Merge branch 'master' into ndarrayload

# Conflicts:
#	src/TensorFlowNET.Core/Tensorflow.Binding.csproj
#	src/TensorFlowNET.Keras/Datasets/Imdb.cs

Author: lingbai-kong

src/TensorFlowNET.Core/APIs/c_api.cs

@@ -16,6 +16,7 @@ limitations under the License.
 
 using System;
 using System.Runtime.InteropServices;
+using static Tensorflow.CppShapeInferenceResult.Types;
 
 namespace Tensorflow
 {
@@ -50,6 +51,35 @@ public static string StringPiece(IntPtr handle)
             return handle == IntPtr.Zero ? String.Empty : Marshal.PtrToStringAnsi(handle);
         }
 
+        public unsafe static byte[] ByteStringPiece(Buffer? handle)
+        {
+            if (handle is null)
+            {
+                return new byte[0];
+            }
+            var data = handle.ToArray();
+            return data;
+        }
+          
+          public unsafe static byte[] ByteStringPieceFromNativeString(IntPtr handle)
+        {
+            if (handle == IntPtr.Zero)
+            {
+                return new byte[0];
+            }
+
+            byte* str_data = (byte*)handle.ToPointer();
+            List<byte> bytes = new List<byte>();
+            byte current = 255;
+            while (current != ((byte)'\0'))
+            {
+                current = *(str_data++);
+                bytes.Add(current);
+            }
+            var data = bytes.ToArray();
+            return data;
+        }
+
         [UnmanagedFunctionPointer(CallingConvention.Winapi)]
         public delegate void Deallocator(IntPtr data, IntPtr size, ref DeallocatorArgs args);
 

src/TensorFlowNET.Core/APIs/c_api.customize.cs

@@ -10,7 +10,7 @@ public partial class c_api
         [DllImport(TensorFlowLibName)]
         public static extern void TFC_SetAttr(SafeGraphHandle graph, IntPtr op, string attr_name, SafeBufferHandle attr_value_proto, SafeStatusHandle status);
         [DllImport(TensorFlowLibName)]
-        public static extern IntPtr TFC_GetHandleShapeAndType(SafeGraphHandle c_graph, TF_Output output);
+        public static extern SafeBufferHandle TFC_GetHandleShapeAndType(SafeGraphHandle c_graph, TF_Output output);
         [DllImport(TensorFlowLibName)]
         public static extern void TFC_SetHandleShapeAndType(SafeGraphHandle c_graph, TF_Output output, byte[] data, long proto_len, SafeStatusHandle status);
     }

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

@@ -91,8 +91,7 @@ public Tensor concat(IEnumerable<Tensor> values, int axis, string name = "concat
                     return identity(values.First(), name: scope);
                 });
             }
-
-            return gen_array_ops.concat_v2(values.ToArray(), ops.convert_to_tensor(axis), name: name);
+            return array_ops.concat(values.ToArray(), axis, name: name);
         }
 
         /// <summary>
@@ -163,14 +162,17 @@ public Tensor transpose<T1>(T1 a, Axis perm = null, string name = "transpose", b
         /// Reverses specific dimensions of a tensor.
         /// </summary>
         /// <param name="tensor"></param>
-        /// <param name="axis"></param>
+        /// <param name="axis">The indices of the dimensions to reverse. Must be in the range [-rank(tensor), rank(tensor)).</param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public Tensor reverse(Tensor tensor, int[] axis, string name = null)
-            => gen_array_ops.reverse(tensor, ops.convert_to_tensor(axis), name: name);
-
-        public Tensor reverse(Tensor tensor, Tensor axis, string name = null)
-            => gen_array_ops.reverse(tensor, axis, name: name);
+        public Tensor reverse(Tensor tensor, Axis axis, string name = null)
+        {
+            if (axis.IsScalar)
+            {
+                axis = new Axis(axis.axis);
+            }
+            return array_ops.reverse(tensor, axis, name: name);
+        }
 
         /// <summary>
         /// Returns the rank of a tensor.

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

@@ -46,10 +46,10 @@ public Tensor while_loop(Func<Tensor, Tensor> cond,
             Tensor loop_vars,
             int parallel_iterations = 10)
         {
-            Func<Tensor[], Tensor> cond1 = x
+            Func<Tensors, Tensor> cond1 = x
                 => cond(x[0]);
 
-            Func<Tensor[], Tensor[]> body1 = x
+            Func<Tensors, Tensors> body1 = x
                 => new[] { body(x[0]) };
 
             var results = control_flow_ops.while_loop(cond1,
@@ -58,9 +58,9 @@ public Tensor while_loop(Func<Tensor, Tensor> cond,
             return results[0];
         }
 
-        public Tensor[] while_loop(Func<Tensor[], Tensor> cond,
-            Func<Tensor[], Tensor[]> body,
-            Tensor[] loop_vars,
+        public Tensor[] while_loop(Func<Tensors, Tensor> cond,
+            Func<Tensors, Tensors> body,
+            Tensors loop_vars,
             int parallel_iterations = 10,
             string name = null)
             => control_flow_ops.while_loop(cond, body, loop_vars,

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

@@ -14,6 +14,10 @@ You may obtain a copy of the License at
    limitations under the License.
 ******************************************************************************/
 
+using OneOf.Types;
+using System;
+using System.Buffers.Text;
+using Tensorflow.Contexts;
 using static Tensorflow.Binding;
 
 namespace Tensorflow
@@ -162,17 +166,108 @@ public Tensor ssim_multiscale(Tensor img1, Tensor img2, float max_val, float[] p
             public Tensor sobel_edges(Tensor image)
                 => image_ops_impl.sobel_edges(image);
 
-            public Tensor decode_jpeg(Tensor contents,
-                        int channels = 0,
-                        int ratio = 1,
-                        bool fancy_upscaling = true,
-                        bool try_recover_truncated = false,
-                        int acceptable_fraction = 1,
-                        string dct_method = "",
-                        string name = null)
-                => gen_image_ops.decode_jpeg(contents, channels: channels, ratio: ratio,
-                    fancy_upscaling: fancy_upscaling, try_recover_truncated: try_recover_truncated,
-                    acceptable_fraction: acceptable_fraction, dct_method: dct_method);
+            /// <summary>
+            /// Adjust contrast of RGB or grayscale images.
+            /// </summary>
+            /// <param name="images">Images to adjust.  At least 3-D.</param>
+            /// <param name="contrast_factor"></param>
+            /// <param name="name">A float multiplier for adjusting contrast.</param>
+            /// <returns>The contrast-adjusted image or images.</returns>
+            public Tensor adjust_contrast(Tensor images, float contrast_factor, string name = null)
+                => gen_image_ops.adjust_contrastv2(images, contrast_factor, name);
+
+            /// <summary>
+            /// Adjust hue of RGB images.
+            /// </summary>
+            /// <param name="images">RGB image or images. The size of the last dimension must be 3.</param>
+            /// <param name="delta">float.  How much to add to the hue channel.</param>
+            /// <param name="name">A name for this operation (optional).</param>
+            /// <returns>Adjusted image(s), same shape and DType as `image`.</returns>
+            /// <exception cref="ValueError">if `delta` is not in the interval of `[-1, 1]`.</exception>
+            public Tensor adjust_hue(Tensor images, float delta, string name = null)
+            {
+                if (tf.Context.executing_eagerly())
+                {
+                    if (delta < -1f || delta > 1f)
+                        throw new ValueError("delta must be in the interval [-1, 1]");
+                }
+                return gen_image_ops.adjust_hue(images, delta, name: name);
+            }
+
+            /// <summary>
+            /// Adjust saturation of RGB images.
+            /// </summary>
+            /// <param name="image">RGB image or images. The size of the last dimension must be 3.</param>
+            /// <param name="saturation_factor">float. Factor to multiply the saturation by.</param>
+            /// <param name="name">A name for this operation (optional).</param>
+            /// <returns>Adjusted image(s), same shape and DType as `image`.</returns>
+            public Tensor adjust_saturation(Tensor image, float saturation_factor, string name = null)
+                => gen_image_ops.adjust_saturation(image, saturation_factor, name);
+
+            /// <summary>
+            /// Greedily selects a subset of bounding boxes in descending order of score.
+            /// </summary>
+            /// <param name="boxes">
+            /// A 4-D float `Tensor` of shape `[batch_size, num_boxes, q, 4]`. If `q` 
+            /// is 1 then same boxes are used for all classes otherwise, if `q` is equal 
+            /// to number of classes, class-specific boxes are used.
+            /// </param>
+            /// <param name="scores">
+            /// A 3-D float `Tensor` of shape `[batch_size, num_boxes, num_classes]` 
+            /// representing a single score corresponding to each box(each row of boxes).
+            /// </param>
+            /// <param name="max_output_size_per_class">
+            /// A scalar integer `Tensor` representing the 
+            /// maximum number of boxes to be selected by non-max suppression per class
+            /// </param>
+            /// <param name="max_total_size">
+            /// A int32 scalar representing maximum number of boxes retained 
+            /// over all classes.Note that setting this value to a large number may 
+            /// result in OOM error depending on the system workload.
+            /// </param>
+            /// <param name="iou_threshold">
+            /// A float representing the threshold for deciding whether boxes 
+            /// overlap too much with respect to IOU.
+            /// </param>
+            /// <param name="score_threshold">
+            /// A float representing the threshold for deciding when to 
+            /// remove boxes based on score.
+            /// </param>
+            /// <param name="pad_per_class">
+            /// If false, the output nmsed boxes, scores and classes are 
+            /// padded/clipped to `max_total_size`. If true, the output nmsed boxes, scores and classes are padded to be of length `max_size_per_class`*`num_classes`, 
+            /// unless it exceeds `max_total_size` in which case it is clipped to `max_total_size`. Defaults to false.
+            /// </param>
+            /// <param name="clip_boxes">
+            /// If true, the coordinates of output nmsed boxes will be clipped 
+            /// to[0, 1]. If false, output the box coordinates as it is. Defaults to true.
+            /// </param>
+            /// <returns>
+            /// 'nmsed_boxes': A [batch_size, max_detections, 4] float32 tensor containing the non-max suppressed boxes.
+            /// 'nmsed_scores': A [batch_size, max_detections] float32 tensor containing the scores for the boxes.
+            /// 'nmsed_classes': A [batch_size, max_detections] float32 tensor containing the class for boxes.
+            /// 'valid_detections': A [batch_size] int32 tensor indicating the number of
+            ///     valid detections per batch item. Only the top valid_detections[i] entries
+            ///     in nms_boxes[i], nms_scores[i] and nms_class[i] are valid. The rest of the
+            ///     entries are zero paddings.
+            /// </returns>
+            public (Tensor, Tensor, Tensor, Tensor) combined_non_max_suppression(
+                        Tensor boxes, 
+                        Tensor scores, 
+                        int max_output_size_per_class, 
+                        int max_total_size,
+                        float iou_threshold,
+                        float score_threshold,
+                        bool pad_per_class = false,
+                        bool clip_boxes = true)
+            {
+                var iou_threshold_t = ops.convert_to_tensor(iou_threshold, TF_DataType.TF_FLOAT, name: "iou_threshold");
+                var score_threshold_t = ops.convert_to_tensor(score_threshold, TF_DataType.TF_FLOAT, name: "score_threshold");
+                var max_total_size_t = ops.convert_to_tensor(max_total_size);
+                var max_output_size_per_class_t = ops.convert_to_tensor(max_output_size_per_class);
+                return gen_image_ops.combined_non_max_suppression(boxes, scores, max_output_size_per_class_t, max_total_size_t,
+                                       iou_threshold_t, score_threshold_t, pad_per_class, clip_boxes);
+            }
 
             /// <summary>
             /// Extracts crops from the input image tensor and resizes them using bilinear sampling or nearest neighbor sampling (possibly with aspect ratio change) to a common output size specified by crop_size. This is more general than the crop_to_bounding_box op which extracts a fixed size slice from the input image and does not allow resizing or aspect ratio change.
@@ -187,7 +282,19 @@ public Tensor decode_jpeg(Tensor contents,
             /// <param name="name">A name for the operation (optional).</param>
             /// <returns>A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth].</returns>
             public Tensor crop_and_resize(Tensor image, Tensor boxes, Tensor box_ind, Tensor crop_size, string method = "bilinear", float extrapolation_value = 0f, string name = null) =>
-                image_ops_impl.crop_and_resize(image, boxes, box_ind, crop_size, method, extrapolation_value, name);
+                gen_image_ops.crop_and_resize(image, boxes, box_ind, crop_size, method, extrapolation_value, name);
+
+            public Tensor decode_jpeg(Tensor contents,
+                        int channels = 0,
+                        int ratio = 1,
+                        bool fancy_upscaling = true,
+                        bool try_recover_truncated = false,
+                        int acceptable_fraction = 1,
+                        string dct_method = "",
+                        string name = null)
+                => gen_image_ops.decode_jpeg(contents, channels: channels, ratio: ratio,
+                    fancy_upscaling: fancy_upscaling, try_recover_truncated: try_recover_truncated,
+                    acceptable_fraction: acceptable_fraction, dct_method: dct_method);
 
             public Tensor extract_glimpse(Tensor input, Tensor size, Tensor offsets, bool centered = true, bool normalized = true,
                 bool uniform_noise = true, string name = null)

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

@@ -14,6 +14,7 @@ You may obtain a copy of the License at
    limitations under the License.
 ******************************************************************************/
 
+using Tensorflow.NumPy;
 using Tensorflow.Operations;
 
 namespace Tensorflow
@@ -42,10 +43,20 @@ public Tensor erf(Tensor x, string name = null)
 
             public Tensor multiply(Tensor x, Tensor y, string name = null)
                 => math_ops.multiply(x, y, name: name);
-
             public Tensor divide_no_nan(Tensor a, Tensor b, string name = null)
                 => math_ops.div_no_nan(a, b);
 
+            /// <summary>
+            /// Computes the Euclidean norm of elements across dimensions of a tensor.
+            /// </summary>
+            /// <param name="input_tensor">The tensor to reduce. Should have numeric type.</param>
+            /// <param name="axis">The dimensions to reduce. If `None` (the default), reduces all dimensions.Must be in the range `[-rank(input_tensor), rank(input_tensor))`</param>
+            /// <param name="keepdims">If true, retains reduced dimensions with length 1.</param>
+            /// <param name="name">A name for the operation (optional).</param>
+            /// <returns>The reduced tensor, of the same dtype as the input_tensor.</returns>
+            public Tensor reduce_euclidean_norm(Tensor input_tensor, Axis? axis = null, bool keepdims = false, string name = null)
+                => math_ops.reduce_euclidean_norm(input_tensor, axis: axis, keepdims: keepdims, name);
+
             public Tensor square(Tensor x, string name = null)
                 => math_ops.square(x, name: name);
 
@@ -354,7 +365,7 @@ public Tensor divide(Tensor a, Tensor b)
             => a / b;
 
         public Tensor sqrt(Tensor a, string name = null)
-            => gen_math_ops.sqrt(a, name);
+            => math_ops.sqrt(a, name);
 
         public Tensor sign(Tensor a, string name = null)
             => gen_math_ops.sign(a, name);
@@ -452,7 +463,18 @@ public Tensor multiply(Tensor x, Tensor y, string name = null)
         /// <returns></returns>
         public Tensor multiply<Tx, Ty>(Tx x, Ty y, string name = null)
             => gen_math_ops.mul(ops.convert_to_tensor(x), ops.convert_to_tensor(y), name: name);
-
+        /// <summary>
+        /// return scalar product
+        /// </summary>
+        /// <typeparam name="Tx"></typeparam>
+        /// <typeparam name="Ty"></typeparam>
+        /// <param name="x"></param>
+        /// <param name="y"></param>
+        /// <param name="axes"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public Tensor dot_prod<Tx, Ty>(Tx x, Ty y, NDArray axes, string name = null)
+            => math_ops.tensordot(convert_to_tensor(x), convert_to_tensor(y), axes, name: name);
         public Tensor negative(Tensor x, string name = null)
             => gen_math_ops.neg(x, name);
 
@@ -600,5 +622,7 @@ public Tensor squared_difference(Tensor x, Tensor y, string name = null)
             => gen_math_ops.squared_difference(x: x, y: y, name: name);
         public Tensor complex(Tensor real, Tensor imag, Tensorflow.TF_DataType? dtype = null, 
                 string name = null) => gen_ops.complex(real, imag, dtype, name);
+        public Tensor exp(Tensor x,
+                string name = null) => gen_math_ops.exp(x, name);
     }
 }

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

@@ -14,6 +14,7 @@ You may obtain a copy of the License at
    limitations under the License.
 ******************************************************************************/
 
+using System.Xml.Linq;
 using Tensorflow.Operations;
 using Tensorflow.Operations.Activation;
 using static Tensorflow.Binding;
@@ -126,6 +127,26 @@ public Tensor[] fused_batch_norm(Tensor x,
                     name: name,
                     exponential_avg_factor: exponential_avg_factor);
 
+            /// <summary>
+            /// Normalizes a tensor by `mean` and `variance`, and applies (optionally) a`scale` \\(\gamma\\) to it, as well as an `offset` \\(\beta\\).
+            /// </summary>
+            /// <param name="x">A floating point tensor.</param>
+            /// <param name="mean">A mean `Tensor`.</param>
+            /// <param name="variance">A variance `Tensor`.</param>
+            /// <param name="offset"> An offset `Tensor`, often denoted \\(\beta\\) in equations, or NULL. If present, will be added to the normalized tensor.</param>
+            /// <param name="scale"> A scale `Tensor`, often denoted \\(\gamma\\) in equations, or NULL. If present, the scale is applied to the normalized tensor.</param>
+            /// <param name="variance_epsilon"> A small float number to avoid dividing by 0.</param>
+            /// <param name="name">A name for this operation.</param>
+            /// <returns>the normalized, scaled, offset tensor.</returns>
+            public Tensor batch_normalization(Tensor x,
+                        Tensor mean,
+                        Tensor variance,
+                        Tensor offset,
+                        Tensor scale,
+                        float variance_epsilon,
+                        string name = null) => nn_impl.batch_normalization(x, mean, variance, offset, scale, variance_epsilon, name);
+
+
             public Tensor max_pool(Tensor value, int[] ksize, int[] strides, string padding, string data_format = "NHWC", string name = null)
                 => nn_ops.max_pool(value, ksize, strides, padding, data_format: data_format, name: name);
 

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

@@ -31,6 +31,6 @@ public Tensor reshape(Tensor tensor,
         public Tensor reshape(Tensor tensor,
                 object[] shape,
                 string name = null)
-            => gen_array_ops.reshape(tensor, ops.convert_to_tensor(shape), name);
+            => array_ops.reshape(tensor, shape, name);
     }
 }