Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 307690267 Change-Id: Ic78e8ce07983dd496ba9563bcf60352473a0428c
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
parent
75940041fc
commit
567f7e24ef
@ -9878,12 +9878,26 @@ func RegisterDataset(scope *Scope, dataset tf.Output, address tf.Output, protoco
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return op.Output(0)
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}
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// DataServiceDatasetAttr is an optional argument to DataServiceDataset.
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type DataServiceDatasetAttr func(optionalAttr)
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// DataServiceDatasetTaskRefreshIntervalHintMs sets the optional task_refresh_interval_hint_ms attribute to value.
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// If not specified, defaults to -1
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func DataServiceDatasetTaskRefreshIntervalHintMs(value int64) DataServiceDatasetAttr {
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return func(m optionalAttr) {
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m["task_refresh_interval_hint_ms"] = value
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}
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}
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// Creates a dataset that reads data from the tf.data service.
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func DataServiceDataset(scope *Scope, address tf.Output, protocol tf.Output, max_outstanding_requests tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
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func DataServiceDataset(scope *Scope, address tf.Output, protocol tf.Output, max_outstanding_requests tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...DataServiceDatasetAttr) (handle tf.Output) {
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if scope.Err() != nil {
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return
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}
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attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
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for _, a := range optional {
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a(attrs)
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}
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opspec := tf.OpSpec{
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Type: "DataServiceDataset",
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Input: []tf.Input{
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@ -11879,75 +11893,6 @@ func ExtractGlimpse(scope *Scope, input tf.Output, size tf.Output, offsets tf.Ou
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return op.Output(0)
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}
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// Converts one or more images from RGB to HSV.
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//
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// Outputs a tensor of the same shape as the `images` tensor, containing the HSV
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// value of the pixels. The output is only well defined if the value in `images`
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// are in `[0,1]`.
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//
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// `output[..., 0]` contains hue, `output[..., 1]` contains saturation, and
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// `output[..., 2]` contains value. All HSV values are in `[0,1]`. A hue of 0
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// corresponds to pure red, hue 1/3 is pure green, and 2/3 is pure blue.
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//
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// Usage Example:
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//
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// >>> blue_image = tf.stack([
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// ... tf.zeros([5,5]),
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// ... tf.zeros([5,5]),
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// ... tf.ones([5,5])],
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// ... axis=-1)
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// >>> blue_hsv_image = tf.image.rgb_to_hsv(blue_image)
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// >>> blue_hsv_image[0,0].numpy()
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// array([0.6666667, 1. , 1. ], dtype=float32)
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//
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//
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// Arguments:
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// images: 1-D or higher rank. RGB data to convert. Last dimension must be size 3.
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//
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// Returns `images` converted to HSV.
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func RGBToHSV(scope *Scope, images tf.Output) (output tf.Output) {
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if scope.Err() != nil {
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return
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}
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opspec := tf.OpSpec{
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Type: "RGBToHSV",
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Input: []tf.Input{
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images,
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},
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// Decode the frame(s) of a GIF-encoded image to a uint8 tensor.
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//
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// GIF images with frame or transparency compression are not supported.
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// On Linux and MacOS systems, convert animated GIFs from compressed to
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// uncompressed by running:
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//
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// convert $src.gif -coalesce $dst.gif
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//
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// This op also supports decoding JPEGs and PNGs, though it is cleaner to use
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// `tf.io.decode_image`.
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//
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// Arguments:
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// contents: 0-D. The GIF-encoded image.
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//
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// Returns 4-D with shape `[num_frames, height, width, 3]`. RGB channel order.
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func DecodeGif(scope *Scope, contents tf.Output) (image tf.Output) {
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if scope.Err() != nil {
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return
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}
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opspec := tf.OpSpec{
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Type: "DecodeGif",
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Input: []tf.Input{
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contents,
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},
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// SampleDistortedBoundingBoxAttr is an optional argument to SampleDistortedBoundingBox.
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type SampleDistortedBoundingBoxAttr func(optionalAttr)
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@ -11990,7 +11935,7 @@ func SampleDistortedBoundingBoxMinObjectCovered(value float32) SampleDistortedBo
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//
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// value: The cropped area of the image must have an aspect ratio =
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// width / height within this range.
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// If not specified, defaults to {f:0.75 f:1.33}
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// If not specified, defaults to {f:0.75 f:1.33}
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func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistortedBoundingBoxAttr {
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return func(m optionalAttr) {
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m["aspect_ratio_range"] = value
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@ -12001,7 +11946,7 @@ func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistorted
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//
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// value: The cropped area of the image must contain a fraction of the
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// supplied image within this range.
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// If not specified, defaults to {f:0.05 f:1}
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// If not specified, defaults to {f:0.05 f:1}
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func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr {
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return func(m optionalAttr) {
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m["area_range"] = value
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@ -12104,6 +12049,75 @@ func SampleDistortedBoundingBox(scope *Scope, image_size tf.Output, bounding_box
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return op.Output(0), op.Output(1), op.Output(2)
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}
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// Converts one or more images from RGB to HSV.
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//
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// Outputs a tensor of the same shape as the `images` tensor, containing the HSV
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// value of the pixels. The output is only well defined if the value in `images`
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// are in `[0,1]`.
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//
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// `output[..., 0]` contains hue, `output[..., 1]` contains saturation, and
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// `output[..., 2]` contains value. All HSV values are in `[0,1]`. A hue of 0
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// corresponds to pure red, hue 1/3 is pure green, and 2/3 is pure blue.
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//
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// Usage Example:
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//
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// >>> blue_image = tf.stack([
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// ... tf.zeros([5,5]),
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// ... tf.zeros([5,5]),
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// ... tf.ones([5,5])],
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// ... axis=-1)
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// >>> blue_hsv_image = tf.image.rgb_to_hsv(blue_image)
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// >>> blue_hsv_image[0,0].numpy()
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// array([0.6666667, 1. , 1. ], dtype=float32)
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//
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//
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// Arguments:
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// images: 1-D or higher rank. RGB data to convert. Last dimension must be size 3.
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//
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// Returns `images` converted to HSV.
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func RGBToHSV(scope *Scope, images tf.Output) (output tf.Output) {
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if scope.Err() != nil {
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return
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}
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opspec := tf.OpSpec{
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Type: "RGBToHSV",
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Input: []tf.Input{
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images,
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},
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// Decode the frame(s) of a GIF-encoded image to a uint8 tensor.
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//
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// GIF images with frame or transparency compression are not supported.
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// On Linux and MacOS systems, convert animated GIFs from compressed to
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// uncompressed by running:
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//
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// convert $src.gif -coalesce $dst.gif
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//
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// This op also supports decoding JPEGs and PNGs, though it is cleaner to use
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// `tf.io.decode_image`.
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//
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// Arguments:
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// contents: 0-D. The GIF-encoded image.
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//
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// Returns 4-D with shape `[num_frames, height, width, 3]`. RGB channel order.
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func DecodeGif(scope *Scope, contents tf.Output) (image tf.Output) {
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if scope.Err() != nil {
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return
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}
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opspec := tf.OpSpec{
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Type: "DecodeGif",
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Input: []tf.Input{
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contents,
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},
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// DecodeBmpAttr is an optional argument to DecodeBmp.
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type DecodeBmpAttr func(optionalAttr)
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@ -18608,7 +18622,7 @@ func SampleDistortedBoundingBoxV2Seed2(value int64) SampleDistortedBoundingBoxV2
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//
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// value: The cropped area of the image must have an aspect ratio =
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// width / height within this range.
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// If not specified, defaults to {f:0.75 f:1.33}
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// If not specified, defaults to {f:0.75 f:1.33}
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func SampleDistortedBoundingBoxV2AspectRatioRange(value []float32) SampleDistortedBoundingBoxV2Attr {
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return func(m optionalAttr) {
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m["aspect_ratio_range"] = value
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@ -18619,7 +18633,7 @@ func SampleDistortedBoundingBoxV2AspectRatioRange(value []float32) SampleDistort
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//
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// value: The cropped area of the image must contain a fraction of the
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// supplied image within this range.
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// If not specified, defaults to {f:0.05 f:1}
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// If not specified, defaults to {f:0.05 f:1}
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func SampleDistortedBoundingBoxV2AreaRange(value []float32) SampleDistortedBoundingBoxV2Attr {
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return func(m optionalAttr) {
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m["area_range"] = value
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@ -19023,7 +19037,7 @@ func ImageSummaryMaxImages(value int64) ImageSummaryAttr {
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// ImageSummaryBadColor sets the optional bad_color attribute to value.
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//
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// value: Color to use for pixels with non-finite values.
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// If not specified, defaults to {dtype:DT_UINT8 tensor_shape:{dim:{size:4}} int_val:255 int_val:0 int_val:0 int_val:255}
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// If not specified, defaults to {dtype:DT_UINT8 tensor_shape:{dim:{size:4}} int_val:255 int_val:0 int_val:0 int_val:255}
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func ImageSummaryBadColor(value tf.Tensor) ImageSummaryAttr {
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return func(m optionalAttr) {
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m["bad_color"] = value
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@ -20094,7 +20108,7 @@ func Conv3DBackpropFilterV2DataFormat(value string) Conv3DBackpropFilterV2Attr {
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// filter element on that dimension. The dimension order is determined by the
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// value of `data_format`, see above for details. Dilations in the batch and
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// depth dimensions must be 1.
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// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
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func Conv3DBackpropFilterV2Dilations(value []int64) Conv3DBackpropFilterV2Attr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -21266,7 +21280,7 @@ func Conv2DBackpropInputDataFormat(value string) Conv2DBackpropInputAttr {
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// element on that dimension. The dimension order is determined by the value of
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// `data_format`, see above for details. Dilations in the batch and depth
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// dimensions must be 1.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func Conv2DBackpropInputDilations(value []int64) Conv2DBackpropInputAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -21974,7 +21988,7 @@ func Conv2DDataFormat(value string) Conv2DAttr {
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// filter element on that dimension. The dimension order is determined by the
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// value of `data_format`, see above for details. Dilations in the batch and
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// depth dimensions must be 1.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func Conv2DDilations(value []int64) Conv2DAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -22170,7 +22184,7 @@ func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeOutType(value tf.DataTy
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// QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeDilations sets the optional dilations attribute to value.
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//
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// value: List of dilation values.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -22239,7 +22253,7 @@ func QuantizedDepthwiseConv2DWithBiasAndReluOutType(value tf.DataType) Quantized
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// QuantizedDepthwiseConv2DWithBiasAndReluDilations sets the optional dilations attribute to value.
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//
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// value: List of dilation values.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func QuantizedDepthwiseConv2DWithBiasAndReluDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAndReluAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -22354,7 +22368,7 @@ func QuantizedDepthwiseConv2DWithBiasOutType(value tf.DataType) QuantizedDepthwi
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// QuantizedDepthwiseConv2DWithBiasDilations sets the optional dilations attribute to value.
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//
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// value: List of dilation values.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func QuantizedDepthwiseConv2DWithBiasDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -22413,7 +22427,7 @@ func QuantizedDepthwiseConv2DOutType(value tf.DataType) QuantizedDepthwiseConv2D
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// QuantizedDepthwiseConv2DDilations sets the optional dilations attribute to value.
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//
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// value: List of dilation values.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func QuantizedDepthwiseConv2DDilations(value []int64) QuantizedDepthwiseConv2DAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -22587,7 +22601,7 @@ func QuantizedConv2DPerChannelOutType(value tf.DataType) QuantizedConv2DPerChann
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// QuantizedConv2DPerChannelDilations sets the optional dilations attribute to value.
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//
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// value: list of dilation values.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func QuantizedConv2DPerChannelDilations(value []int64) QuantizedConv2DPerChannelAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -22964,7 +22978,7 @@ func Conv3DBackpropInputV2DataFormat(value string) Conv3DBackpropInputV2Attr {
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// filter element on that dimension. The dimension order is determined by the
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// value of `data_format`, see above for details. Dilations in the batch and
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// depth dimensions must be 1.
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// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
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func Conv3DBackpropInputV2Dilations(value []int64) Conv3DBackpropInputV2Attr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -25284,7 +25298,7 @@ func AvgPool3DGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksi
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type Conv3DBackpropFilterAttr func(optionalAttr)
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// Conv3DBackpropFilterDilations sets the optional dilations attribute to value.
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// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
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func Conv3DBackpropFilterDilations(value []int64) Conv3DBackpropFilterAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -25347,7 +25361,7 @@ func Conv3DDataFormat(value string) Conv3DAttr {
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// filter element on that dimension. The dimension order is determined by the
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// value of `data_format`, see above for details. Dilations in the batch and
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// depth dimensions must be 1.
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// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
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func Conv3DDilations(value []int64) Conv3DAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -25598,7 +25612,7 @@ func DepthwiseConv2dNativeBackpropInputDataFormat(value string) DepthwiseConv2dN
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// element on that dimension. The dimension order is determined by the value of
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// `data_format`, see above for details. Dilations in the batch and depth
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// dimensions must be 1.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func DepthwiseConv2dNativeBackpropInputDilations(value []int64) DepthwiseConv2dNativeBackpropInputAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -26082,7 +26096,7 @@ func QuantizedConv2DOutType(value tf.DataType) QuantizedConv2DAttr {
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// filter element on that dimension. The dimension order is determined by the
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// value of `data_format`, see above for details. Dilations in the batch and
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// depth dimensions must be 1.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func QuantizedConv2DDilations(value []int64) QuantizedConv2DAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -40335,7 +40349,7 @@ func DepthwiseConv2dNativeBackpropFilterDataFormat(value string) DepthwiseConv2d
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// element on that dimension. The dimension order is determined by the value of
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// `data_format`, see above for details. Dilations in the batch and depth
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// dimensions must be 1.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func DepthwiseConv2dNativeBackpropFilterDilations(value []int64) DepthwiseConv2dNativeBackpropFilterAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -45832,7 +45846,7 @@ func Conv2DBackpropFilterDataFormat(value string) Conv2DBackpropFilterAttr {
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// element on that dimension. The dimension order is determined by the value of
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// `data_format`, see above for details. Dilations in the batch and depth
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// dimensions must be 1.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func Conv2DBackpropFilterDilations(value []int64) Conv2DBackpropFilterAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -46824,7 +46838,7 @@ func CreateJob(scope *Scope, dataset_id tf.Output, address tf.Output, protocol t
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type Conv3DBackpropInputAttr func(optionalAttr)
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// Conv3DBackpropInputDilations sets the optional dilations attribute to value.
|
||||
// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
|
||||
// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
|
||||
func Conv3DBackpropInputDilations(value []int64) Conv3DBackpropInputAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["dilations"] = value
|
||||
@ -46895,7 +46909,7 @@ func DepthwiseConv2dNativeDataFormat(value string) DepthwiseConv2dNativeAttr {
|
||||
// element on that dimension. The dimension order is determined by the value of
|
||||
// `data_format`, see above for details. Dilations in the batch and depth
|
||||
// dimensions must be 1.
|
||||
// If not specified, defaults to {i:1 i:1 i:1 i:1}
|
||||
// If not specified, defaults to {i:1 i:1 i:1 i:1}
|
||||
func DepthwiseConv2dNativeDilations(value []int64) DepthwiseConv2dNativeAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["dilations"] = value
|
||||
|
||||
Loading…
Reference in New Issue
Block a user