Class: TensorStream::InferShape

Inherits:
Object
  • Object
show all
Extended by:
ArrayOpsHelper, OpHelper
Defined in:
lib/tensor_stream/helpers/infer_shape.rb

Overview

Convenience class for guessing the shape of a tensor

Class Method Summary collapse

Methods included from OpHelper

_op, cons, format_source, fp_type?, i_cons, i_op, i_var, int_type?, reduced_shape, shape_eval, shape_full_specified, shapes_fully_specified_and_equal

Methods included from ArrayOpsHelper

_reduced_shape, arr_pad, array_set!, broadcast, broadcast_dimensions, deep_dup_array, gather, get_rank, last_axis, process_function_op, reduce, reduce_axis, shape_diff, slice_tensor, softmax, softmax_grad, split_tensor, strided_slice, strided_slice_grad, tile_arr, transpose_with_perm, truncate, vector_op

Class Method Details

._infer_reduction_op_shape(tensor) ⇒ Object


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# File 'lib/tensor_stream/helpers/infer_shape.rb', line 186

def self._infer_reduction_op_shape(tensor)
  return [] if tensor.inputs[1].nil?
  return nil if tensor.inputs[0].nil?
  return nil unless tensor.inputs[0].shape.known?

  input_shape = tensor.inputs[0].shape.shape
  rank = input_shape.size

  axis = tensor.inputs[1].const_value
  return nil if axis.nil?

  axis = [axis] unless axis.is_a?(Array)
  axis = axis.map { |a| a < 0 ? rank - a.abs : a }

  input_shape.each_with_index.map { |item, index|
    if axis.include?(index)
      next 1 if tensor.options[:keepdims]

      next nil
    end
    item
  }.compact
end

.infer_shape(tensor) ⇒ Object


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# File 'lib/tensor_stream/helpers/infer_shape.rb', line 10

def self.infer_shape(tensor)
  case tensor.operation
  when :assign
    tensor.inputs[0]&.shape&.shape
  when :const
    shape_eval(tensor.options[:value])
  when :variable_v2
    tensor.shape ? tensor.shape.shape : nil
  when :placeholder
    return nil if tensor.inputs[0].nil?
    return tensor.inputs[0].shape.shape if tensor.inputs.size == 1

    TensorShape.infer_shape(tensor.inputs[0].shape.shape, tensor.inputs[1].shape.shape) if tensor.inputs.size == 2 && tensor.inputs[0] && tensor.inputs[1]
  when :case, :case_grad
    tensor.inputs[2]&.shape&.shape
  when :const
    shape_eval(tensor.options[:value])
  when :variable_v2
    tensor.shape ? tensor.shape.shape : nil
  when :assign
    possible_shape = if tensor.inputs[0]&.shape&.shape
      tensor.inputs[0].shape.shape
    else
      tensor.inputs[1].shape.shape
    end

    possible_shape
  when :index
    return nil unless tensor.inputs[0].is_a?(Tensor)
    return nil unless tensor.inputs[0].const_value

    input_shape = tensor.inputs[0].shape
    return nil unless input_shape.known?

    s = input_shape.shape.dup
    s.shift
    s
  when :arg_min, :argmax, :argmin
    return nil unless tensor.inputs[0].shape.known?
    return nil if tensor.inputs[1] && tensor.inputs[1].const_value.nil?

    axis = tensor.inputs[1].nil? ? 0 : tensor.inputs[1].const_value
    new_shape = tensor.inputs[0].shape.shape
    new_shape.each_with_index.collect { |shape, index|
      next nil if index == axis

      shape
    }.compact
  when :mean, :prod, :sum, :arg_max
    return [] if tensor.inputs[1].nil?
    return nil if tensor.inputs[0].nil?
    return nil unless tensor.inputs[0].shape.known?

    input_shape = tensor.inputs[0].shape.shape
    rank = input_shape.size

    axis = tensor.inputs[1].const_value
    return nil if axis.nil?

    axis = [axis] unless axis.is_a?(Array)
    axis = axis.map { |a| a < 0 ? rank - a.abs : a }

    input_shape.each_with_index.map { |item, index|
      if axis.include?(index)
        next 1 if tensor.options[:keepdims]

        next nil
      end
      item
    }.compact
  when :flow_group
    []
  when :zeros, :ones, :fill, :random_standard_normal, :random_uniform, :truncated_normal
    a_shape = tensor.inputs[0] ? tensor.inputs[0].const_value : tensor.options[:shape]
    return nil if a_shape.nil?

    a_shape.is_a?(Array) ? a_shape : [a_shape]
  when :zeros_like, :ones_like
    tensor.inputs[0].shape.shape
  when :shape
    tensor.inputs[0].shape.shape ? [tensor.inputs[0].shape.shape.size] : nil
  when :pad
    return nil unless tensor.inputs[0].shape.known?
    return nil unless tensor.inputs[1].const_value

    size = tensor.inputs[0].shape.shape.reduce(:*) || 1
    dummy_tensor_for_shape = TensorShape.reshape(Array.new(size), tensor.inputs[0].shape)
    shape_eval(arr_pad(dummy_tensor_for_shape, tensor.inputs[1].const_value))
  when :transpose
    return nil unless shape_full_specified(tensor.inputs[0])
    return nil if tensor.inputs[1].is_a?(Tensor)

    rank = tensor.inputs[0].shape.shape.size
    perm = tensor.inputs[1] || (0...rank).to_a.reverse
    perm.map { |p| tensor.inputs[0].shape.shape[p] }
  when :stack
    return nil unless shape_full_specified(tensor.inputs[0])

    axis = tensor.options[:axis] || 0
    new_shape = [tensor.inputs.size]
    tensor.inputs[0].shape.shape.inject(new_shape) { |ns, i| ns << i }
    rank = tensor.inputs[0].shape.shape.size + 1
    axis = rank + axis if axis < 0
    rotated_shape = Array.new(axis + 1) { new_shape.shift }
    rotated_shape.rotate! + new_shape
  when :concat
    return nil if tensor.inputs[0].const_value.nil?

    axis = tensor.inputs[0].const_value # get axis

    axis_size = 0

    tensor.inputs[1..tensor.inputs.size].each do |input_item|
      return nil if input_item.shape.shape.nil?
      return nil if input_item.shape.shape[axis].nil?

      axis_size += input_item.shape.shape[axis]
    end

    new_shape = tensor.inputs[1].shape.shape.dup
    new_shape[axis] = axis_size
    new_shape
  when :slice, :squeeze
    nil
  when :broadcast_gradient_args
    nil
  when :no_op
    nil
  when :softmax_cross_entropy_with_logits_v2, :sparse_softmax_cross_entropy_with_logits
    nil
  when :decode_png, :flow_dynamic_stitch, :dynamic_stitch, :gather
    nil
  when :eye
    return [tensor.inputs[0].const_value, tensor.inputs[1].const_value] if tensor.inputs[0].const_value && tensor.inputs[1].const_value

    nil
  when :unstack
    return nil unless tensor.inputs[0].shape.known?

    new_shape = tensor.inputs[0].shape.shape.dup
    rank = new_shape.size - 1
    axis = tensor.options[:axis] || 0
    axis = rank + axis if axis < 0
    rotated_shape = Array.new(axis + 1) { new_shape.shift }
    rotated_shape.rotate!(-1) + new_shape
  when :conv2d
    return nil unless tensor.inputs[0].shape.known?
    return nil unless tensor.inputs[1].shape.known?

    new_shape = tensor.inputs[0].shape.shape.dup
    new_shape[3] = tensor.inputs[1].shape.shape[3]

    # account for stride and padding options
    strides = tensor.options[:strides]

    case tensor.options[:padding]
    when "SAME"
      new_shape[1] /= strides[1]
      new_shape[2] /= strides[2]
    when "VALID"
      new_shape[1] = (new_shape[1] - tensor.inputs[1].shape.shape[0]) / strides[1] + 1
      new_shape[2] = (new_shape[2] - tensor.inputs[1].shape.shape[1]) / strides[2] + 1
    else
      raise TensorStream::ValueError, "Invalid padding option only 'SAME', 'VALID' accepted"
    end

    new_shape
  when :conv2d_backprop_input
    return nil unless tensor.inputs[0].const_value

    tensor.inputs[0].const_value
  else
    TensorStream::OpMaker.infer_shape(self, tensor)
  end
end