summary: "Increments \'ref\' until it reaches \'limit\'."
description: "This operation outputs \"ref\" after the update is done. This makes it\neasier to chain operations that need to use the updated value."
}
op {
name: "CropAndResize"
input_arg {
name: "image"
description: "A 4-D tensor of shape `[batch, image_height, image_width, depth]`.\nBoth `image_height` and `image_width` need to be positive."
type_attr: "T"
}
input_arg {
name: "boxes"
description: "A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor\nspecifies the coordinates of a box in the `box_ind[i]` image and is specified\nin normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of\n`y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the\n`[0, 1]` interval of normalized image height is mapped to\n`[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in\nwhich case the sampled crop is an up-down flipped version of the original\nimage. The width dimension is treated similarly. Normalized coordinates\noutside the `[0, 1]` range are allowed, in which case we use\n`extrapolation_value` to extrapolate the input image values."
type: DT_FLOAT
}
input_arg {
name: "box_ind"
description: "A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`.\nThe value of `box_ind[i]` specifies the image that the `i`-th box refers to."
type: DT_INT32
}
input_arg {
name: "crop_size"
description: "A 1-D tensor of 2 elements, `size = [crop_height, crop_width]`. All\ncropped image patches are resized to this size. The aspect ratio of the image\ncontent is not preserved. Both `crop_height` and `crop_width` need to be\npositive."
type: DT_INT32
}
output_arg {
name: "crops"
description: "A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`."
type: DT_FLOAT
}
attr {
name: "T"
type: "type"
allowed_values {
list {
type: DT_UINT8
type: DT_INT8
type: DT_INT16
type: DT_INT32
type: DT_INT64
type: DT_HALF
type: DT_FLOAT
type: DT_DOUBLE
}
}
}
attr {
name: "method"
type: "string"
default_value {
s: "bilinear"
}
description: "A string specifying the interpolation method. Only \'bilinear\' is\nsupported for now."
allowed_values {
list {
s: "bilinear"
}
}
}
attr {
name: "extrapolation_value"
type: "float"
default_value {
f: 0
}
description: "Value used for extrapolation, when applicable."
}
summary: "Extracts crops from the input image tensor and bilinearly resizes them (possibly"
description: "with aspect ratio change) to a common output size specified by `crop_size`. This\nis more general than the `crop_to_bounding_box` op which extracts a fixed size\nslice from the input image and does not allow resizing or aspect ratio change.\n\nReturns a tensor with `crops` from the input `image` at positions defined at the\nbounding box locations in `boxes`. The cropped boxes are all resized (with\nbilinear interpolation) to a fixed `size = [crop_height, crop_width]`. The\nresult is a 4-D tensor `[num_boxes, crop_height, crop_width, depth]`."
}
op {
name: "CropAndResizeGradBoxes"
input_arg {
name: "grads"
description: "A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`."
type: DT_FLOAT
}
input_arg {
name: "image"
description: "A 4-D tensor of shape `[batch, image_height, image_width, depth]`.\nBoth `image_height` and `image_width` need to be positive."
type_attr: "T"
}
input_arg {
name: "boxes"
description: "A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor\nspecifies the coordinates of a box in the `box_ind[i]` image and is specified\nin normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of\n`y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the\n`[0, 1]` interval of normalized image height is mapped to\n`[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in\nwhich case the sampled crop is an up-down flipped version of the original\nimage. The width dimension is treated similarly. Normalized coordinates\noutside the `[0, 1]` range are allowed, in which case we use\n`extrapolation_value` to extrapolate the input image values."
type: DT_FLOAT
}
input_arg {
name: "box_ind"
description: "A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`.\nThe value of `box_ind[i]` specifies the image that the `i`-th box refers to."
type: DT_INT32
}
output_arg {
name: "output"
description: "A 2-D tensor of shape `[num_boxes, 4]`."
type: DT_FLOAT
}
attr {
name: "T"
type: "type"
allowed_values {
list {
type: DT_UINT8
type: DT_INT8
type: DT_INT16
type: DT_INT32
type: DT_INT64
type: DT_HALF
type: DT_FLOAT
type: DT_DOUBLE
}
}
}
attr {
name: "method"
type: "string"
default_value {
s: "bilinear"
}
description: "A string specifying the interpolation method. Only \'bilinear\' is\nsupported for now."
allowed_values {
list {
s: "bilinear"
}
}
}
summary: "Computes the gradient of the crop_and_resize op wrt the input boxes tensor."
}
op {
name: "CropAndResizeGradImage"
input_arg {
name: "grads"
description: "A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`."
type: DT_FLOAT
}
input_arg {
name: "boxes"
description: "A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor\nspecifies the coordinates of a box in the `box_ind[i]` image and is specified\nin normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of\n`y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the\n`[0, 1]` interval of normalized image height is mapped to\n`[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in\nwhich case the sampled crop is an up-down flipped version of the original\nimage. The width dimension is treated similarly. Normalized coordinates\noutside the `[0, 1]` range are allowed, in which case we use\n`extrapolation_value` to extrapolate the input image values."
type: DT_FLOAT
}
input_arg {
name: "box_ind"
description: "A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`.\nThe value of `box_ind[i]` specifies the image that the `i`-th box refers to."
type: DT_INT32
}
input_arg {
name: "image_size"
description: "A 1-D tensor with value `[batch, image_height, image_width, depth]`\ncontaining the original image size. Both `image_height` and `image_width` need\nto be positive."
type: DT_INT32
}
output_arg {
name: "output"
description: "A 4-D tensor of shape `[batch, image_height, image_width, depth]`."
type_attr: "T"
}
attr {
name: "T"
type: "type"
allowed_values {
list {
type: DT_FLOAT
type: DT_HALF
type: DT_DOUBLE
}
}
}
attr {
name: "method"
type: "string"
default_value {
s: "bilinear"
}
description: "A string specifying the interpolation method. Only \'bilinear\' is\nsupported for now."
allowed_values {
list {
s: "bilinear"
}
}
}
summary: "Computes the gradient of the crop_and_resize op wrt the input image tensor."
