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improve raggedtensor · feelsyt/TensorFlow.NET@02bfb9a · GitHub

/ TensorFlow.NET Public
forked from SciSharp/TensorFlow.NET

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improve raggedtensor
1 parent eb4c1f4 commit 02bfb9a

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Lines changed: 127 additions & 0 deletions

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‎src/TensorFlowNET.Core/Operations/array_ops.cs‎

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Original file line numberDiff line numberDiff line change
@@ -1139,5 +1139,18 @@ public static Tensor placeholder(TF_DataType dtype, Shape shape = null, string n
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var _op = tf.OpDefLib._apply_op_helper("Placeholder", name: name, args: new { dtype, shape });
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return _op.output;
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}
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public static int get_positive_axis(int axis, int ndims=-100, string axis_name="axis", string ndims_name= "ndims")
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{
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if(ndims != -100)
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{
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if (axis >= 0 && axis < ndims) return axis;
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else if (-ndims <= axis && axis < 0) return axis + ndims;
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else throw new ValueError($"{axis_name}={axis} out of bounds:expected {-ndims}<={axis_name}<{ndims}");
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} else if(axis < 0) throw new ValueError($"{axis_name}={axis} may only be negative if {ndims_name} is statically known.");
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return axis;
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}
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}
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}

‎src/TensorFlowNET.Core/Tensors/Ragged/RaggedTensor.cs‎

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@@ -163,5 +163,38 @@ public static implicit operator RaggedTensor(Tensor tensor)
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{
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return tensor.Tag as RaggedTensor;
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}
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public Tensor nrows(TF_DataType out_type, string name = null)
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{
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tf_with(ops.name_scope(name, "RaggedNRows"), scope =>
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{
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return math_ops.cast(this._row_partition.nrows(), dtype: out_type);
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});
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return null;
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}
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public RaggedTensor row_lengths(int axis=-1, string name=null)
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{
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if (axis == 0) return this._row_partition.nrows();
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if (axis == 1) return this._row_partition.row_lengths();
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var values = (RaggedTensor)this._values;
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axis = array_ops.get_positive_axis(
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axis, this.shape.rank, ndims_name: "rank(this)");
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if (axis == 0) return this.nrows(this._row_partition.GetDataType());
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else if (axis == 1)
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{
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var splits = this._row_partition.row_splits;
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return splits[new Slice(start: 1)] - splits[new Slice(stop: -1)];
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}
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else if (this._values is RaggedTensor)
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{
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return values.row_lengths(axis - 1);
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}
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else
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{
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var shape = array_ops.shape(values, out_type: this._row_partition.GetDataType());
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return array_ops.ones(shape[new Slice(stop:axis - 1)], this._row_partition.GetDataType()) *
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shape[axis - 1];
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}
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}
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}
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}

‎src/TensorFlowNET.Core/Tensors/Ragged/RowPartition.cs‎

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Original file line numberDiff line numberDiff line change
@@ -14,10 +14,15 @@ You may obtain a copy of the License at
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limitations under the License.
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******************************************************************************/
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using Serilog.Debugging;
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using System;
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using System.Collections.Concurrent;
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using System.Collections.Generic;
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//using System.ComponentModel.DataAnnotations;
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using System.Text;
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using System.Xml.Linq;
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using Tensorflow.Framework;
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using Tensorflow.NumPy;
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using static Tensorflow.Binding;
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namespace Tensorflow
@@ -99,5 +104,55 @@ public static RowPartition from_row_splits(Tensor row_splits,
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return new RowPartition(row_splits);
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});
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}
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public static RowPartition from_row_lengths(Tensor row_lengths,
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bool validate=true,
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TF_DataType dtype = TF_DataType.TF_INT32,
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TF_DataType dtype_hint= TF_DataType.TF_INT32)
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{
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row_lengths = _convert_row_partition(
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row_lengths, "row_lengths", dtype_hint: dtype_hint, dtype: dtype);
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Tensor row_limits = math_ops.cumsum<Tensor>(row_lengths, tf.constant(-1));
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Tensor row_splits = array_ops.concat(new Tensor[] { tf.convert_to_tensor(np.array(new int[] { 0 }, TF_DataType.TF_INT64)), row_limits }, axis:0);
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return new RowPartition(row_splits: row_splits, row_lengths: row_lengths);
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}
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public static Tensor _convert_row_partition(Tensor partition, string name, TF_DataType dtype,
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TF_DataType dtype_hint= TF_DataType.TF_INT64)
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{
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if (partition is NDArray && partition.GetDataType() == np.int32) partition = ops.convert_to_tensor(partition, name: name);
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if (partition.GetDataType() != np.int32 && partition.GetDataType() != np.int64) throw new ValueError($"{name} must have dtype int32 or int64");
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return partition;
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}
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public Tensor nrows()
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{
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/*Returns the number of rows created by this `RowPartition*/
131+
if (this._nrows != null) return this._nrows;
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var nsplits = tensor_shape.dimension_at_index(this._row_splits.shape, 0);
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if (nsplits == null) return array_ops.shape(this._row_splits, out_type: this.row_splits.dtype)[0] - 1;
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else return constant_op.constant(nsplits.value - 1, dtype: this.row_splits.dtype);
135+
}
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public Tensor row_lengths()
138+
{
139+
140+
if (this._row_splits != null)
141+
{
142+
int nrows_plus_one = tensor_shape.dimension_value(this._row_splits.shape[0]);
143+
return tf.constant(nrows_plus_one - 1);
144+
145+
}
146+
if (this._row_lengths != null)
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{
148+
var nrows = tensor_shape.dimension_value(this._row_lengths.shape[0]);
149+
return tf.constant(nrows);
150+
}
151+
if(this._nrows != null)
152+
{
153+
return tensor_util.constant_value(this._nrows);
154+
}
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return tf.constant(-1);
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}
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}
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}

‎test/TensorFlowNET.UnitTest/ManagedAPI/RaggedTensorTest.cs‎

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,26 @@
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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 System.Threading.Tasks;
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using Microsoft.VisualStudio.TestTools.UnitTesting;
7+
using Tensorflow;
8+
using Tensorflow.NumPy;
9+
using static Tensorflow.Binding;
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11+
namespace TensorFlowNET.UnitTest.ManagedAPI
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{
13+
public class RaggedTensorTest :EagerModeTestBase
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{
15+
[TestMethod]
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public void Test_from_row_lengths()
17+
{
18+
var row_lengths = tf.convert_to_tensor(np.array(new int[] { 2, 0, 3, 1, 1 }, TF_DataType.TF_INT64));
19+
var rp = RowPartition.from_row_lengths(row_lengths, validate: false);
20+
var rp_row_lengths = rp.row_lengths();
21+
var rp_nrows = rp.nrows();
22+
Assert.IsTrue(rp_nrows.ToArray<long>()[0] == rp.nrows().ToArray<long>()[0]);
23+
24+
}
25+
}
26+
}

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