Class: Daimond::NN::MaxPool2d

Inherits:
Module
  • Object
show all
Defined in:
lib/daimond/nn/max_pool2d.rb

Instance Method Summary collapse

Methods inherited from Module

#call, #load, #parameters, #save, #zero_grad

Constructor Details

#initialize(kernel_size, stride: nil) ⇒ MaxPool2d

Returns a new instance of MaxPool2d.



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# File 'lib/daimond/nn/max_pool2d.rb', line 6

def initialize(kernel_size, stride: nil)
  super()
  @kernel_size = kernel_size.is_a?(Array) ? kernel_size : [kernel_size, kernel_size]
  @stride = stride || kernel_size
  @mask = nil  # для backward
end

Instance Method Details

#forward(input) ⇒ Object 



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# File 'lib/daimond/nn/max_pool2d.rb', line 13

def forward(input)
  # input: [batch, channels, h, w]
  batch_size = input.shape[0]
  channels = input.shape[1]
  h_in = input.shape[2]
  w_in = input.shape[3]

  k_h, k_w = @kernel_size
  s = @stride

  h_out = (h_in - k_h) / s + 1
  w_out = (w_in - k_w) / s + 1

  output = Numo::DFloat.zeros(batch_size, channels, h_out, w_out)
  @mask = {}  # запоминаем индексы максимумов

  batch_size.times do |b|
    channels.times do |c|
      h_out.times do |i|
        w_out.times do |j|
          # Окно пулинга
          i0 = i * s
          j0 = j * s
          window = input.data[b, c, i0...i0+k_h, j0...j0+k_w]

          max_val = window.max
          output[b, c, i, j] = max_val

          # Сохраняем позицию максимума для backward
          max_idx = window.to_a.flatten.index(max_val)
          @mask[[b, c, i, j]] = [i0 + max_idx / k_w, j0 + max_idx % k_w]
        end
      end
    end
  end

  out = Tensor.new(output, prev: [input], op: 'maxpool2d')

  out._backward = lambda do
    grad = out.grad
    batch_size.times do |b|
      channels.times do |c|
        h_out.times do |i|
          w_out.times do |j|
            idx_i, idx_j = @mask[[b, c, i, j]]
            input.grad[b, c, idx_i, idx_j] += grad[b, c, i, j]
          end
        end
      end
    end
  end

  out
end