Merge branch 'master' of https://github.com/NiklasGustafsson/TensorFlow.NET

src/TensorFlowNET.Core/Keras/ArgsDefinition/Pooling/Pooling1DArgs.cs

@@ -0,0 +1,34 @@
+namespace Tensorflow.Keras.ArgsDefinition
+{
+    public class Pooling1DArgs : LayerArgs
+    {
+        /// <summary>
+        /// The pooling function to apply, e.g. `tf.nn.max_pool2d`.
+        /// </summary>
+        public IPoolFunction PoolFunction { get; set; }
+
+        /// <summary>
+        /// specifying the size of the pooling window.
+        /// </summary>
+        public int PoolSize { get; set; }
+
+        /// <summary>
+        /// specifying the strides of the pooling operation.
+        /// </summary>
+        public int Strides { 
+            get { return _strides.HasValue ? _strides.Value : PoolSize; }
+            set { _strides = value; } 
+        }
+        private int? _strides = null;
+
+        /// <summary>
+        /// The padding method, either 'valid' or 'same'.
+        /// </summary>
+        public string Padding { get; set; } = "valid";
+
+        /// <summary>
+        /// one of `channels_last` (default) or `channels_first`.
+        /// </summary>
+        public string DataFormat { get; set; }
+    }
+}

src/TensorFlowNET.Keras/Layers/LayersApi.cs

@@ -325,6 +325,16 @@ namespace Tensorflow.Keras.Layers
             return input_layer.InboundNodes[0].Outputs;
         }
 
+        public MaxPooling1D MaxPooling1D(int? pool_size = null,
+            int? strides = null,
+            string padding = "valid")
+            => new MaxPooling1D(new Pooling1DArgs
+            {
+                PoolSize = pool_size ?? 2,
+                Strides = strides ?? (pool_size ?? 2),
+                Padding = padding
+            });
+
         public MaxPooling2D MaxPooling2D(TensorShape pool_size = null,
             TensorShape strides = null,
             string padding = "valid")
@@ -448,6 +458,20 @@ namespace Tensorflow.Keras.Layers
         public GlobalAveragePooling2D GlobalAveragePooling2D()
             => new GlobalAveragePooling2D(new Pooling2DArgs { });
 
+        public GlobalAveragePooling1D GlobalAveragePooling1D(string data_format = "channels_last")
+            => new GlobalAveragePooling1D(new Pooling1DArgs { DataFormat = data_format });
+
+        public GlobalAveragePooling2D GlobalAveragePooling2D(string data_format = "channels_last")
+            => new GlobalAveragePooling2D(new Pooling2DArgs { DataFormat = data_format });
+
+        public GlobalMaxPooling1D GlobalMaxPooling1D(string data_format = "channels_last")
+            => new GlobalMaxPooling1D(new Pooling1DArgs { DataFormat = data_format });
+
+        public GlobalMaxPooling2D GlobalMaxPooling2D(string data_format = "channels_last")
+            => new GlobalMaxPooling2D(new Pooling2DArgs { DataFormat = data_format });
+
+
+
         Activation GetActivationByName(string name)
             => name switch
             {

src/TensorFlowNET.Keras/Layers/Pooling/GlobalAveragePooling1D.cs

@@ -0,0 +1,23 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Keras.ArgsDefinition;
+
+namespace Tensorflow.Keras.Layers
+{
+    public class GlobalAveragePooling1D : GlobalPooling1D
+    {
+        public GlobalAveragePooling1D(Pooling1DArgs args)
+            : base(args)
+        {
+        }
+
+        protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null)
+        {
+            if (data_format == "channels_last")
+                return math_ops.reduce_mean(inputs, new int[] { 1 }, false);
+            else
+                return math_ops.reduce_mean(inputs, new int[] { 2 }, false);
+        }
+    }
+}

src/TensorFlowNET.Keras/Layers/Pooling/GlobalMaxPooling1D.cs

@@ -0,0 +1,23 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Keras.ArgsDefinition;
+
+namespace Tensorflow.Keras.Layers
+{
+    public class GlobalMaxPooling1D : GlobalPooling1D
+    {
+        public GlobalMaxPooling1D(Pooling1DArgs args)
+            : base(args)
+        {
+        }
+
+        protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null)
+        {
+            if (data_format == "channels_last")
+                return math_ops.reduce_max(inputs, new int[] { 1 }, false);
+            else
+                return math_ops.reduce_max(inputs, new int[] { 2 }, false);
+        }
+    }
+}

src/TensorFlowNET.Keras/Layers/Pooling/GlobalMaxPooling2D.cs

@@ -0,0 +1,23 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Keras.ArgsDefinition;
+
+namespace Tensorflow.Keras.Layers
+{
+    public class GlobalMaxPooling2D : GlobalPooling2D
+    {
+        public GlobalMaxPooling2D(Pooling2DArgs args)
+            : base(args)
+        {
+        }
+
+        protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null)
+        {
+            if (data_format == "channels_last")
+                return math_ops.reduce_max(inputs, new int[] { 1, 2 }, false);
+            else
+                return math_ops.reduce_max(inputs, new int[] { 2, 3 }, false);
+        }
+    }
+}

src/TensorFlowNET.Keras/Layers/Pooling/GlobalPooling1D.cs

@@ -0,0 +1,23 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Keras.ArgsDefinition;
+using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Utils;
+
+namespace Tensorflow.Keras.Layers
+{
+    public abstract class GlobalPooling1D : Layer
+    {
+        Pooling1DArgs args;
+        protected string data_format => args.DataFormat;
+        protected InputSpec input_spec;
+
+        public GlobalPooling1D(Pooling1DArgs args) : base(args)
+        {
+            this.args = args;
+            args.DataFormat = conv_utils.normalize_data_format(data_format);
+            input_spec = new InputSpec(ndim: 3);
+        }
+    }
+}

src/TensorFlowNET.Keras/Layers/Pooling/MaxPooling1D.cs

@@ -0,0 +1,14 @@
+using Tensorflow.Keras.ArgsDefinition;
+using Tensorflow.Operations;
+
+namespace Tensorflow.Keras.Layers
+{
+    public class MaxPooling1D : Pooling1D
+    {
+        public MaxPooling1D(Pooling1DArgs args)
+            : base(args)
+        {
+            args.PoolFunction = new MaxPoolFunction();
+        }
+    }
+}

src/TensorFlowNET.Keras/Layers/Pooling/Pooling1D.cs

@@ -0,0 +1,62 @@
+/*****************************************************************************
+   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+******************************************************************************/
+
+using Tensorflow.Keras.ArgsDefinition;
+using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Utils;
+
+namespace Tensorflow.Keras.Layers
+{
+    public class Pooling1D : Layer
+    {
+        Pooling1DArgs args;
+        InputSpec input_spec;
+
+        public Pooling1D(Pooling1DArgs args)
+            : base(args)
+        {
+            this.args = args;
+            args.Padding = conv_utils.normalize_padding(args.Padding);
+            args.DataFormat = conv_utils.normalize_data_format(args.DataFormat);
+            input_spec = new InputSpec(ndim: 3);
+        }
+
+        protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null)
+        {
+            int[] pool_shape;
+            int[] strides;
+            if (args.DataFormat == "channels_last")
+            {
+                pool_shape = new int[] { 1, args.PoolSize, 1 };
+                strides = new int[] { 1, args.Strides, 1 };
+            }
+            else
+            {
+                pool_shape = new int[] { 1, 1, args.PoolSize };
+                strides = new int[] { 1, 1, args.Strides };
+            }
+
+            var outputs = args.PoolFunction.Apply(
+                inputs,
+                ksize: pool_shape,
+                strides: strides,
+                padding: args.Padding.ToUpper(),
+                data_format: conv_utils.convert_data_format(args.DataFormat, 3));
+
+            return outputs;
+        }
+    }
+}

test/TensorFlowNET.Keras.UnitTest/Layers/PoolingTest.cs

@@ -0,0 +1,305 @@
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+using NumSharp;
+using System.Linq;
+using Tensorflow;
+using static Tensorflow.Binding;
+using static Tensorflow.KerasApi;
+
+namespace TensorFlowNET.Keras.UnitTest
+{
+    /// <summary>
+    /// https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/layers
+    /// </summary>
+    [TestClass]
+    public class PoolingTest : EagerModeTestBase
+    {
+        private NDArray input_array_1D = np.array(new float[,,]
+            {
+                {{1,2,3,3,3},{1,2,3,3,3},{1,2,3,3,3}},
+                {{4,5,6,3,3},{4,5,6,3,3},{4,5,6,3,3}},
+                {{7,8,9,3,3},{7,8,9,3,3},{7,8,9,3,3}},
+                {{7,8,9,3,3},{7,8,9,3,3},{7,8,9,3,3}}
+            });
+
+        private NDArray input_array_2D = np.array(new float[,,,]
+            {{
+                {{1,2,3,3,3},{1,2,3,3,3},{1,2,3,3,3}},
+                {{4,5,6,3,3},{4,5,6,3,3},{4,5,6,3,3}},
+                },{
+                {{7,8,9,3,3},{7,8,9,3,3},{7,8,9,3,3}},
+                {{7,8,9,3,3},{7,8,9,3,3},{7,8,9,3,3}}
+                },{
+                {{1,2,3,3,3},{1,2,3,3,3},{1,2,3,3,3}},
+                {{4,5,6,3,3},{4,5,6,3,3},{4,5,6,3,3}},
+                },{
+                {{7,8,9,3,3},{7,8,9,3,3},{7,8,9,3,3}},
+                {{7,8,9,3,3},{7,8,9,3,3},{7,8,9,3,3}}
+            }});
+
+        [TestMethod]
+        public void GlobalAverage1DPoolingChannelsLast()
+        {
+            var pool = keras.layers.GlobalAveragePooling1D();
+            var y = pool.Apply(input_array_1D);
+
+            Assert.AreEqual(4, y.shape[0]);
+            Assert.AreEqual(5, y.shape[1]);
+
+            var expected = np.array(new float[,]
+            {
+                {1,2,3,3,3},
+                {4,5,6,3,3},
+                {7,8,9,3,3},
+                {7,8,9,3,3}
+            });
+
+            Assert.AreEqual(expected, y[0].numpy());
+        }
+
+        [TestMethod]
+        public void GlobalAverage1DPoolingChannelsFirst()
+        {
+            var pool = keras.layers.GlobalAveragePooling1D(data_format: "channels_first");
+            var y = pool.Apply(input_array_1D);
+
+            Assert.AreEqual(4, y.shape[0]);
+            Assert.AreEqual(3, y.shape[1]);
+
+            var expected = np.array(new float[,]
+            {
+                {2.4f, 2.4f, 2.4f},
+                {4.2f, 4.2f, 4.2f},
+                {6.0f, 6.0f, 6.0f},
+                {6.0f, 6.0f, 6.0f}
+            });
+
+            Assert.AreEqual(expected, y[0].numpy());
+        }
+
+        [TestMethod]
+        public void GlobalAverage2DPoolingChannelsLast()
+        {
+            var pool = keras.layers.GlobalAveragePooling2D();
+            var y = pool.Apply(input_array_2D);
+
+            Assert.AreEqual(4, y.shape[0]);
+            Assert.AreEqual(5, y.shape[1]);
+
+            var expected = np.array(new float[,]
+            {
+                {2.5f, 3.5f, 4.5f, 3.0f, 3.0f},
+                {7.0f, 8.0f, 9.0f, 3.0f, 3.0f},
+                {2.5f, 3.5f, 4.5f, 3.0f, 3.0f},
+                {7.0f, 8.0f, 9.0f, 3.0f, 3.0f}
+            });
+
+            Assert.AreEqual(expected, y[0].numpy());
+        }
+
+        [TestMethod]
+        public void GlobalAverage2DPoolingChannelsFirst()
+        {
+            var pool = keras.layers.GlobalAveragePooling2D(data_format: "channels_first");
+            var y = pool.Apply(input_array_2D);
+
+            Assert.AreEqual(4, y.shape[0]);
+            Assert.AreEqual(2, y.shape[1]);
+
+            var expected = np.array(new float[,]
+            {
+                {2.4f, 4.2f},
+                {6.0f, 6.0f},
+                {2.4f, 4.2f},
+                {6.0f, 6.0f}
+            });
+
+            Assert.AreEqual(expected, y[0].numpy());
+        }
+
+        [TestMethod]
+        public void GlobalMax1DPoolingChannelsLast()
+        {
+            var pool = keras.layers.GlobalMaxPooling1D();
+            var y = pool.Apply(input_array_1D);
+
+            Assert.AreEqual(4, y.shape[0]);
+            Assert.AreEqual(5, y.shape[1]);
+
+            var expected = np.array(new float[,]
+            {
+                {1,2,3,3,3},
+                {4,5,6,3,3},
+                {7,8,9,3,3},
+                {7,8,9,3,3}
+            });
+
+            Assert.AreEqual(expected, y[0].numpy());
+        }
+
+        [TestMethod]
+        public void GlobalMax1DPoolingChannelsFirst()
+        {
+            var pool = keras.layers.GlobalMaxPooling1D(data_format: "channels_first");
+            var y = pool.Apply(input_array_1D);
+
+            Assert.AreEqual(4, y.shape[0]);
+            Assert.AreEqual(3, y.shape[1]);
+
+            var expected = np.array(new float[,]
+            {
+                {3.0f, 3.0f, 3.0f},
+                {6.0f, 6.0f, 6.0f},
+                {9.0f, 9.0f, 9.0f},
+                {9.0f, 9.0f, 9.0f}
+            });
+
+            Assert.AreEqual(expected, y[0].numpy());
+        }
+
+        [TestMethod]
+        public void GlobalMax2DPoolingChannelsLast()
+        {
+            var input_array_2D = np.array(new float[,,,]
+            {{
+                {{1,2,3,3,3},{1,2,3,3,3},{1,2,3,9,3}},
+                {{4,5,6,3,3},{4,5,6,3,3},{4,5,6,3,3}},
+                },{
+                {{7,8,9,3,3},{7,8,9,3,3},{7,8,9,3,3}},
+                {{7,8,9,3,3},{7,8,9,3,3},{7,8,9,3,3}}
+                },{
+                {{1,2,3,3,3},{1,2,3,3,3},{1,2,3,3,9}},
+                {{4,5,6,3,3},{4,5,6,3,3},{4,5,6,3,3}},
+                },{
+                {{7,8,9,3,3},{7,8,9,3,3},{7,8,9,3,3}},
+                {{7,8,9,3,3},{7,8,9,3,3},{7,8,9,3,3}}
+            }});
+
+            var pool = keras.layers.GlobalMaxPooling2D();
+            var y = pool.Apply(input_array_2D);
+
+            Assert.AreEqual(4, y.shape[0]);
+            Assert.AreEqual(5, y.shape[1]);
+
+            var expected = np.array(new float[,]
+            {
+                {4.0f, 5.0f, 6.0f, 9.0f, 3.0f},
+                {7.0f, 8.0f, 9.0f, 3.0f, 3.0f},
+                {4.0f, 5.0f, 6.0f, 3.0f, 9.0f},
+                {7.0f, 8.0f, 9.0f, 3.0f, 3.0f}
+            });
+
+            Assert.AreEqual(expected, y[0].numpy());
+        }
+
+        [TestMethod]
+        public void GlobalMax2DPoolingChannelsFirst()
+        {
+            var input_array_2D = np.array(new float[,,,]
+            {{
+                {{1,2,3,3,3},{1,2,3,3,3},{1,2,3,9,3}},
+                {{4,5,6,3,3},{4,5,6,3,3},{4,5,6,3,3}},
+                },{
+                {{7,8,9,3,3},{7,8,9,3,3},{7,8,9,3,3}},
+                {{7,8,9,3,3},{7,8,9,3,3},{7,8,9,3,3}}
+                },{
+                {{1,2,3,3,3},{1,2,3,3,3},{1,2,3,3,9}},
+                {{4,5,6,3,3},{4,5,6,3,3},{4,5,6,3,3}},
+                },{
+                {{7,8,9,3,3},{7,8,9,3,3},{7,8,9,3,3}},
+                {{7,8,9,3,3},{7,8,9,3,3},{7,8,9,3,3}}
+            }});
+
+            var pool = keras.layers.GlobalMaxPooling2D(data_format: "channels_first");
+            var y = pool.Apply(input_array_2D);
+
+            Assert.AreEqual(4, y.shape[0]);
+            Assert.AreEqual(2, y.shape[1]);
+
+            var expected = np.array(new float[,]
+            {
+                {9.0f, 6.0f},
+                {9.0f, 9.0f},
+                {9.0f, 6.0f},
+                {9.0f, 9.0f}
+            });
+
+            Assert.AreEqual(expected, y[0].numpy());
+        }
+
+        [TestMethod, Ignore("There's an error generated from TF complaining about the shape of the pool. Needs further investigation.")]
+        public void Max1DPoolingChannelsLast()
+        {
+            var x = input_array_1D;
+            var pool = keras.layers.MaxPooling1D(pool_size:2, strides:1);
+            var y = pool.Apply(x);
+
+            Assert.AreEqual(4, y.shape[0]);
+            Assert.AreEqual(2, y.shape[1]);
+            Assert.AreEqual(5, y.shape[2]);
+
+            var expected = np.array(new float[,,]
+            {
+                {{2.0f, 2.0f, 3.0f, 3.0f, 3.0f},
+                 { 1.0f, 2.0f, 3.0f, 3.0f, 3.0f}},
+
+                {{4.0f, 5.0f, 6.0f, 3.0f, 3.0f},
+                 {4.0f, 5.0f, 6.0f, 3.0f, 3.0f}},
+
+                {{7.0f, 8.0f, 9.0f, 3.0f, 3.0f},
+                 {7.0f, 8.0f, 9.0f, 3.0f, 3.0f}},
+
+                {{7.0f, 8.0f, 9.0f, 3.0f, 3.0f},
+                 {7.0f, 8.0f, 9.0f, 3.0f, 3.0f}}
+            });
+
+            Assert.AreEqual(expected, y[0].numpy());
+        }
+
+        [TestMethod]
+        public void Max2DPoolingChannelsLast()
+        {
+            var x = np.array(new float[,,,]
+            {{
+                {{1,2,3,3,3},{1,2,3,3,3},{1,2,3,9,3}},
+                {{4,5,6,3,3},{4,5,6,3,3},{4,5,6,3,3}},
+                },{
+                {{7,8,9,3,3},{7,8,9,3,3},{7,8,9,3,3}},
+                {{7,8,9,3,3},{7,8,9,3,3},{7,8,9,3,3}}
+                },{
+                {{1,2,3,3,3},{1,2,3,3,3},{1,2,3,3,9}},
+                {{4,5,6,3,3},{4,5,6,3,3},{4,5,6,3,3}},
+                },{
+                {{7,8,9,3,3},{7,8,9,3,3},{7,8,9,3,3}},
+                {{7,8,9,3,3},{7,8,9,3,3},{7,8,9,3,3}}
+            }});
+
+            var pool = keras.layers.MaxPooling2D(pool_size: 2, strides: 1);
+            var y = pool.Apply(x);
+
+            Assert.AreEqual(4, y.shape[0]);
+            Assert.AreEqual(1, y.shape[1]);
+            Assert.AreEqual(2, y.shape[2]);
+            Assert.AreEqual(5, y.shape[3]);
+
+            var expected = np.array(new float[,,,]
+            {
+                {{{4.0f, 5.0f, 6.0f, 3.0f, 3.0f},
+                 {4.0f, 5.0f, 6.0f, 9.0f, 3.0f}}},
+
+
+               {{{7.0f, 8.0f, 9.0f, 3.0f, 3.0f},
+                 {7.0f, 8.0f, 9.0f, 3.0f, 3.0f}}},
+
+
+               {{{4.0f, 5.0f, 6.0f, 3.0f, 3.0f},
+                 {4.0f, 5.0f, 6.0f, 3.0f, 9.0f}}},
+
+
+               {{{7.0f, 8.0f, 9.0f, 3.0f, 3.0f},
+                 {7.0f, 8.0f, 9.0f, 3.0f, 3.0f}}}
+            });
+
+            Assert.AreEqual(expected, y[0].numpy());
+        }
+    }
+}