pytorch中的自定义反向传播,求导实例

import torch
import numpy as np
from PIL import Image
from torch.autograd import gradcheck
class Bicubic(torch.autograd.Function):
def basis_function(self, x, a=-1):
  x_abs = np.abs(x)
  if x_abs < 1 and x_abs >= 0:
    y = (a + 2) * np.power(x_abs, 3) - (a + 3) * np.power(x_abs, 2) + 1
  elif x_abs > 1 and x_abs < 2:
    y = a * np.power(x_abs, 3) - 5 * a * np.power(x_abs, 2) + 8 * a * x_abs - 4 * a
  else:
    y = 0
  return y
def bicubic_interpolate(self,data_in, scale=1 / 4, mode='edge'):
  # data_in = data_in.detach().numpy()
  self.grad = np.zeros(data_in.shape,dtype=np.float32)
  obj_shape = (int(data_in.shape[0] * scale), int(data_in.shape[1] * scale), data_in.shape[2])
  data_tmp = data_in.copy()
  data_obj = np.zeros(shape=obj_shape, dtype=np.float32)
  data_in = np.pad(data_in, pad_width=((2, 2), (2, 2), (0, 0)), mode=mode)
  print(data_tmp.shape)
  for axis0 in range(obj_shape[0]):
    f_0 = float(axis0) / scale - np.floor(axis0 / scale)
    int_0 = int(axis0 / scale) + 2
    axis0_weight = np.array(
      [[self.basis_function(1 + f_0), self.basis_function(f_0), self.basis_function(1 - f_0), self.basis_function(2 - f_0)]])
    for axis1 in range(obj_shape[1]):
      f_1 = float(axis1) / scale - np.floor(axis1 / scale)
      int_1 = int(axis1 / scale) + 2
      axis1_weight = np.array(
        [[self.basis_function(1 + f_1), self.basis_function(f_1), self.basis_function(1 - f_1), self.basis_function(2 - f_1)]])
      nbr_pixel = np.zeros(shape=(obj_shape[2], 4, 4), dtype=np.float32)
      grad_point = np.matmul(np.transpose(axis0_weight, (1, 0)), axis1_weight)
      for i in range(4):
        for j in range(4):
          nbr_pixel[:, i, j] = data_in[int_0 + i - 1, int_1 + j - 1, :]
          for ii in range(data_in.shape[2]):
            self.grad[int_0 - 2 + i - 1, int_1 - 2 + j - 1, ii] = grad_point[i,j]
      tmp = np.matmul(axis0_weight, nbr_pixel)
      data_obj[axis0, axis1, :] = np.matmul(tmp, np.transpose(axis1_weight, (1, 0)))[:, 0, 0]
      # img = np.transpose(img[0, :, :, :], [1, 2, 0])
  return data_obj

def forward(self,input):
  print(type(input))
  input_ = input.detach().numpy()
  output = self.bicubic_interpolate(input_)
  # return input.new(output)
  return torch.Tensor(output)

def backward(self,grad_output):
  print(self.grad.shape,grad_output.shape)
  grad_output.detach().numpy()
  grad_output_tmp = np.zeros(self.grad.shape,dtype=np.float32)
  for i in range(self.grad.shape[0]):
    for j in range(self.grad.shape[1]):
      grad_output_tmp[i,j,:] = grad_output[int(i/4),int(j/4),:]
  grad_input = grad_output_tmp*self.grad
  print(type(grad_input))
  # return grad_output.new(grad_input)
  return torch.Tensor(grad_input)

def bicubic(input):
return Bicubic()(input)

def main():
	hr = Image.open('./baboon/baboon_hr.png').convert('L')
	hr = torch.Tensor(np.expand_dims(np.array(hr), axis=2))
	hr.requires_grad = True
	lr = bicubic(hr)
	print(lr.is_leaf)
	loss=torch.mean(lr)
	loss.backward()
if __name__ =='__main__':
	main()