| @@ -21,6 +21,12 @@ namespace Tensorflow | |||||
| { | { | ||||
| public class gen_image_ops | public class gen_image_ops | ||||
| { | { | ||||
| public static (Tensor, Tensor, Tensor, Tensor) combined_non_max_suppression(Tensor boxes, Tensor scores, Tensor max_output_size_per_class, Tensor max_total_size, | |||||
| Tensor iou_threshold, Tensor score_threshold, bool pad_per_class, bool clip_boxes) | |||||
| { | |||||
| throw new NotImplementedException("combined_non_max_suppression"); | |||||
| } | |||||
| public static Tensor convert_image_dtype(Tensor image, TF_DataType dtype, bool saturate = false, string name= null) | public static Tensor convert_image_dtype(Tensor image, TF_DataType dtype, bool saturate = false, string name= null) | ||||
| { | { | ||||
| if (dtype == image.dtype) | if (dtype == image.dtype) | ||||
| @@ -1086,9 +1086,9 @@ namespace Tensorflow | |||||
| public static Tensor pow<Tx, Ty>(Tx x, Ty y, string name = null) | public static Tensor pow<Tx, Ty>(Tx x, Ty y, string name = null) | ||||
| { | { | ||||
| if (tf.context.executing_eagerly()) | |||||
| if (tf.Context.executing_eagerly()) | |||||
| { | { | ||||
| var results = tf.Runner.TFE_FastPathExecute(tf.context, tf.context.device_name, | |||||
| var results = tf.Runner.TFE_FastPathExecute(tf.Context, tf.Context.DeviceName, | |||||
| "Pow", name, | "Pow", name, | ||||
| null, | null, | ||||
| x, y); | x, y); | ||||
| @@ -1096,7 +1096,7 @@ namespace Tensorflow | |||||
| return results[0]; | return results[0]; | ||||
| } | } | ||||
| var _op = tf._op_def_lib._apply_op_helper("Pow", name, args: new { x, y }); | |||||
| var _op = tf.OpDefLib._apply_op_helper("Pow", name, args: new { x, y }); | |||||
| return _op.outputs[0]; | return _op.outputs[0]; | ||||
| } | } | ||||
| @@ -1214,4 +1214,4 @@ namespace Tensorflow | |||||
| return _op.outputs[0]; | return _op.outputs[0]; | ||||
| } | } | ||||
| } | } | ||||
| } | |||||
| } | |||||
| @@ -1,4 +1,5 @@ | |||||
| using System.Linq; | |||||
| using System; | |||||
| using System.Linq; | |||||
| using System.Collections.Generic; | using System.Collections.Generic; | ||||
| using static Tensorflow.Binding; | using static Tensorflow.Binding; | ||||
| @@ -9881,6 +9882,11 @@ namespace Tensorflow.Operations | |||||
| return op.output; | return op.output; | ||||
| } | } | ||||
| public static Tensor encode_jpeg_variable_quality(Tensor image, Tensor quality) | |||||
| { | |||||
| throw new NotImplementedException(""); | |||||
| } | |||||
| /// <summary> | /// <summary> | ||||
| /// PNG-encode an image. | /// PNG-encode an image. | ||||
| /// </summary> | /// </summary> | ||||
| @@ -28061,6 +28067,11 @@ namespace Tensorflow.Operations | |||||
| return op; | return op; | ||||
| } | } | ||||
| public static Tensor scale_and_translate (Tensor images_t, Tensor new_size, Tensor[] scale, Tensor zeroes, string kernel_type, bool antialias) | |||||
| { | |||||
| throw new NotImplementedException("scale_and_translate"); | |||||
| } | |||||
| /// <summary> | /// <summary> | ||||
| /// Outputs a <c>Summary</c> protocol buffer with scalar values. | /// Outputs a <c>Summary</c> protocol buffer with scalar values. | ||||
| /// </summary> | /// </summary> | ||||
| @@ -337,7 +337,7 @@ or rank = 4. Had rank = {0}", rank)); | |||||
| object hd, bbox_h_start; | object hd, bbox_h_start; | ||||
| if ((bool)h[1]) | if ((bool)h[1]) | ||||
| { | { | ||||
| hd = math_ops.cast(h[0], dtypes.float64); | |||||
| hd = math_ops.cast((IVariableV1)h[0], dtypes.float64); | |||||
| bbox_h_start = math_ops.cast(((int)hd - (int)hd * central_fraction) / 2, dtypes.int32); | bbox_h_start = math_ops.cast(((int)hd - (int)hd * central_fraction) / 2, dtypes.int32); | ||||
| } else | } else | ||||
| { | { | ||||
| @@ -348,7 +348,7 @@ or rank = 4. Had rank = {0}", rank)); | |||||
| object wd, bbox_w_start; | object wd, bbox_w_start; | ||||
| if ((bool)w[1]) | if ((bool)w[1]) | ||||
| { | { | ||||
| wd = math_ops.cast((RefVariable)w[0], dtypes.float64); | |||||
| wd = math_ops.cast((IVariableV1)w[0], dtypes.float64); | |||||
| bbox_w_start = math_ops.cast(((int)wd - (int)wd * central_fraction) / 2, dtypes.int32); | bbox_w_start = math_ops.cast(((int)wd - (int)wd * central_fraction) / 2, dtypes.int32); | ||||
| } else | } else | ||||
| { | { | ||||
| @@ -1743,7 +1743,7 @@ new_height, new_width"); | |||||
| public static Tensor crop_and_resize(Tensor image, Tensor boxes, Tensor box_ind, Tensor crop_size, string method, float extrapolation_value, string name) | public static Tensor crop_and_resize(Tensor image, Tensor boxes, Tensor box_ind, Tensor crop_size, string method, float extrapolation_value, string name) | ||||
| { | { | ||||
| var _op = tf._op_def_lib._apply_op_helper("CropAndResize", name: name, args: new | |||||
| var _op = tf.OpDefLib._apply_op_helper("CropAndResize", name: name, args: new | |||||
| { | { | ||||
| image, | image, | ||||
| boxes, | boxes, | ||||
| @@ -1780,8 +1780,8 @@ new_height, new_width"); | |||||
| Tensor score_threshold_tensor = ops.convert_to_tensor( | Tensor score_threshold_tensor = ops.convert_to_tensor( | ||||
| score_threshold, dtype: dtypes.float32, name: "score_threshold"); | score_threshold, dtype: dtypes.float32, name: "score_threshold"); | ||||
| return gen_image_ops.combined_non_max_suppression( | return gen_image_ops.combined_non_max_suppression( | ||||
| boxes, scores, max_output_size_per_class, max_total_size, iou_threshold, | |||||
| score_threshold, pad_per_class, clip_boxes); | |||||
| boxes, scores, max_output_size_per_class, max_total_size, iou_threshold_tensor, | |||||
| score_threshold_tensor, pad_per_class, clip_boxes); | |||||
| }); | }); | ||||
| } | } | ||||