizer": {
-            "weight": 0.0
-          }
-        },
-        "initializer": {
-          "truncatedNormalInitializer": {
-            "stddev": 0.009999999776482582
+      }
+    }
+    first_stage_nms_score_threshold: 0.0
+    first_stage_nms_iou_threshold: 0.7
+    first_stage_max_proposals: 300
+    first_stage_localization_loss_weight: 2.0
+    first_stage_objectness_loss_weight: 1.0
+    initial_crop_size: 14
+    maxpool_kernel_size: 2
+    maxpool_stride: 2
+    second_stage_box_predictor {
+      mask_rcnn_box_predictor {
+        use_dropout: false
+        dropout_keep_probability: 1.0
+        fc_hyperparams {
+          op: FC
+          regularizer {
+            l2_regularizer {
+              weight: 0.0
+            }
           }
-        }
-      },
-      "firstStageNmsScoreThreshold": 0.0,
-      "firstStageNmsIouThreshold": 0.699999988079071,
-      "firstStageMaxProposals": 300,
-      "firstStageLocalizationLossWeight": 2.0,
-      "firstStageObjectnessLossWeight": 1.0,
-      "initialCropSize": 14,
-      "maxpoolKernelSize": 2,
-      "maxpoolStride": 2,
-      "secondStageBoxPredictor": {
-        "maskRcnnBoxPredictor": {
-          "fcHyperparams": {
-            "op": "FC",
-            "regularizer": {
-              "l2Regularizer": {
-                "weight": 0.0
-              }
-            },
-            "initializer": {
-              "varianceScalingInitializer": {
-                "factor": 1.0,
-                "uniform": true,
-                "mode": "FAN_AVG"
-              }
+          initializer {
+            variance_scaling_initializer {
+              factor: 1.0
+              uniform: true
+              mode: FAN_AVG
             }
-          },
-          "useDropout": false,
-          "dropoutKeepProbability": 1.0
+          }
         }
-      },
-      "secondStagePostProcessing": {
-        "batchNonMaxSuppression": {
-          "scoreThreshold": 0.0,
-          "iouThreshold": 0.6000000238418579,
-          "maxDetectionsPerClass": 100,
-          "maxTotalDetections": 300
-        },
-        "scoreConverter": "SOFTMAX"
-      },
-      "secondStageLocalizationLossWeight": 2.0,
-      "secondStageClassificationLossWeight": 1.0
+      }
     }
-  },
-  "trainConfig": {
-    "batchSize": 1,
-    "dataAugmentationOptions": [
-      {
-        "randomHorizontalFlip": {}
+    second_stage_post_processing {
+      batch_non_max_suppression {
+        score_threshold: 0.0
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 300
       }
-    ],
-    "optimizer": {
-      "momentumOptimizer": {
-        "learningRate": {
-          "manualStepLearningRate": {
-            "initialLearningRate": 9.999999747378752e-05,
-            "schedule": [
-              {
-                "step": 500000,
-                "learningRate": 9.999999747378752e-06
-              },
-              {
-                "step": 700000,
-                "learningRate": 9.999999974752427e-07
-              }
-            ]
-          }
-        },
-        "momentumOptimizerValue": 0.8999999761581421
-      },
-      "useMovingAverage": false
-    },
-    "gradientClippingByNorm": 10.0,
-    "fineTuneCheckpoint": "D:/tmp/faster_rcnn_resnet101_coco/model.ckpt",
-    "fromDetectionCheckpoint": true,
-    "numSteps": 800000
-  },
-  "trainInputReader": {
-    "labelMapPath": "D:/Projects/PythonLab/tf-models/research/object_detection/data/pascal_label_map.pbtxt",
-    "tfRecordInputReader": {
-      "inputPath": [
-        "D:/Projects/PythonLab/tf-models/research/object_detection/data/pascal_train.record"
-      ]
+      score_converter: SOFTMAX
     }
-  },
-  "evalConfig": {
-    "numExamples": 4952
-  },
-  "evalInputReader": [
-    {
-      "labelMapPath": "D:/Projects/PythonLab/tf-models/research/object_detection/data/pascal_label_map.pbtxt",
-      "shuffle": false,
-      "numReaders": 1,
-      "tfRecordInputReader": {
-        "inputPath": [
-          "D:/Projects/PythonLab/tf-models/research/object_detection/data/pascal_val.record"
-        ]
+    second_stage_localization_loss_weight: 2.0
+    second_stage_classification_loss_weight: 1.0
+  }
+}
+
+train_config: {
+  batch_size: 1
+  optimizer {
+    momentum_optimizer: {
+      learning_rate: {
+        manual_step_learning_rate {
+          initial_learning_rate: 0.0001
+          schedule {
+            step: 500000
+            learning_rate: .00001
+          }
+          schedule {
+            step: 700000
+            learning_rate: .000001
+          }
+        }
       }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  gradient_clipping_by_norm: 10.0
+  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
+  from_detection_checkpoint: true
+  num_steps: 800000
+  data_augmentation_options {
+    random_horizontal_flip {
     }
-  ]
-}
+  }
+}
+
+train_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pascal_train.record"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pascal_label_map.pbtxt"
+}
+
+eval_config: {
+  num_examples: 4952
+}
+
+eval_input_reader: {
+  tf_record_input_reader {
+    input_path: "PATH_TO_BE_CONFIGURED/pascal_val.record"
+  }
+  label_map_path: "PATH_TO_BE_CONFIGURED/pascal_label_map.pbtxt"
+  shuffle: false
+  num_readers: 1
+}

src/TensorFlowNET.Models/ObjectDetection/Utils/ConfigUtil.cs

@@ -1,4 +1,5 @@
-using System;
+using Protobuf.Text;
+using System;
 using System.Collections.Generic;
 using System.IO;
 using System.Text;
@@ -10,8 +11,8 @@ namespace Tensorflow.Models.ObjectDetection.Utils
     {
         public static TrainEvalPipelineConfig get_configs_from_pipeline_file(string pipeline_config_path)
         {
-            var json = File.ReadAllText(pipeline_config_path);
-            var pipeline_config = TrainEvalPipelineConfig.Parser.ParseJson(json);
+            var config = File.ReadAllText(pipeline_config_path);
+            var pipeline_config = TrainEvalPipelineConfig.Parser.ParseText(config);
 
             return pipeline_config;
         }

src/TensorFlowNET.Models/TensorFlowNET.Models.csproj

@@ -4,6 +4,16 @@
     <TargetFramework>netcoreapp2.2</TargetFramework>
     <AssemblyName>TensorFlow.Models</AssemblyName>
     <RootNamespace>Tensorflow.Models</RootNamespace>
+    <Version>0.0.1</Version>
+    <Authors>Haiping Chen</Authors>
+    <Company>SciSharp STACK</Company>
+    <PackageProjectUrl>https://github.com/SciSharp/TensorFlow.NET</PackageProjectUrl>
+    <RepositoryUrl>https://github.com/SciSharp/TensorFlow.NET</RepositoryUrl>
+    <RepositoryType>git</RepositoryType>
+    <PackageTags>TensorFlow</PackageTags>
+    <Description>Models and examples built with TensorFlow.</Description>
+    <GeneratePackageOnBuild>true</GeneratePackageOnBuild>
+    <Copyright>Apache2</Copyright>
   </PropertyGroup>
 
   <ItemGroup>
@@ -16,6 +26,10 @@
     </Content>
   </ItemGroup>
 
+  <ItemGroup>
+    <PackageReference Include="Protobuf.Text" Version="0.3.1" />
+  </ItemGroup>
+
   <ItemGroup>
     <ProjectReference Include="..\TensorFlowNET.Core\TensorFlowNET.Core.csproj" />
   </ItemGroup>

test/TensorFlowNET.UnitTest/QueueTest.cs

@@ -70,5 +70,27 @@ namespace TensorFlowNET.UnitTest
                 // until queue has more element.
             }
         }
+
+        [TestMethod]
+        public void PriorityQueue()
+        {
+            var queue = tf.PriorityQueue(3, tf.@string);
+            var init = queue.enqueue_many(new[] { 2L, 4L, 3L }, new[] { "p1", "p2", "p3" });
+            var x = queue.dequeue();
+
+            using (var sess = tf.Session())
+            {
+                init.run();
+
+                var result = sess.run(x);
+                Assert.AreEqual(result[0].GetInt64(), 2L);
+
+                result = sess.run(x);
+                Assert.AreEqual(result[0].GetInt64(), 3L);
+
+                result = sess.run(x);
+                Assert.AreEqual(result[0].GetInt64(), 4L);
+            }
+        }
     }
 }