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https://github.com/photoprism/photoprism.git
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519a6ab34a
* AI: Added support for non BHWC models Tensorflow models use BHWC by default, however, if we are using converted models, we can find that the expected input is BCHW. Now the input is configurable (although the restriction of being dimesion 4 is still there) via Shape parameter on the input definition. Also, the model instrospection will try to deduce the input shape from the model signature. * AI: Added more tests for enum parsing ShapeComponent was missing from the tests * AI: Modified external tests to the new url The path has been moved from tensorflow/vision to tensorflow/models * AI: Moved the builder to the model to reuse it It should reduce the amount of allocations done * AI: fixed errors after merge Mainly incorrect paths and duplicated variables
113 lines
2.5 KiB
Go
113 lines
2.5 KiB
Go
package tensorflow
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import (
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"errors"
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"fmt"
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tf "github.com/wamuir/graft/tensorflow"
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)
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type ImageTensorBuilder struct {
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data []float32
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shape []ShapeComponent
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resolution int
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rIndex int
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gIndex int
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bIndex int
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}
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func shapeLen(c ShapeComponent, res int) int {
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switch c {
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case ShapeBatch:
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return 1
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case ShapeHeight, ShapeWidth:
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return res
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case ShapeColor:
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return 3
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default:
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return -1
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}
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}
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func NewImageTensorBuilder(input *PhotoInput) (*ImageTensorBuilder, error) {
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if len(input.Shape) != 4 {
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return nil, fmt.Errorf("tensorflow: the shape length is %d and should be 4", len(input.Shape))
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}
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if input.Shape[0] != ShapeBatch {
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return nil, errors.New("tensorflow: the first shape component must be Batch")
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}
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if input.Shape[1] != ShapeColor && input.Shape[3] != ShapeColor {
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return nil, fmt.Errorf("tensorflow: unsupported shape %v", input.Shape)
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}
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totalSize := 1
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for i := range input.Shape {
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totalSize *= shapeLen(input.Shape[i], input.Resolution())
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}
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// Allocate just one big chunk
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flatBuffer := make([]float32, totalSize)
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rIndex, gIndex, bIndex := input.ColorChannelOrder.Indices()
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return &ImageTensorBuilder{
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data: flatBuffer,
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shape: input.Shape,
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resolution: input.Resolution(),
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rIndex: rIndex,
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gIndex: gIndex,
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bIndex: bIndex,
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}, nil
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}
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func (t *ImageTensorBuilder) Set(x, y int, r, g, b float32) {
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t.data[t.flatIndex(x, y, t.rIndex)] = r
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t.data[t.flatIndex(x, y, t.gIndex)] = g
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t.data[t.flatIndex(x, y, t.bIndex)] = b
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}
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func (t *ImageTensorBuilder) flatIndex(x, y, c int) int {
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shapeVal := func(s ShapeComponent) int {
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switch s {
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case ShapeBatch:
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return 0
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case ShapeColor:
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return c
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case ShapeWidth:
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return x
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case ShapeHeight:
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return y
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default:
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return 0
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}
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}
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idx := 0
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for _, s := range t.shape {
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idx = idx*shapeLen(s, t.resolution) + shapeVal(s)
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}
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return idx
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}
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func (t *ImageTensorBuilder) BuildTensor() (*tf.Tensor, error) {
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arr := make([][][][]float32, shapeLen(t.shape[0], t.resolution))
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offset := 0
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for i := 0; i < shapeLen(t.shape[0], t.resolution); i++ {
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arr[i] = make([][][]float32, shapeLen(t.shape[1], t.resolution))
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for j := 0; j < shapeLen(t.shape[1], t.resolution); j++ {
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arr[i][j] = make([][]float32, shapeLen(t.shape[2], t.resolution))
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for k := 0; k < shapeLen(t.shape[2], t.resolution); k++ {
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arr[i][j][k] = t.data[offset : offset+shapeLen(t.shape[3], t.resolution)]
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offset += shapeLen(t.shape[3], t.resolution)
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}
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}
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}
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return tf.NewTensor(arr)
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}
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