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tf.PriorityQueue, Protobuf.TextFormat · MSavameri/TensorFlow.NET@a596dbe · GitHub

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tf.PriorityQueue, Protobuf.TextFormat
1 parent 7a828c0 commit a596dbe

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Lines changed: 328 additions & 146 deletions

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‎docs/source/Queue.md‎

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@@ -62,6 +62,33 @@ A FIFOQueue that supports batching variable-sized tensors by padding. A `Padding
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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.
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```csharp
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[TestMethod]
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public void PriorityQueue()
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{
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var queue = tf.PriorityQueue(3, tf.@string);
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var init = queue.enqueue_many(new[] { 2L, 4L, 3L }, new[] { "p1", "p2", "p3" });
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var x = queue.dequeue();
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using (var sess = tf.Session())
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{
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init.run();
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// output will 2, 3, 4
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var result = sess.run(x);
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Assert.AreEqual(result[0].GetInt64(), 2L);
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result = sess.run(x);
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Assert.AreEqual(result[0].GetInt64(), 3L);
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result = sess.run(x);
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Assert.AreEqual(result[0].GetInt64(), 4L);
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}
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}
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```
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#### RandomShuffleQueue
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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

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

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

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

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

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

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

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

‎src/TensorFlowNET.Core/Operations/gen_data_flow_ops.cs‎

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@@ -77,6 +77,22 @@ public static Tensor fifo_queue_v2(TF_DataType[] component_types, TensorShape[]
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return _op.output;
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}
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public static Tensor priority_queue_v2(TF_DataType[] component_types, TensorShape[] shapes,
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int capacity = -1, string container = "", string shared_name = "",
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string name = null)
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{
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var _op = _op_def_lib._apply_op_helper("PriorityQueueV2", name, new
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{
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component_types,
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shapes,
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capacity,
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container,
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shared_name
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});
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return _op.output;
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}
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public static Operation queue_enqueue(Tensor handle, Tensor[] components, int timeout_ms = -1, string name = null)
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{
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var _op = _op_def_lib._apply_op_helper("QueueEnqueue", name, new

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

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

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