diff --git a/data/img001.bmp b/data/img001.bmp
new file mode 100644
index 00000000..d149d76f
Binary files /dev/null and b/data/img001.bmp differ
diff --git a/src/TensorFlowNET.Core/APIs/tf.image.cs b/src/TensorFlowNET.Core/APIs/tf.image.cs
index ac9cbc60..41ef5296 100644
--- a/src/TensorFlowNET.Core/APIs/tf.image.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.image.cs
@@ -339,6 +339,13 @@ namespace Tensorflow
=> image_ops_impl.decode_image(contents, channels: channels, dtype: dtype,
name: name, expand_animations: expand_animations);
+ public Tensor encode_png(Tensor contents, string name = null)
+ => image_ops_impl.encode_png(contents, name: name);
+
+ public Tensor encode_jpeg(Tensor contents, string name = null)
+ => image_ops_impl.encode_jpeg(contents, name: name);
+
+
///
/// Convenience function to check if the 'contents' encodes a JPEG image.
///
diff --git a/src/TensorFlowNET.Core/APIs/tf.io.cs b/src/TensorFlowNET.Core/APIs/tf.io.cs
index be1e86e6..ea1e44b2 100644
--- a/src/TensorFlowNET.Core/APIs/tf.io.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.io.cs
@@ -16,6 +16,7 @@
using System.Collections.Generic;
using Tensorflow.IO;
+using Tensorflow.Operations;
namespace Tensorflow
{
@@ -46,6 +47,12 @@ namespace Tensorflow
public Tensor[] restore_v2(Tensor prefix, string[] tensor_names,
string[] shape_and_slices, TF_DataType[] dtypes, string name = null)
=> ops.restore_v2(prefix, tensor_names, shape_and_slices, dtypes, name: name);
+
+ public Operation write_file(string filename, Tensor conentes, string name = null)
+ => write_file(Tensorflow.ops.convert_to_tensor(filename, TF_DataType.TF_STRING), conentes, name);
+
+ public Operation write_file(Tensor filename, Tensor conentes, string name = null)
+ => gen_ops.write_file(filename, conentes, name);
}
public GFile gfile = new GFile();
diff --git a/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.cs b/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.cs
index a8141d35..3fd98e7a 100644
--- a/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.cs
+++ b/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.cs
@@ -55,6 +55,12 @@ namespace Tensorflow.Keras.Layers
string kernel_initializer = "glorot_uniform",
string bias_initializer = "zeros");
+ public ILayer Conv2D(int filters,
+ Shape kernel_size = null,
+ Shape strides = null,
+ string padding = "valid"
+ );
+
public ILayer Conv2D(int filters,
Shape kernel_size = null,
Shape strides = null,
diff --git a/src/TensorFlowNET.Core/Operations/image_ops_impl.cs b/src/TensorFlowNET.Core/Operations/image_ops_impl.cs
index 318b8b14..f1aff28e 100644
--- a/src/TensorFlowNET.Core/Operations/image_ops_impl.cs
+++ b/src/TensorFlowNET.Core/Operations/image_ops_impl.cs
@@ -102,7 +102,10 @@ namespace Tensorflow
{
throw new ValueError("\'image\' must be fully defined.");
}
- var dims = image_shape["-3:"];
+ var dims = new Shape(new[] {
+ image_shape.dims[image_shape.dims.Length - 3],
+ image_shape.dims[image_shape.dims.Length - 2],
+ image_shape.dims[image_shape.dims.Length - 1]});
foreach (var dim in dims.dims)
{
if (dim == 0)
@@ -112,16 +115,18 @@ namespace Tensorflow
}
var image_shape_last_three_elements = new Shape(new[] {
- image_shape.dims[image_shape.dims.Length - 1],
+ image_shape.dims[image_shape.dims.Length - 3],
image_shape.dims[image_shape.dims.Length - 2],
- image_shape.dims[image_shape.dims.Length - 3]});
+ image_shape.dims[image_shape.dims.Length - 1]});
if (!image_shape_last_three_elements.IsFullyDefined)
{
Tensor image_shape_ = array_ops.shape(image);
- var image_shape_return = tf.constant(new[] {
- image_shape_.dims[image_shape.dims.Length - 1],
- image_shape_.dims[image_shape.dims.Length - 2],
- image_shape_.dims[image_shape.dims.Length - 3]});
+ var image_shape_return = tf.slice(image_shape_, new[] { Math.Max(image_shape.dims.Length - 3, 0) }, new[] { 3 });
+
+ //var image_shape_return = tf.constant(new[] {
+ // image_shape_.dims[image_shape_.dims.Length - 3],
+ // image_shape_.dims[image_shape_.dims.Length - 2],
+ // image_shape_.dims[image_shape_.dims.Length - 1]});
return new Operation[] {
check_ops.assert_positive(
@@ -209,10 +214,10 @@ namespace Tensorflow
}
public static Tensor flip_left_right(Tensor image)
- => _flip(image, 0, "flip_left_right");
+ => _flip(image, 1, "flip_left_right");
public static Tensor flip_up_down(Tensor image)
- => _flip(image, 1, "flip_up_down");
+ => _flip(image, 0, "flip_up_down");
internal static Tensor _flip(Tensor image, int flip_index, string scope_name)
{
@@ -223,11 +228,11 @@ namespace Tensorflow
Shape shape = image.shape;
if (shape.ndim == 3 || shape.ndim == Unknown)
{
- return fix_image_flip_shape(image, gen_array_ops.reverse(image, ops.convert_to_tensor(new int[] { flip_index })));
+ return fix_image_flip_shape(image, gen_array_ops.reverse_v2(image, ops.convert_to_tensor(new int[] { flip_index })));
}
else if (shape.ndim == 4)
{
- return gen_array_ops.reverse_v2(image, ops.convert_to_tensor(new[] { (flip_index + 1) % 2 }));
+ return gen_array_ops.reverse_v2(image, ops.convert_to_tensor(new[] { flip_index + 1 }));
}
else
{
@@ -2047,6 +2052,22 @@ new_height, new_width");
});
}
+ public static Tensor encode_jpeg(Tensor contents, string name = null)
+ {
+ return tf_with(ops.name_scope(name, "encode_jpeg"), scope =>
+ {
+ return gen_ops.encode_jpeg(contents, name:name);
+ });
+ }
+
+ public static Tensor encode_png(Tensor contents, string name = null)
+ {
+ return tf_with(ops.name_scope(name, "encode_png"), scope =>
+ {
+ return gen_ops.encode_png(contents, name: name);
+ });
+ }
+
public static Tensor is_jpeg(Tensor contents, string name = null)
{
return tf_with(ops.name_scope(name, "is_jpeg"), scope =>
diff --git a/src/TensorFlowNET.Keras/Layers/LayersApi.cs b/src/TensorFlowNET.Keras/Layers/LayersApi.cs
index 95828fbf..bcc19dc2 100644
--- a/src/TensorFlowNET.Keras/Layers/LayersApi.cs
+++ b/src/TensorFlowNET.Keras/Layers/LayersApi.cs
@@ -112,7 +112,28 @@ namespace Tensorflow.Keras.Layers
KernelInitializer = GetInitializerByName(kernel_initializer),
BiasInitializer = GetInitializerByName(bias_initializer)
});
-
+ public ILayer Conv2D(int filters,
+ Shape kernel_size = null,
+ Shape strides = null,
+ string padding = "valid")
+ => new Conv2D(new Conv2DArgs
+ {
+ Rank = 2,
+ Filters = filters,
+ KernelSize = (kernel_size == null) ? (5, 5) : kernel_size,
+ Strides = strides == null ? (1, 1) : strides,
+ Padding = padding,
+ DataFormat = null,
+ DilationRate = (1, 1),
+ Groups = 1,
+ UseBias = false,
+ KernelRegularizer = null,
+ KernelInitializer =tf.glorot_uniform_initializer,
+ BiasInitializer = tf.zeros_initializer,
+ BiasRegularizer = null,
+ ActivityRegularizer = null,
+ Activation = keras.activations.Linear,
+ });
///
/// 2D convolution layer (e.g. spatial convolution over images).
/// This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs.
diff --git a/test/TensorFlowNET.Graph.UnitTest/ImageTest.cs b/test/TensorFlowNET.Graph.UnitTest/ImageTest.cs
index d671b609..127b65bf 100644
--- a/test/TensorFlowNET.Graph.UnitTest/ImageTest.cs
+++ b/test/TensorFlowNET.Graph.UnitTest/ImageTest.cs
@@ -4,6 +4,7 @@ using System.Linq;
using Tensorflow;
using static Tensorflow.Binding;
using System;
+using System.IO;
namespace TensorFlowNET.UnitTest
{
@@ -164,5 +165,94 @@ namespace TensorFlowNET.UnitTest
Assert.AreEqual(result.size, 16ul);
Assert.AreEqual(result[0, 0, 0, 0], 12f);
}
+
+ [TestMethod]
+ public void ImageSaveTest()
+ {
+ var imgPath = TestHelper.GetFullPathFromDataDir("img001.bmp");
+ var jpegImgPath = TestHelper.GetFullPathFromDataDir("img001.jpeg");
+ var pngImgPath = TestHelper.GetFullPathFromDataDir("img001.png");
+
+ File.Delete(jpegImgPath);
+ File.Delete(pngImgPath);
+
+ var contents = tf.io.read_file(imgPath);
+ var bmp = tf.image.decode_image(contents);
+ Assert.AreEqual(bmp.name, "decode_image/DecodeImage:0");
+
+ var jpeg = tf.image.encode_jpeg(bmp);
+ var op1 = tf.io.write_file(jpegImgPath, jpeg);
+
+ var png = tf.image.encode_png(bmp);
+ var op2 = tf.io.write_file(pngImgPath, png);
+
+ this.session().run(op1);
+ this.session().run(op2);
+
+ Assert.IsTrue(File.Exists(jpegImgPath), "not find file:" + jpegImgPath);
+ Assert.IsTrue(File.Exists(pngImgPath), "not find file:" + pngImgPath);
+
+ // 如果要测试图片正确性，需要注释下面两行代码
+ File.Delete(jpegImgPath);
+ File.Delete(pngImgPath);
+ }
+
+ [TestMethod]
+ public void ImageFlipTest()
+ {
+ var imgPath = TestHelper.GetFullPathFromDataDir("img001.bmp");
+
+ var contents = tf.io.read_file(imgPath);
+ var bmp = tf.image.decode_image(contents);
+
+ // 左右翻转
+ var lrImgPath = TestHelper.GetFullPathFromDataDir("img001_lr.png");
+ File.Delete(lrImgPath);
+
+ var lr = tf.image.flip_left_right(bmp);
+ var png = tf.image.encode_png(lr);
+ var op = tf.io.write_file(lrImgPath, png);
+ this.session().run(op);
+
+ Assert.IsTrue(File.Exists(lrImgPath), "not find file:" + lrImgPath);
+
+ // 上下翻转
+ var updownImgPath = TestHelper.GetFullPathFromDataDir("img001_updown.png");
+ File.Delete(updownImgPath);
+
+ var updown = tf.image.flip_up_down(bmp);
+ var pngupdown = tf.image.encode_png(updown);
+ var op2 = tf.io.write_file(updownImgPath, pngupdown);
+ this.session().run(op2);
+ Assert.IsTrue(File.Exists(updownImgPath));
+
+
+ // 暂时先人工观测图片是否翻转，观测时需要删除下面这两行代码
+ File.Delete(lrImgPath);
+ File.Delete(updownImgPath);
+
+ // 多图翻转
+ // 目前直接通过 bmp 拿到 shape ，这里先用默认定义图片大小来构建了
+ var mImg = tf.stack(new[] { bmp, lr }, axis:0);
+ print(mImg.shape);
+
+ var up2 = tf.image.flip_up_down(mImg);
+
+ var updownImgPath_m1 = TestHelper.GetFullPathFromDataDir("img001_m_ud.png"); // 直接上下翻转
+ File.Delete(updownImgPath_m1);
+
+ var img001_updown_m2 = TestHelper.GetFullPathFromDataDir("img001_m_lr_ud.png"); // 先左右再上下
+ File.Delete(img001_updown_m2);
+
+ var png2 = tf.image.encode_png(up2[0]);
+ tf.io.write_file(updownImgPath_m1, png2);
+
+ png2 = tf.image.encode_png(up2[1]);
+ tf.io.write_file(img001_updown_m2, png2);
+
+ // 如果要测试图片正确性，需要注释下面两行代码
+ File.Delete(updownImgPath_m1);
+ File.Delete(img001_updown_m2);
+ }
}
}
diff --git a/test/TensorFlowNET.UnitTest/ManagedAPI/ArrayOpsTest.cs b/test/TensorFlowNET.UnitTest/ManagedAPI/ArrayOpsTest.cs
index 675689bb..e25c9779 100644
--- a/test/TensorFlowNET.UnitTest/ManagedAPI/ArrayOpsTest.cs
+++ b/test/TensorFlowNET.UnitTest/ManagedAPI/ArrayOpsTest.cs
@@ -3,6 +3,7 @@ using Tensorflow.NumPy;
using Tensorflow;
using static Tensorflow.Binding;
using System.Linq;
+using Tensorflow.Operations;
namespace TensorFlowNET.UnitTest.ManagedAPI
{
@@ -105,5 +106,321 @@ namespace TensorFlowNET.UnitTest.ManagedAPI
Assert.IsTrue(Equal(a[0].ToArray().Reverse().ToArray(), b[0].ToArray()));
Assert.IsTrue(Equal(a[1].ToArray().Reverse().ToArray(), b[1].ToArray()));
}
+
+ [TestMethod]
+ public void ReverseImgArray3D()
+ {
+ // 创建 sourceImg 数组
+ var sourceImgArray = new float[,,] {
+ {
+ { 237, 28, 36 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ },
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ }
+ };
+ var sourceImg = ops.convert_to_tensor(sourceImgArray);
+
+ // 创建 lrImg 数组
+ var lrImgArray = new float[,,] {
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 237, 28, 36 }
+ },
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ }
+ };
+ var lrImg = ops.convert_to_tensor(lrImgArray);
+
+ var lr = tf.image.flip_left_right(sourceImg);
+ Assert.IsTrue(Equal(lrImg.numpy().ToArray(), lr.numpy().ToArray()), "tf.image.flip_left_right fail.");
+
+ var lr2 = tf.reverse(sourceImg, 1);
+ Assert.IsTrue(Equal(lrImg.numpy().ToArray(), lr2.numpy().ToArray()), "tf.reverse (axis=1) fail.");
+
+ var lr3 = gen_array_ops.reverse_v2(sourceImg, ops.convert_to_tensor(new[] { 1 }));
+ Assert.IsTrue(Equal(lrImg.numpy().ToArray(), lr3.numpy().ToArray()), "gen_array_ops.reverse_v2 axis=1 fail.");
+
+ // 创建 udImg 数组
+ var udImgArray = new float[,,] {
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ },
+ {
+ { 237, 28, 36 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ }
+ };
+ var udImg = ops.convert_to_tensor(udImgArray);
+
+ var ud = tf.image.flip_up_down(sourceImg);
+ Assert.IsTrue(Equal(udImg.numpy().ToArray(), ud.numpy().ToArray()), "tf.image.flip_up_down fail.");
+
+ var ud2 = tf.reverse(sourceImg, new Axis(0));
+ Assert.IsTrue(Equal(udImg.numpy().ToArray(), ud2.numpy().ToArray()), "tf.reverse (axis=0) fail.");
+
+ var ud3 = gen_array_ops.reverse_v2(sourceImg, ops.convert_to_tensor(new[] { 0 }));
+ Assert.IsTrue(Equal(udImg.numpy().ToArray(), ud3.numpy().ToArray()), "gen_array_ops.reverse_v2 axis=0 fail.");
+ }
+
+ [TestMethod]
+ public void ReverseImgArray4D()
+ {
+ // 原图左上角，加一张左右翻转后的图片
+ var m = new float[,,,] {
+ {
+ {
+ { 237, 28, 36 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ },
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ }
+ },
+ {
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 237, 28, 36 }
+ },
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ }
+ }
+ };
+ var sourceImg = ops.convert_to_tensor(m);
+
+ var lrArray = new float[,,,] {
+ {
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 237, 28, 36 },
+ },
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ }
+ },
+ {
+ {
+ { 237, 28, 36 },
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ },
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ }
+ }
+ };
+ var lrImg = ops.convert_to_tensor(lrArray);
+
+ // 创建 ud 数组
+ var udArray = new float[,,,] {
+ {
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ },
+ {
+ { 237, 28, 36 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ }
+ },
+ {
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ },
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 237, 28, 36 }
+ }
+ }
+ };
+ var udImg = ops.convert_to_tensor(udArray);
+
+ var ud3 = gen_array_ops.reverse_v2(sourceImg, ops.convert_to_tensor(new[] { 1 }));
+ Assert.IsTrue(Equal(udImg.numpy().ToArray(), ud3.numpy().ToArray()), "gen_array_ops.reverse_v2 axis=1 fail.");
+
+ var ud2 = tf.reverse(sourceImg, new Axis(1));
+ Assert.IsTrue(Equal(udImg.numpy().ToArray(), ud2.numpy().ToArray()), "tf.reverse (axis=1) fail.");
+
+ var ud = tf.image.flip_up_down(sourceImg);
+ Assert.IsTrue(Equal(udImg.numpy().ToArray(), ud.numpy().ToArray()), "tf.image.flip_up_down fail.");
+
+ // 左右翻转
+ var lr = tf.image.flip_left_right(sourceImg);
+ Assert.IsTrue(Equal(lrImg.numpy().ToArray(), lr.numpy().ToArray()), "tf.image.flip_left_right fail.");
+
+ var lr2 = tf.reverse(sourceImg, 0);
+ Assert.IsTrue(Equal(lrImg.numpy().ToArray(), lr2.numpy().ToArray()), "tf.reverse (axis=1) fail.");
+
+ var lr3 = gen_array_ops.reverse_v2(sourceImg, ops.convert_to_tensor(new[] { 0 }));
+ Assert.IsTrue(Equal(lrImg.numpy().ToArray(), lr3.numpy().ToArray()), "gen_array_ops.reverse_v2 axis=1 fail.");
+
+ }
+
+ [TestMethod]
+ public void ReverseImgArray4D_3x3()
+ {
+ // 原图左上角，加一张左右翻转后的图片
+ var m = new float[,,,] {
+ {
+ {
+ { 237, 28, 36 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ },
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ },
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ }
+ },
+ {
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 237, 28, 36 }
+ },
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ },
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ }
+ }
+ };
+ var sourceImg = ops.convert_to_tensor(m);
+
+ var lrArray = new float[,,,] {
+ {
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 237, 28, 36 },
+ },
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ },
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ }
+ },
+ {
+ {
+ { 237, 28, 36 },
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ },
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ },
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ }
+ }
+ };
+ var lrImg = ops.convert_to_tensor(lrArray);
+
+ // 创建 ud 数组
+ var udArray = new float[,,,] {
+ {
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ },
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ },
+ {
+ { 237, 28, 36 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ }
+ },
+ { {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ },
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 255, 255, 255 }
+ },
+ {
+ { 255, 255, 255 },
+ { 255, 255, 255 },
+ { 237, 28, 36 }
+ }
+ }
+ };
+ var udImg = ops.convert_to_tensor(udArray);
+
+ var ud3 = gen_array_ops.reverse_v2(sourceImg, ops.convert_to_tensor(new[] { 1 }));
+ Assert.IsTrue(Equal(udImg.numpy().ToArray(), ud3.numpy().ToArray()), "gen_array_ops.reverse_v2 axis=1 fail.");
+
+ var ud2 = tf.reverse(sourceImg, new Axis(1));
+ Assert.IsTrue(Equal(udImg.numpy().ToArray(), ud2.numpy().ToArray()), "tf.reverse (axis=1) fail.");
+
+ var ud = tf.image.flip_up_down(sourceImg);
+ Assert.IsTrue(Equal(udImg.numpy().ToArray(), ud.numpy().ToArray()), "tf.image.flip_up_down fail.");
+
+ // 左右翻转
+ var lr = tf.image.flip_left_right(sourceImg);
+ Assert.IsTrue(Equal(lrImg.numpy().ToArray(), lr.numpy().ToArray()), "tf.image.flip_left_right fail.");
+
+ var lr2 = tf.reverse(sourceImg, 0);
+ Assert.IsTrue(Equal(lrImg.numpy().ToArray(), lr2.numpy().ToArray()), "tf.reverse (axis=1) fail.");
+
+ var lr3 = gen_array_ops.reverse_v2(sourceImg, ops.convert_to_tensor(new[] { 0 }));
+ Assert.IsTrue(Equal(lrImg.numpy().ToArray(), lr3.numpy().ToArray()), "gen_array_ops.reverse_v2 axis=1 fail.");
+
+ }
}
}
diff --git a/test/TensorFlowNET.UnitTest/NumPy/ShapeTest.cs b/test/TensorFlowNET.UnitTest/NumPy/ShapeTest.cs
new file mode 100644
index 00000000..f5a8685b
--- /dev/null
+++ b/test/TensorFlowNET.UnitTest/NumPy/ShapeTest.cs
@@ -0,0 +1,44 @@
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+using Tensorflow.NumPy;
+using System;
+using System.Linq;
+using static Tensorflow.Binding;
+using Tensorflow;
+
+namespace TensorFlowNET.UnitTest.NumPy
+{
+ [TestClass]
+ public class ShapeTest : EagerModeTestBase
+ {
+ [Ignore]
+ [TestMethod]
+ public unsafe void ShapeGetLastElements()
+ {
+ // test code from function _CheckAtLeast3DImage
+ // 之前的 _CheckAtLeast3DImage 有bug，现在通过测试，下面的代码是正确的
+ // todo: shape["-3:"] 的写法，目前有bug，需要修复，单元测试等修复后再放开，暂时先忽略测试
+
+ var image_shape = new Shape(new[] { 32, 64, 3 });
+ var image_shape_4d = new Shape(new[] { 4, 64, 32, 3 });
+
+ var image_shape_last_three_elements = new Shape(new[] {
+ image_shape.dims[image_shape.dims.Length - 3],
+ image_shape.dims[image_shape.dims.Length - 2],
+ image_shape.dims[image_shape.dims.Length - 1]});
+
+ var image_shape_last_three_elements2 = image_shape["-3:"];
+
+ Assert.IsTrue(Equal(image_shape_last_three_elements.dims, image_shape_last_three_elements2.dims), "3dims get fail.");
+
+ var image_shape_last_three_elements_4d = new Shape(new[] {
+ image_shape_4d.dims[image_shape_4d.dims.Length - 3],
+ image_shape_4d.dims[image_shape_4d.dims.Length - 2],
+ image_shape_4d.dims[image_shape_4d.dims.Length - 1]});
+
+ var image_shape_last_three_elements2_4d = image_shape_4d["-3:"];
+
+ Assert.IsTrue(Equals(image_shape_last_three_elements_4d.dims, image_shape_last_three_elements2_4d.dims), "4dims get fail.");
+ }
+
+ }
+}
\ No newline at end of file