|
- /**
- * Copyright 2019 Huawei Technologies Co., Ltd
- *
- * 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.
- */
-
- /*!
- * \file image_ops.h
- * \brief
- */
- #ifndef OPS_BUILT_IN_OP_PROTO_INC_IMAGE_OPS_H_
- #define OPS_BUILT_IN_OP_PROTO_INC_IMAGE_OPS_H_
-
- #include "graph/operator_reg.h"
-
- namespace ge {
-
- /**
- *@brief Adjust the hue of one or more images . \n
-
- *@par Inputs:
- *Input images is a tensor of at least 3 dimensions. The last dimension is
- interpretted as channels, and must be three. Inputs include:
- *@li images:A Tensor of type float. Images to adjust. At least 3-D.
- *@li delta:A Tensor of type float. A float delta to add to the hue . \n
-
- *@par Outputs:
- *y:A Tensor of type float . \n
-
- *@attention Constraints:
- *Input images is a tensor of at least 3 dimensions. The last dimension is
- interpretted as channels, and must be three . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow AdjustHue operator.
- */
-
- REG_OP(AdjustHue)
- .INPUT(images, TensorType({DT_FLOAT16,DT_FLOAT}))
- .INPUT(delta, TensorType({DT_FLOAT}))
- .OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT}))
- .OP_END_FACTORY_REG(AdjustHue)
-
- /**
- *@brief Adjust the saturation of one or more images . \n
-
- *@par Inputs:
- *Input images is a tensor of at least 3 dimensions. The last dimension is
- interpretted as channels, and must be three. Inputs include:
- *@li images:A Tensor of type float. Images to adjust. At least 3-D.
- *@li scale:A Tensor of type float. A float scale to add to the saturation . \n
-
- *@par Outputs:
- *y:A Tensor of type float . \n
-
- *@attention Constraints:
- *Input images is a tensor of at least 3 dimensions. The last dimension is
- interpretted as channels, and must be three . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow AdjustSaturation operator.
- */
-
- REG_OP(AdjustSaturation)
- .INPUT(images, TensorType({DT_FLOAT16,DT_FLOAT}))
- .INPUT(scale, TensorType({DT_FLOAT}))
- .OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT}))
- .OP_END_FACTORY_REG(AdjustSaturation)
-
- /**
- *@brief Adjust the contrast of one or more images . \n
-
- *@par Inputs:
- *Input images is a tensor of at least 3 dimensions. The last 3 dimensions are
- interpreted as '[height, width, channels]'. Inputs include:
- *@li images:A Tensor of type float. Images to adjust. At least 3-D.
- *@li scale:A Tensor of type float. A float multiplier for adjusting contrast . \n
-
- *@par Outputs:
- *y:A Tensor of type float . \n
-
- *@attention Constraints:
- *Input images is a tensor of at least 3 dimensions. The last dimension is
- interpretted as channels, and must be three . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow AdjustContrast operator.
- */
-
- REG_OP(AdjustContrast)
- .INPUT(images, TensorType({DT_FLOAT16,DT_FLOAT}))
- .INPUT(contrast_factor, TensorType({DT_FLOAT}))
- .OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT}))
- .OP_END_FACTORY_REG(AdjustContrast)
-
- /**
- *@brief Extracts crops from the input image tensor and resizes them. Extracts
- crops from the input image tensor and resizes them using bilinear sampling or
- nearest neighbor sampling to a common output size specified by crop_size . \n
-
- *@par Inputs:
- *Input images must be a 4-D tensor. Inputs include:
- *@li images:A Tensor. Must be one of the following types:uint8, uint16, int8,
- int16, int32, int64, float16, float, double. A 4-D tensor of shape
- [batch, image_height, image_width, depth].
- *@li boxes: A Tensor of type float. A 2-D tensor of shape [num_boxes, 4].
- *@li box_index: A Tensor of type int32. A 1-D tensor of shape [num_boxes] with
- int32 values in [0, batch).
- *@li crop_size: A Tensor of type int32. A 1-D tensor of 2 elements, crop_size
- = [crop_height, crop_width]. All cropped image patches are resized to this size . \n
-
- *@par Attributes:
- *@li extrapolation_value: An optional float. Defaults to 0. Value used for
- extrapolation, when applicable.
- *@li method: An optional string from: '"bilinear", "nearest"'. Defaults to
- "bilinear". Currently two sampling methods are supported: Bilinear and
- NearestNeighbor . \n
-
- *@par Outputs:
- *y:A Tensor of type float . \n
-
- *@attention Constraints:
- *Input images must be a 4-D tensor . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow CropAndResize operator.
- */
-
- REG_OP(CropAndResize)
- .INPUT(x, TensorType({DT_UINT8, DT_UINT16, DT_INT8, \
- DT_INT16, DT_INT32, DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .INPUT(boxes, TensorType({DT_FLOAT}))
- .INPUT(box_index, TensorType({DT_INT32}))
- .INPUT(crop_size, TensorType({DT_INT32}))
- .OUTPUT(y, TensorType({DT_FLOAT}))
- .ATTR(extrapolation_value, Float, 0)
- .ATTR(method, String, "bilinear")
- .OP_END_FACTORY_REG(CropAndResize)
-
- /**
- *@brief Extracts crops from the input image tensor and resizes them.
- * Extracts crops from the input image tensor and resizes them using bilinear sampling or
- * nearest neighbor sampling to a common output size specified by crop_size . \n
-
- *@par Inputs:
- *Input images must be a 5HD tensor. Inputs include:
- *@li x:A Tensor. Must be one of the following types:float16, float. A 5HD tensor of shape
- * [batch, C1, image_height, image_width, C0].
- *@li boxes: A Tensor of type float. A 2-D tensor of shape [num_boxes, 4].
- *@li box_index: A Tensor of type int32. A 1-D tensor of shape [num_boxes] with int32 values in [0, batch) . \n
-
- *@par Attributes:
- *@li crop_size: list int. [crop_height, crop_width]. All cropped image patches are resized to this size.
- *@li extrapolation_value: An optional float. Defaults to 0. Value used for extrapolation, when applicable.
- *@li method: An optional string from: '"bilinear"'. Defaults to "bilinear" . \n
-
- *@par Outputs:
- *y:A Tensor of type float . \n
-
- *@attention Constraints:
- *Input images must be a 5HD tensor . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow CropAndResize operator.
-
- * @par Restrictions:
- * Warning: THIS FUNCTION IS DEPRECATED. Please use CropAndResize instead.
- */
- REG_OP(CropAndResizeD)
- .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(boxes, TensorType({DT_FLOAT}))
- .INPUT(box_index, TensorType({DT_INT32}))
- .OUTPUT(y, TensorType({DT_FLOAT}))
- .REQUIRED_ATTR(crop_size, ListInt)
- .ATTR(extrapolation_value, Float, 0)
- .ATTR(method, String, "bilinear")
- .OP_END_FACTORY_REG(CropAndResizeD)
-
- /**
- *@brief Computes the gradient of the crop_and_resize op wrt the input
- boxes tensor . \n
-
- *@par Inputs:
- *Input images and grads must be a 4-D tensor. Inputs include:
- *@li grads: A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth].
- *@li images: A 4-D tensor of shape [batch, image_height, image_width, depth].
- Both image_height and image_width need to be positive.
- *@li boxes: A 2-D tensor of shape [num_boxes, 4]. The i-th row of the tensor
- specifies the coordinates of a box in the box_ind[i] image and is specified in
- normalized coordinates [y1, x1, y2, x2].
- *@li box_index: A 1-D tensor of shape [num_boxes] with int32 values in
- [0, batch). The value of box_ind[i] specifies the image that the i-th box
- refers to . \n
-
- *@par Attributes:
- method: A string specifying the interpolation method. Only 'bilinear' is
- supported for now . \n
-
- *@par Outputs:
- *y:A 2-D tensor of shape [num_boxes, 4] . \n
-
- *@attention Constraints:
- *Input images and grads must be a 4-D tensor . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow CropAndResizeGradBoxes operator.
- */
-
- REG_OP(CropAndResizeGradBoxes)
- .INPUT(grads, TensorType({DT_FLOAT}))
- .INPUT(images, TensorType({DT_UINT8, DT_UINT16, DT_INT8, DT_INT16, \
- DT_INT32, DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .INPUT(boxes, TensorType({DT_FLOAT}))
- .INPUT(box_index, TensorType({DT_INT32}))
- .OUTPUT(y, TensorType({DT_FLOAT}))
- .ATTR(method, String, "bilinear")
- .OP_END_FACTORY_REG(CropAndResizeGradBoxes)
-
- /**
- *@brief Computes the gradient of the crop_and_resize op wrt the input
- images tensor . \n
-
- *@par Inputs:
- *Input grads must be a 4-D tensor. Inputs include:
- *@li grads: A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth].
- *@li boxes: A 2-D tensor of shape [num_boxes, 4]. The i-th row of the tensor
- specifies the coordinates of a box in the box_ind[i] image and is specified
- in normalized coordinates [y1, x1, y2, x2].
- *@li box_index: A 1-D tensor of shape [num_boxes] with int32 values in
- [0, batch). The value of box_ind[i] specifies the image that the i-th box
- refers to.
- *@li image_size: A 1-D tensor with value [batch, image_height, image_width,
- depth] containing the original image size. Both image_height and image_width
- need to be positive . \n
-
- *@par Attributes:
- method: A string specifying the interpolation method. Only 'bilinear' is
- supported for now . \n
-
- *@par Outputs:
- *y:A 4-D tensor of shape [batch, image_height, image_width, depth] . \n
-
- *@attention Constraints:
- *Input grads must be a 4-D tensor . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow CropAndResizeGradImage operator.
- */
-
- REG_OP(CropAndResizeGradImage)
- .INPUT(grads, TensorType({DT_FLOAT}))
- .INPUT(boxes, TensorType({DT_FLOAT}))
- .INPUT(box_index, TensorType({DT_INT32}))
- .INPUT(image_size, TensorType({DT_INT32}))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .ATTR(method, String, "bilinear")
- .REQUIRED_ATTR(T, Type)
- .OP_END_FACTORY_REG(CropAndResizeGradImage)
-
- /**
- *@brief Extracts a glimpse from the input tensor . \n
-
- *@par Inputs:
- *Input x must be a 4-D tensor. Inputs include:
- *@li x: A 4-D float tensor of shape [batch_size, height, width, channels].
- *@li size: A 1-D tensor of 2 elements containing the size of the glimpses to
- extract. The glimpse height must be specified first, following by the glimpse
- width.
- *@li offsets: A 2-D integer tensor of shape [batch_size, 2] containing the y,
- x locations of the center of each window . \n
-
- *@par Attributes:
- *@li centered: indicates if the offset coordinates are centered relative to
- the image, in which case the (0, 0) offset is relative to the center of the
- input images. If false, the (0,0) offset corresponds to the upper left corner
- of the input images.
- *@li normalized: indicates if the offset coordinates are normalized.
- *@li uniform_noise: indicates if the noise should be generated using a
- uniform distribution or a Gaussian distribution.
- *@li noise: indicates if the noise should uniform, gaussian, or zero.
- The default is uniform which means the the noise type will be decided by
- uniform_noise . \n
-
- *@par Outputs:
- *y:A tensor representing the glimpses [batch_size, glimpse_height,
- glimpse_width, channels] . \n
-
- *@attention Constraints:
- *Input x must be a 4-D tensor . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow CropAndResizeGradImage operator.
- */
-
- REG_OP(ExtractGlimpse)
- .INPUT(x, TensorType({DT_FLOAT}))
- .INPUT(size, TensorType({DT_INT32}))
- .INPUT(offsets, TensorType({DT_FLOAT}))
- .OUTPUT(y, TensorType({DT_FLOAT}))
- .ATTR(centered, Bool, true)
- .ATTR(normalized, Bool, true)
- .ATTR(uniform_noise, Bool, true)
- .ATTR(noise, String, "uniform")
- .OP_END_FACTORY_REG(ExtractGlimpse)
-
- /**
- *@brief Convert one or more images from HSV to RGB . \n
-
- *@par Inputs:
- *Last dimension of input x must be size 3. Inputs include:
- *images: 1-D or higher rank. HSV data to convert. Last dimension must be size 3 . \n
-
- *@par Outputs:
- *y:images converted to RGB . \n
-
- *@attention Constraints:
- *Last dimension of input x must be size 3 . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow HSVToRGB operator.
- */
-
- REG_OP(HSVToRGB)
- .INPUT(images, TensorType({DT_FLOAT16,DT_FLOAT,DT_DOUBLE}))
- .OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT,DT_DOUBLE}))
- .OP_END_FACTORY_REG(HSVToRGB)
-
- /**
- *@brief Resize quantized images to size using quantized bilinear interpolation . \n
-
- *@par Inputs:
- *Input images must be a 4-D tensor. Inputs include:
- *@li images: 4-D with shape [batch, height, width, channels].
- *@li size: A 1-D int32 Tensor of 2 elements: new_height, new_width. The new
- size for the images.
- *@li min: A Tensor of type float.
- *@li max: A Tensor of type float . \n
-
- *@par Attributes:
- *@li align_corners: An optional bool. Defaults to False. If true, the centers
- of the 4 corner pixels of the input and output tensors are aligned, preserving
- the values at the corner pixels. Defaults to false.
- *@li half_pixel_centers: indicates if the offset coordinates are normalized . \n
-
- *@par Outputs:
- *@li resized_images: 4-D with shape [batch, new_height, new_width, channels].
- *@li y_min: A Tensor of type float.
- *@li y_max: A Tensor of type float . \n
-
- *@attention Constraints:
- *Input images and output images must be quantized types . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow QuantizedResizeBilinear operator.
- */
-
- REG_OP(QuantizedResizeBilinear)
- .INPUT(images, TensorType({DT_QUINT8,DT_QINT32,DT_FLOAT}))
- .INPUT(size, TensorType({ DT_INT32 }))
- .INPUT(min, TensorType({ DT_FLOAT }))
- .INPUT(max, TensorType({ DT_FLOAT }))
- .OUTPUT(resized_images, TensorType({DT_QUINT8,DT_QINT32,DT_FLOAT }))
- .OUTPUT(y_min, TensorType({ DT_FLOAT }))
- .OUTPUT(y_max, TensorType({ DT_FLOAT }))
- .ATTR(align_corners, Bool, false)
- .ATTR(half_pixel_centers, Bool, false)
- .OP_END_FACTORY_REG(QuantizedResizeBilinear)
-
- /**
- *@brief Resize images to size using area interpolation . \n
-
- *@par Inputs:
- *Input images must be a 4-D tensor. Inputs include:
- *@li images: 4-D with shape [batch, height, width, channels].
- *@li size: A 1-D int32 Tensor of 2 elements: new_height, new_width.
- The new size for the images . \n
-
- *@par Attributes:
- *align_corners: If true, the centers of the 4 corner pixels of the input and
- output tensors are aligned, preserving the values at the corner pixels.
- Defaults to false . \n
-
- *@par Outputs:
- *y: 4-D with shape [batch, new_height, new_width, channels] . \n
-
- *@attention Constraints:
- *Input images can be of different types but output images are always float . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow ResizeArea operator.
- */
-
- REG_OP(ResizeArea)
- .INPUT(images, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, \
- DT_INT32, DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .INPUT(size, TensorType({DT_INT32}))
- .OUTPUT(y, TensorType({DT_FLOAT}))
- .ATTR(align_corners, Bool, false)
- .OP_END_FACTORY_REG(ResizeArea)
-
- /**
- *@brief Computes the gradient of bicubic interpolation . \n
-
- *@par Inputs:
- *Input grads must be a 4-D tensor. Inputs include:
- *@li grads: A Tensor of type float. 4-D with shape [batch, height, width,
- channels].
- *@li original_image: A Tensor. Must be one of the following types: float,
- double. 4-D with shape [batch, orig_height, orig_width, channels], The image
- tensor that was resized . \n
-
- *@par Attributes:
- *@li align_corners: An optional bool. Defaults to False. If true, the centers
- of the 4 corner pixels of the input and grad tensors are aligned. Defaults to
- false.
- *@li half_pixel_centers: An optional bool. Defaults to False . \n
-
- *@par Outputs:
- *y: A Tensor. Has the same type as original_image . \n
-
- *@attention Constraints:
- *Input images can be of different types but output images are always float . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow ResizeBicubicGrad operator.
- */
-
- REG_OP(ResizeBicubicGrad)
- .INPUT(grads, TensorType({DT_FLOAT}))
- .INPUT(original_image, TensorType({DT_FLOAT, DT_DOUBLE}))
- .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
- .ATTR(align_corners, Bool, false)
- .ATTR(half_pixel_centers, Bool, false)
- .OP_END_FACTORY_REG(ResizeBicubicGrad)
-
- /**
- *@brief Resize images to size using bicubic interpolation . \n
-
- *@par Inputs:
- *Input images must be a 4-D tensor. Inputs include:
- *@li images: 4-D with shape [batch, height, width, channels].
- *@li size: A 1-D int32 Tensor of 2 elements: new_height, new_width. The new
- size for the images . \n
-
- *@par Attributes:
- *@li align_corners: If true, the centers of the 4 corner pixels of the input
- and output tensors are aligned, preserving the values at the corner pixels.
- Defaults to false.
- *@li half_pixel_centers: An optional bool. Defaults to False . \n
-
- *@par Outputs:
- *y: 4-D with shape [batch, new_height, new_width, channels] . \n
-
- *@attention Constraints:
- *Input images can be of different types but output images are always float . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow ResizeBicubic operator.
- */
-
- REG_OP(ResizeBicubic)
- .INPUT(images, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, \
- DT_INT32, DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .INPUT(size, TensorType({DT_INT32}))
- .OUTPUT(y, TensorType({DT_FLOAT}))
- .ATTR(align_corners, Bool, false)
- .ATTR(half_pixel_centers, Bool, false)
- .OP_END_FACTORY_REG(ResizeBicubic)
-
- /**
- *@brief Computes the gradient of nearest neighbor interpolation . \n
-
- *@par Inputs:
- *Input grads must be a 4-D tensor. Inputs include:
- *@li grads: A Tensor. Must be one of the following types: uint8, int8, int32,
- float16, float, double. 4-D with shape [batch, height, width, channels].
- *@li size: A 1-D int32 Tensor of 2 elements: orig_height, orig_width.
- The original input size . \n
-
- *@par Attributes:
- *@li align_corners: An optional bool. Defaults to False. If true, the centers
- of the 4 corner pixels of the input and grad tensors are aligned. Defaults to
- false.
- *@li half_pixel_centers: An optional bool. Defaults to False . \n
-
- *@par Outputs:
- *y: A Tensor. Has the same type as grads . \n
-
- *@attention Constraints:
- *Input grads must be a 4-D tensor . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow ResizeNearestNeighborV2Grad operator.
- */
-
- REG_OP(ResizeNearestNeighborV2Grad)
- .INPUT(grads, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, DT_INT32,
- DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .INPUT(size, TensorType({DT_INT32}))
- .OUTPUT(y, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, DT_INT32,
- DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .ATTR(align_corners, Bool, false)
- .ATTR(half_pixel_centers, Bool, false)
- .OP_END_FACTORY_REG(ResizeNearestNeighborV2Grad)
-
- /**
- *@brief Computes the gradient of nearest neighbor interpolation . \n
-
- *@par Inputs:
- *Input grads must be a 4-D tensor. Inputs include:
- *grads: A Tensor. 4-D with shape [batch, height, width, channels].
-
-
- *@par Attributes:
- *@li align_corners: An optional bool. Defaults to False. If true, the centers
- of the 4 corner pixels of the input and grad tensors are aligned. Defaults to
- false.
- *@li size: An list type. Specify the images size . \n
-
- *@par Outputs:
- *y: A Tensor. Has the same type as grads . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow ResizeNearestNeighborV2GradD operator.
- *
- * @par Restrictions:
- * Warning: THIS FUNCTION IS DEPRECATED. Please use ResizeNearestNeighborV2Grad instead.
- */
-
- REG_OP(ResizeNearestNeighborV2GradD)
- .INPUT(grads, TensorType({DT_FLOAT}))
- .OUTPUT(y, TensorType({DT_FLOAT}))
- .REQUIRED_ATTR(size, ListInt)
- .ATTR(align_corners, Bool, false)
- .ATTR(half_pixel_centers, Bool, false)
- .OP_END_FACTORY_REG(ResizeNearestNeighborV2GradD)
-
- /**
- *@brief Computes the gradient of bilinear interpolation . \n
-
- *@par Inputs:
- *Input grads must be a 4-D tensor. Inputs include:
- *@li grads: A Tensor of type float32. 4-D with shape [batch, height, width,
- channels].
- *@li original_image: A Tensor. 4-D with shape [batch, orig_height, orig_width,
- channels], The image tensor that was resized . \n
-
- *@par Attributes:
- *align_corners: An optional bool. Defaults to False. If true, the centers of
- the 4 corner pixels of the input and grad tensors are aligned. Defaults to
- false . \n
-
- *@par Outputs:
- *y: A Tensor. Has the same type as original_image . \n
-
- *@attention Constraints:
- *Input grads must be a 4-D tensor . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow ResizeBilinearV2Grad operator.
- */
-
- REG_OP(ResizeBilinearV2Grad)
- .INPUT(grads, TensorType({DT_FLOAT}))
- .INPUT(original_image, TensorType::FloatingDataType())
- .OUTPUT(y, TensorType({DT_FLOAT}))
- .ATTR(align_corners, Bool, false)
- .ATTR(half_pixel_centers, Bool, false)
- .OP_END_FACTORY_REG(ResizeBilinearV2Grad)
-
- /**
- *@brief Resize images to size using bilinear interpolation . \n
-
- *@par Inputs:
- *Input images must be a 4-D tensor. Inputs include:
- *@li x: 4-D with shape [batch, height, width, channels].
- *@li size: A 1-D int32 Tensor of 2 elements: new_height, new_width. The new
- size for the images . \n
-
- *@par Attributes:
- *align_corners: If true, the centers of the 4 corner pixels of the input and
- output tensors are aligned, preserving the values at the corner pixels.
- Defaults to false . \n
-
- *@par Outputs:
- *y: 4-D with shape [batch, new_height, new_width, channels] . \n
-
- *@attention Constraints:
- *Input images can be of different types but output images are always float . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow ResizeBilinearV2 operator.
- */
-
- REG_OP(ResizeBilinearV2)
- .INPUT(x, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16,
- DT_INT32, DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .INPUT(size, TensorType({DT_INT32}))
- .OUTPUT(y, TensorType({DT_FLOAT}))
- .ATTR(align_corners, Bool, false)
- .ATTR(half_pixel_centers, Bool, false)
- .OP_END_FACTORY_REG(ResizeBilinearV2)
-
- /**
- *@brief Converts one or more images from RGB to HSV . \n
-
- *@par Inputs:
- *Last dimension of input images must be size 3. Inputs include:
- *images: A Tensor. Must be one of the following types: float, double. 1-D or
- higher rank. RGB data to convert. Last dimension must be size 3 . \n
-
- *@par Outputs:
- *y: A Tensor. Has the same type as images . \n
-
- *@attention Constraints:
- *Outputs a tensor of the same shape as the images tensor, containing the HSV
- value of the pixels. The output is only well defined if the value in images
- are in [0,1] . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow RGBToHSV operator.
- */
-
- REG_OP(RGBToHSV)
- .INPUT(images, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE }))
- .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE }))
- .OP_END_FACTORY_REG(RGBToHSV)
-
- /**
- *@brief Generate a single randomly distorted bounding box for an image . \n
-
- *@par Inputs:
- *Input images must be a 4-D tensor. Inputs include:
- *@li image_size: 1-D, containing [height, width, channels].
- *@li bounding_boxes: 3-D with shape [batch, N, 4] describing the N bounding
- boxes associated with the image.
- *@li min_object_covered: The cropped area of the image must contain at least
- this fraction of any bounding box supplied. The value of this parameter should
- be non-negative. In the case of 0, the cropped area does not need to overlap
- any of the bounding boxes supplied . \n
-
- *@par Attributes:
- *@li seed: If either seed or seed2 are set to non-zero, the random number
- generator is seeded by the given seed. Otherwise, it is seeded by a random seed.
- *@li seed2: A second seed to avoid seed collision.
- *@li aspect_ratio_range: The cropped area of the image must have an aspect
- ratio = width / height within this range.
- *@li max_attempts: Number of attempts at generating a cropped region of the
- image of the specified constraints. After max_attempts failures, return the
- entire image.
- *@li use_image_if_no_bounding_boxes: Controls behavior if no bounding boxes
- supplied. If true, assume an implicit bounding box covering the whole input.
- If false, raise an error . \n
-
- *@par Outputs:
- *@li begin: 1-D, containing [offset_height, offset_width, 0].
- *@li size: 1-D, containing [target_height, target_width, -1].
- *@li bboxes: 3-D with shape [1, 1, 4] containing the distorted bounding box . \n
-
- *@attention Constraints:
- *Input images can be of different types but output images are always float . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow SampleDistortedBoundingBoxExt2 operator.
- */
-
- REG_OP(SampleDistortedBoundingBoxExt2)
- .INPUT(image_size, TensorType({ DT_UINT8, DT_INT8, DT_INT16, \
- DT_INT32, DT_INT64 }))
- .INPUT(bounding_boxes, TensorType({ DT_FLOAT }))
- .INPUT(min_object_covered, TensorType({ DT_FLOAT }))
- .OUTPUT(begin, TensorType({ DT_UINT8, DT_INT8, DT_INT16, \
- DT_INT32, DT_INT64 }))
- .OUTPUT(size, TensorType({ DT_UINT8, DT_INT8, DT_INT16, \
- DT_INT32, DT_INT64 }))
- .OUTPUT(bboxes, TensorType({ DT_FLOAT }))
- .ATTR(seed, Int, 0)
- .ATTR(seed2, Int, 0)
- .ATTR(aspect_ratio_range, ListFloat, { 0.75f, 1.33f })
- .ATTR(area_range, ListFloat, { 0.05f, 1.0f })
- .ATTR(max_attempts, Int, 100)
- .ATTR(use_image_if_no_bounding_boxes, Bool, false)
- .OP_END_FACTORY_REG(SampleDistortedBoundingBoxExt2)
-
- /**
- *@brief Resize images to size using nearest neighbor interpolation . \n
-
- *@par Inputs:
- *Input x must be a 4-D tensor. Inputs include:
- *@li x: 4-D with shape [batch, height, width, channels].
- *@li size: A 1-D int32 Tensor of 2 elements: new_height, new_width.
- The new size for the images . \n
-
- *@par Attributes:
- *align_corners: If true, the centers of the 4 corner pixels of the input and
- output tensors are aligned, preserving the values at the corner pixels.
- Defaults to false . \n
-
- *@par Outputs:
- *y: 4-D with shape [batch, new_height, new_width, channels] . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow ResizeNearestNeighborV2 operator.
- */
-
- REG_OP(ResizeNearestNeighborV2)
- .INPUT(x, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, DT_INT32,
- DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .INPUT(size, TensorType({DT_INT32}))
- .OUTPUT(y, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, DT_INT32,
- DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .ATTR(align_corners, Bool, false)
- .ATTR(half_pixel_centers, Bool, false)
- .OP_END_FACTORY_REG(ResizeNearestNeighborV2)
-
- /**
- *@brief Draw bounding boxes on a batch of images . \n
-
- *@par Inputs:
- *Input images must be a 4-D tensor. Inputs include:
- *@li images: A Tensor. Must be one of the following types: float. 4-D with
- shape [batch, height, width, depth]. A batch of images.
- *@li boxes: A Tensor of type float32. 3-D with shape [batch,
- num_bounding_boxes, 4] containing bounding boxes . \n
-
- *@par Outputs:
- *A Tensor. Has the same type as images . \n
-
- *@attention Constraints:
- *Input images must be a 4-D tensor . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow DrawBoundingBoxes operator.
- */
-
- REG_OP(DrawBoundingBoxes)
- .INPUT(images, TensorType({DT_FLOAT}))
- .INPUT(boxes, TensorType({DT_FLOAT}))
- .OUTPUT(y, TensorType({DT_FLOAT}))
- .OP_END_FACTORY_REG(DrawBoundingBoxes)
-
- /**
- *@brief Greedily selects a subset of bounding boxes in descending order of
- score . \n
-
- *@par Inputs:
- *Input boxes and scores must be float type. Inputs include:
- *@li boxes: A 2-D float tensor of shape [num_boxes, 4].
- *@li scores: A 1-D float tensor of shape [num_boxes] representing a single
- score corresponding to each box (each row of boxes).
- *@li max_output_size: A scalar integer tensor representing the maximum number
- of boxes to be selected by non max suppression . \n
-
- *@par Attributes:
- *iou_threshold: A float representing the threshold for deciding whether boxes
- overlap too much with respect to IOU . \n
-
- *@par Outputs:
- *selected_indices: A 1-D integer tensor of shape [M] representing the selected
- indices from the boxes tensor, where M <= max_output_size . \n
-
- *@attention Constraints:
- *Input boxes and scores must be float type . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow NonMaxSuppression operator.
- */
-
- REG_OP(NonMaxSuppression)
- .INPUT(boxes, TensorType({DT_FLOAT}))
- .INPUT(scores, TensorType({DT_FLOAT}))
- .INPUT(max_output_size, TensorType({DT_INT32}))
- .OUTPUT(selected_indices, TensorType({DT_INT32}))
- .ATTR(iou_threshold, Float, 0.5f)
- .OP_END_FACTORY_REG(NonMaxSuppression)
-
- /**
- *@brief Greedily selects a subset of bounding boxes in descending order of
- score . \n
-
- *@par Inputs:
- *Input boxes and scores must be float type. Inputs include:
- *@li boxes: A 2-D float tensor of shape [num_boxes, 4].
- *@li scores: A 1-D float tensor of shape [num_boxes] representing a single
- score corresponding to each box (each row of boxes).
- *@li max_output_size: A scalar integer tensor representing the maximum number
- of boxes to be selected by non max suppression.
- *@li iou_threshold: A 0-D float tensor representing the threshold for deciding
- whether boxes overlap too much with respect to IOU . \n
-
- *@par Outputs:
- *selected_indices: A 1-D integer tensor of shape [M] representing the selected
- indices from the boxes tensor, where M <= max_output_size . \n
-
- *@attention Constraints:
- *Input boxes and scores must be float type . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow NonMaxSuppressionV2 operator.
- */
-
- REG_OP(NonMaxSuppressionV2)
- .INPUT(boxes, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(scores, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(max_output_size, TensorType({DT_INT32}))
- .INPUT(iou_threshold, TensorType({DT_FLOAT16,DT_FLOAT}))
- .OUTPUT(selected_indices, TensorType({DT_INT32}))
- .OP_END_FACTORY_REG(NonMaxSuppressionV2)
-
- /**
- *@brief Greedily selects a subset of bounding boxes in descending order of
- score . \n
-
- *@par Inputs:
- *Input boxes and scores must be float type. Inputs include:
- *@li boxes: A 2-D float tensor of shape [num_boxes, 4].
- *@li scores: A 1-D float tensor of shape [num_boxes] representing a single
- score corresponding to each box (each row of boxes).
- *@li max_output_size: A scalar integer tensor representing the maximum number
- of boxes to be selected by non max suppression.
- *@li iou_threshold: A 0-D float tensor representing the threshold for deciding
- whether boxes overlap too much with respect to IOU.
- *@li score_threshold: A 0-D float tensor representing the threshold for
- deciding when to remove boxes based on score . \n
-
- *@par Outputs:
- *selected_indices: A 1-D integer tensor of shape [M] representing the selected
- indices from the boxes tensor, where M <= max_output_size . \n
-
- *@attention Constraints:
- *Input boxes and scores must be float type . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow NonMaxSuppressionV3 operator.
- */
-
- REG_OP(NonMaxSuppressionV3)
- .INPUT(boxes, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(scores, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(max_output_size, TensorType({DT_INT32}))
- .INPUT(iou_threshold, TensorType({DT_FLOAT16,DT_FLOAT}))
- .INPUT(score_threshold, TensorType({DT_FLOAT16,DT_FLOAT}))
- .OUTPUT(selected_indices, TensorType({DT_INT32}))
- .OP_END_FACTORY_REG(NonMaxSuppressionV3)
-
- /**
- *@brief Greedily selects a subset of bounding boxes in descending order of
- score . \n
-
- *@par Inputs:
- *Input boxes and scores must be float type. Inputs include:
- *@li boxes: A 2-D float tensor of shape [num_boxes, 4].
- *@li scores: A 1-D float tensor of shape [num_boxes] representing a single
- score corresponding to each box (each row of boxes).
- *@li max_output_size: A scalar integer tensor representing the maximum number
- of boxes to be selected by non max suppression.
- *@li iou_threshold: A 0-D float tensor representing the threshold for deciding
- whether boxes overlap too much with respect to IOU.
- *@li score_threshold: A 0-D float tensor representing the threshold for
- deciding when to remove boxes based on score . \n
-
- *@par Attributes:
- *pad_to_max_output_size: If true, the output selected_indices is padded
- to be of length max_output_size. Defaults to false . \n
-
- *@par Outputs:
- *@li selected_indices: A 1-D integer tensor of shape [M] representing the
- selected indices from the boxes tensor, where M <= max_output_size.
- *@li valid_outputs: A 0-D integer tensor representing the number of valid
- elements in selected_indices, with the valid elements appearing first . \n
-
- *@attention Constraints:
- *Input boxes and scores must be float type . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow NonMaxSuppressionV4 operator.
- */
-
- REG_OP(NonMaxSuppressionV4)
- .INPUT(boxes, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(scores, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(max_output_size, TensorType({DT_INT32}))
- .INPUT(iou_threshold, TensorType({DT_FLOAT16,DT_FLOAT}))
- .INPUT(score_threshold, TensorType({DT_FLOAT16,DT_FLOAT}))
- .OUTPUT(selected_indices, TensorType({DT_INT32}))
- .OUTPUT(valid_outputs, TensorType({DT_INT32}))
- .ATTR(pad_to_max_output_size, Bool, false)
- .OP_END_FACTORY_REG(NonMaxSuppressionV4)
-
- /**
- *@brief Greedily selects a subset of bounding boxes in descending order of
- score . \n
-
- *@par Inputs:
- *Input overlaps and scores must be float type. Inputs include:
- *@li overlaps: A 2-D float tensor of shape [num_boxes, num_boxes]
- representing the n-by-n box overlap values.
- *@li scores: A 1-D float tensor of shape [num_boxes] representing a single
- score corresponding to each box (each row of boxes).
- *@li max_output_size: A scalar integer tensor representing the maximum number
- of boxes to be selected by non max suppression.
- *@li overlap_threshold: A 0-D float tensor representing the threshold for
- deciding whether boxes overlap too.
- *@li score_threshold: A 0-D float tensor representing the threshold for
- deciding when to remove boxes based on score . \n
-
- *@par Attributes:
- *pad_to_max_output_size: If true, the output selected_indices is padded
- to be of length max_output_size. Defaults to false . \n
-
- *@par Outputs:
- *selected_indices: A 1-D integer tensor of shape [M] representing the
- selected indices from the boxes tensor, where M <= max_output_size . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow NonMaxSuppressionWithOverlaps operator.
- */
-
- REG_OP(NonMaxSuppressionWithOverlaps)
- .INPUT(overlaps, TensorType({DT_FLOAT}))
- .INPUT(scores, TensorType({DT_FLOAT}))
- .INPUT(max_output_size, TensorType({DT_INT32}))
- .INPUT(overlap_threshold, TensorType({DT_FLOAT}))
- .INPUT(score_threshold, TensorType({DT_FLOAT}))
- .OUTPUT(selected_indices, TensorType({DT_INT32}))
- .OP_END_FACTORY_REG(NonMaxSuppressionWithOverlaps)
-
- /**
- *@brief JPEG-encode an image . \n
-
- *@par Inputs:
- *Input image must be unit8 type. Inputs include:
- *image: A 3-D uint8 Tensor of shape [height, width, channels] . \n
-
- *@par Attributes:
- *@li format: Per pixel image format.
- *@li quality: Quality of the compression from 0 to 100 (higher is better
- and slower).
- *@li progressive: If True, create a JPEG that loads progressively (coarse
- to fine).
- *@li optimize_size: If True, spend CPU/RAM to reduce size with no quality
- change.
- *@li chroma_downsampling: A boolean, default is true.
- *@li density_unit: Unit used to specify x_density and y_density: pixels per
- inch ('in') or centimeter ('cm').
- *@li x_density: Horizontal pixels per density unit.
- *@li y_density: Vertical pixels per density unit.
- *@li xmp_metadata: If not empty, embed this XMP metadata in the image header . \n
-
- *@par Outputs:
- *contents: 0-D. JPEG-encoded image . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow EncodeJpeg operator.
- */
-
- REG_OP(EncodeJpeg)
- .INPUT(image, TensorType({DT_UINT8}))
- .OUTPUT(contents, TensorType({DT_STRING}))
- .ATTR(format, String, "")
- .ATTR(quality, Int, 95)
- .ATTR(progressive, Bool, false)
- .ATTR(optimize_size, Bool, false)
- .ATTR(chroma_downsampling, Bool, true)
- .ATTR(density_unit, String, "in")
- .ATTR(x_density, Int, 300)
- .ATTR(y_density, Int, 300)
- .ATTR(xmp_metadata, String, "")
- .OP_END_FACTORY_REG(EncodeJpeg)
-
- /**
- *@brief PNG-encode an image.
- *@par Inputs:
- *Input image must be unit8 or uint16 type. Inputs include:
- *image: is a 3-D uint8 or uint16 Tensor of shape [height, width, channels]
- where channels is: 1: for grayscale; 2: for grayscale + alpha; 3: for RGB;
- 4: for RGBA . \n
-
- *@par Attributes:
- *compression: Compression level . \n
-
- *@par Outputs:
- *contents: 0-D. PNG-encoded image . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow EncodePng operator.
- */
-
- REG_OP(EncodePng)
- .INPUT(image, TensorType({DT_UINT8, DT_UINT16}))
- .OUTPUT(contents, TensorType({DT_STRING}))
- .ATTR(compression, Int, -1)
- .OP_END_FACTORY_REG(EncodePng)
-
- /**
- *@brief Resizes "images" to "size" using bilinear interpolation . \n
-
- *@par Inputs:
- * One input:
- *x: An NC1HWC0 Tensor.
- * Must be one of the following types: float16, float32 . \n
-
- *@par Attributes:
- *@li size: A required int32 Tensor specifying the new size for the images.
- No default value.
- *@li align_corners: An optional bool. If "true", the centers of the corner
- pixels of the input and output tensors are aligned. Defaults to "false" . \n
-
- *@par Outputs:
- *y: A Tensor with type float32 and the same format as input "images" . \n
-
- *@attention Constraints:
- *@li The input "size" must be a tensor of 2 elements: size[0] <= 2048,
- size[1] <= 2048.
- *@li The input "images" must be a tensor of 5 elements: images[2] <= 2048,
- images[3] <= 2048 . \n
-
- *@par Third-party framework compatibility
- * Compatible with TensorFlow operator ResizeBilinearV2D.
- *
- * @par Restrictions:
- * Warning: THIS FUNCTION IS DEPRECATED. Please use ResizeBilinearV2 instead.
- */
- REG_OP(ResizeBilinearV2D)
- .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(y, TensorType({DT_FLOAT}))
- .ATTR(align_corners, Bool, false)
- .ATTR(half_pixel_centers, Bool, false)
- .REQUIRED_ATTR(size, ListInt)
- .OP_END_FACTORY_REG(ResizeBilinearV2D)
-
- /**
- *@brief Resizes "images" to "size" using bilinear interpolation and keep ratio at the time. \n
-
- *@par Inputs:
- * One input:
- *images: An NC1HWC0 Tensor.
- * Must be one of the following types: float16, float32 . \n
-
- *@par Attributes:
- *@li min_dimension: A required int32 attribute for the min dimension for the images.
- * No default value.
- *@li max_dimension: A required int32 attribute for the max dimension for the images.
- * No default value.
- *@li align_corners: An optional bool. If "true", the centers of the corner
- * pixels of the input and output tensors are aligned. Defaults to "false".
- *@li half_pixel_centers: indicates if the offset coordinates are normalized
- * Defaults to "false" . \n
-
- *@par Outputs:
- *y: A Tensor with type float32 and the same format as input "images" . \n
-
- *@attention Constraints:
- * The input "images" must be a tensor of 5 elements: images[2] <= 2048,
- images[3] <= 2048.
- */
- REG_OP(KeepRatioResizeBilinear)
- .INPUT(images, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(y, TensorType({DT_FLOAT}))
- .REQUIRED_ATTR(min_dimension, Int)
- .REQUIRED_ATTR(max_dimension, Int)
- .ATTR(align_corners, Bool, false)
- .ATTR(half_pixel_centers, Bool, false)
- .OP_END_FACTORY_REG(KeepRatioResizeBilinear)
-
- /**
- *@brief Resizes "images" to "size" using nearest neighbor interpolation . \n
-
- *@par Inputs:
- * One input:
- *x: An NC1HWC0 Tensor.
- * Must be one of the following types: float16, float32, int32, int8, uint8
-
- *@par Attributes:
- *@li size: A required int32 Tensor specifying the new size for the images.
- No default value.
- *@li align_corners: An optional bool. If "true", the centers of the corner
- pixels of the input and output tensors are aligned. Defaults to "false" . \n
-
- *@par Outputs:
- *y: A Tensor with the same type and format as input "images" . \n
-
- *@attention Constraints:
- * The input "size" must be a tensor of 2 elements: size[0] <= 7680,
- size[1] <= 4320
-
- *@par Third-party framework compatibility
- * Compatible with TensorFlow operator ResizeNearestNeighborV2.
- *
- * @par Restrictions:
- * Warning: THIS FUNCTION IS DEPRECATED. Please use ResizeNearestNeighborV2 instead.
- */
- REG_OP(ResizeNearestNeighborV2D)
- .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
- .OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
- .REQUIRED_ATTR(size, ListInt)
- .ATTR(align_corners, Bool, false)
- .ATTR(half_pixel_centers, Bool, false)
- .OP_END_FACTORY_REG(ResizeNearestNeighborV2D)
-
- /**
- *@brief Extract the shape information of a JPEG-encoded image . \n
-
- *@par Inputs:
- *Input contents must be 0-D. Inputs include:
- *contents: 0-D. The JPEG-encoded image . \n
-
- *@par Attributes:
- *output_type: The output type of the operation (int32 or int64). Defaults
- to int32 . \n
-
- *@par Outputs:
- *image_shape: 1-D. The image shape with format [height, width, channels] . \n
-
- *@par Third-party framework compatibility
- *Compatible with tensorflow ExtractJpegShape operator.
- */
-
- REG_OP(ExtractJpegShape)
- .INPUT(contents, TensorType({DT_STRING}))
- .OUTPUT(image_shape, TensorType({DT_INT32, DT_INT64}))
- .REQUIRED_ATTR(output_type, Type)
- .OP_END_FACTORY_REG(ExtractJpegShape)
-
- /**
- *@brief Draw bounding boxes on a batch of images . \n
-
- *@par Inputs:
- *@li images: 4-D with shape `[batch, height, width, depth]`.
- A batch of images.
- *@li boxes: 3-D with shape `[batch, num_bounding_boxes, 4]`
- containing bounding boxes.
- *@li colors: 2-D. A list of RGBA colors to cycle through for the boxes . \n
-
- *@par Outputs:
- *y: Returns 4-D with the same shape as `images`.
- The batch of input images with bounding boxes drawn on the images . \n
-
- *@par Third-party framework compatibility
- * Compatible with tensorflow DrawBoundingBoxesV2 operator.
- */
-
- REG_OP(DrawBoundingBoxesV2)
- .INPUT(images, TensorType({DT_FLOAT}))
- .INPUT(boxes, TensorType({DT_FLOAT}))
- .INPUT(colors, TensorType({DT_FLOAT}))
- .OUTPUT(y, TensorType({DT_FLOAT}))
- .OP_END_FACTORY_REG(DrawBoundingBoxesV2)
-
- /**
- *@brief Greedily selects a subset of bounding boxes in descending order of score,
- pruning away boxes that have high intersection-over-union (IOU) overlap
- with previously selected boxes . \n
-
- *@par Inputs:
- *@li boxes: A 2-D float tensor of shape `[num_boxes, 4]`.
- *@li scores: A 1-D float tensor of shape `[num_boxes]` representing a single
- score corresponding to each box (each row of boxes).
- *@li max_output_size: A scalar integer tensor representing the maximum number of
- boxes to be selected by non max suppression.
- *@li iou_threshold: A 0-D float tensor representing the threshold for deciding whether
- boxes overlap too much with respect to IOU.
- *@li score_threshold: A 0-D float tensor representing the threshold for deciding when to
- remove boxes based on score.
- *@li soft_nms_sigma: A 0-D float tensor representing the sigma parameter for Soft NMS . \n
-
- *@par Attributes:
- pad_to_max_output_size: If true, the output `selected_indices` is padded to be of length
- `max_output_size`. Defaults to false. If not specified, defaults to false . \n
-
- *@par Outputs:
- *@li selected_indices: A 1-D integer tensor of shape [M] representing the
- selected indices from the boxes tensor, where M <= max_output_size.
- *@li selected_scores: A 1-D float tensor of shape `[M]` representing the corresponding
- scores for each selected box, where `M <= max_output_size`.
- *@li valid_outputs: A 0-D integer tensor representing the number of valid
- elements in selected_indices, with the valid elements appearing first . \n
-
- *@par Third-party framework compatibility
- * Compatible with tensorflow NonMaxSuppressionV5 operator.
- */
-
- REG_OP(NonMaxSuppressionV5)
- .INPUT(boxes, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(scores, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(max_output_size, TensorType({DT_INT32}))
- .INPUT(iou_threshold, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(score_threshold, TensorType({DT_FLOAT16, DT_FLOAT}))
- .INPUT(soft_nms_sigma, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(selected_indices, TensorType({DT_INT32}))
- .OUTPUT(selected_scores, TensorType({DT_FLOAT16, DT_FLOAT}))
- .OUTPUT(valid_outputs, TensorType({DT_INT32}))
- .ATTR(pad_to_max_output_size, Bool, false)
- .REQUIRED_ATTR(T, Type)
- .OP_END_FACTORY_REG(NonMaxSuppressionV5)
-
- /**
- *@brief Resizes "images" to "size" by scale and translate . \n
-
- *@par Inputs:
- *@li images: A `Tensor`. Must be one of the following types: `int8`, `uint8`,
- `int16`, `uint16`, `int32`, `int64`, `bfloat16`, `float32`, `float64`.
- *@li size: A `Tensor` of type `int32`.
- *@li scale: A `Tensor` of type `float32`.
- *@li translation: A `Tensor` of type `float32` . \n
-
- *@li kernel_type: type is string, default lanczos3
- *@li antialias: type is bool, default true \n
-
- *@par Outputs:
- *y: A Tensor with type float32 . \n
-
- *@par Third-party framework compatibility
- * Compatible with TensorFlow ScaleAndTranslate operator.
- */
-
- REG_OP(ScaleAndTranslate)
- .INPUT(images, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16,
- DT_INT32, DT_INT64, DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
- .INPUT(size, TensorType({DT_INT32}))
- .INPUT(scale, TensorType({DT_FLOAT}))
- .INPUT(translation, TensorType({DT_FLOAT}))
- .OUTPUT(y, TensorType({DT_FLOAT}))
- .ATTR(kernel_type, String, "lanczos3")
- .ATTR(antialias, Bool, true)
- .OP_END_FACTORY_REG(ScaleAndTranslate)
-
- /**
- *@brief Computes the gradient by scale and translate . \n
-
- *@par Inputs:
- *@li grads: A `Tensor`. Must be one of the following types: `float32`.
- *@li original_image: A `Tensor`. Must have the same type as `grads`.
- *@li scale: A `Tensor` of type `float32`.
- *@li translation: A `Tensor` of type `float32` . \n
-
- *@li kernel_type: type is string, default lanczos3
- *@li antialias: type is bool, default true
-
- *@par Outputs:
- *y: A `Tensor`. Has the same type as `grads` . \n
-
- *@par Third-party framework compatibility
- * Compatible with TensorFlow ScaleAndTranslateGrad operator.
- */
-
- REG_OP(ScaleAndTranslateGrad)
- .INPUT(grads, TensorType({DT_FLOAT}))
- .INPUT(original_image, TensorType({DT_FLOAT}))
- .INPUT(scale, TensorType({DT_FLOAT}))
- .INPUT(translation, TensorType({DT_FLOAT}))
- .OUTPUT(y, TensorType({DT_FLOAT}))
- .ATTR(kernel_type, String, "lanczos3")
- .ATTR(antialias, Bool, true)
- .OP_END_FACTORY_REG(ScaleAndTranslateGrad)
-
- /**
- *@brief Greedily selects a subset of bounding boxes in descending order of score,
- This operation performs non_max_suppression on the inputs per batch, across all classes . \n
-
- *@par Inputs:
- *@li 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.
- *@li 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).
- *@li max_output_size_per_class: A scalar integer tensor representing the maximum number of
- boxes to be selected by non max suppression per class.
- *@li max_total_size: A scalar representing maximum number of boxes retained over all classes.
- *@li iou_threshold: A 0-D float tensor representing the threshold for deciding whether
- boxes overlap too much with respect to IOU.
- *@li score_threshold: A 0-D float tensor representing the threshold for deciding when to remove
- boxes based on score . \n
-
- *@par Attributes:
- *@li 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.
- *@li clip_boxes: If true, assume the box coordinates are between [0, 1] and clip the output boxes
- if they fall beyond [0, 1]. If false, do not do clipping and output the box
- coordinates as it is. If not specified, defaults to true . \n
-
- *@par Outputs:
- *nmsed_boxes:type is float
- *nmsed_scores:type is float
- *nmsed_classes:type is float \n
-
- *@par Third-party framework compatibility
- * Compatible with tensorflow CombinedNonMaxSuppression operator.
- */
-
- REG_OP(CombinedNonMaxSuppression)
- .INPUT(boxes, TensorType({DT_FLOAT}))
- .INPUT(scores, TensorType({DT_FLOAT}))
- .INPUT(max_output_size_per_class, TensorType({DT_INT32}))
- .INPUT(max_total_size, TensorType({DT_INT32}))
- .INPUT(iou_threshold, TensorType({DT_FLOAT}))
- .INPUT(score_threshold, TensorType({DT_FLOAT}))
- .OUTPUT(nmsed_boxes, TensorType({DT_FLOAT}))
- .OUTPUT(nmsed_scores, TensorType({DT_FLOAT}))
- .OUTPUT(nmsed_classes, TensorType({DT_FLOAT}))
- .OUTPUT(valid_detections, TensorType({DT_INT32}))
- .ATTR(pad_per_class, Bool, false)
- .ATTR(clip_boxes, Bool, true)
- .OP_END_FACTORY_REG(CombinedNonMaxSuppression)
-
- /**
- *@brief Function spatial transformer . \n
-
- *@par Inputs:
- *@li x: A Tensor dtype of float16, float32.
- *@li theta: A Tensor dtype of float16, float32, auxiliary coefficients . \n
-
- *@par Attributes:
- *@li output_size: A tuple output size.
- *@li default_theta: A tuple default theta
- *@li use_default_theta: List use default theta
- *@li align_corners: Align corners
-
- *@par Outputs:
- *y: A Tensor dtype of float16, float32, should be same shape and type as x.
- */
- REG_OP(SpatialTransformerD)
- .INPUT(x, TensorType({DT_FLOAT,DT_FLOAT16}))
- .OPTIONAL_INPUT(theta, TensorType({DT_FLOAT,DT_FLOAT16}))
- .OUTPUT(y, TensorType({DT_FLOAT,DT_FLOAT16}))
- .ATTR(output_size, ListInt, {-1, -1})
- .ATTR(default_theta, ListFloat, {})
- .ATTR(align_corners, Bool, false)
- .ATTR(use_default_theta, ListBool, {})
- .OP_END_FACTORY_REG(SpatialTransformerD)
-
- } // namespace ge
-
- #endif // OPS_BUILT_IN_OP_PROTO_INC_IMAGE_OPS_H_
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