import torch
from torch.autograd import Function
from torch.autograd import Variable

class BinarizedF(Function):
  def forward(self, input):
    self.save_for_backward(input)
    a = torch.ones_like(input)
    b = -torch.ones_like(input)
    output = torch.where(input>=0,a,b)
    return output
  def backward(self, output_grad):
    input, = self.saved_tensors
    input_abs = torch.abs(input)
    ones = torch.ones_like(input)
    zeros = torch.zeros_like(input)
    input_grad = torch.where(input_abs<=1,ones, zeros)
    return input_grad

class BinarizedModule(nn.Module):
  def __init__(self):
    super(BinarizedModule, self).__init__()
    self.BF = BinarizedF()
  def forward(self,input):
    print(input.shape)
    output =self.BF(input)
    return output

a = Variable(torch.randn(4,480,640), requires_grad=True)
output = BinarizedModule()(a)
output.backward(torch.ones(a.size()))
print(a)
print(a.grad)

class BinarizedF(Function):
  def forward(self, input):
    self.save_for_backward(input)
    output = torch.ones_like(input)
    output[input<0] = -1
    return output
  def backward(self, output_grad):
    input, = self.saved_tensors
    input_grad = output_grad.clone()
    input_abs = torch.abs(input)
    input_grad[input_abs>1] = 0
    return input_grad