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# -*- coding: utf-8 -*-

"""

Creating Extensions Using numpy and scipy

=========================================

**Author**: `Adam Paszke <https://github.com/apaszke>`_

**Updated by**: `Adam Dziedzic <https://github.com/adam-dziedzic>`_

In this tutorial, we shall go through two tasks:

1. Create a neural network layer with no parameters.

- This calls into **numpy** as part of its implementation

2. Create a neural network layer that has learnable weights

- This calls into **SciPy** as part of its implementation

"""

import torch

from torch.autograd import Function

###############################################################

# Parameter-less example

# ----------------------

#

# This layer doesn’t particularly do anything useful or mathematically

# correct.

#

# It is aptly named BadFFTFunction

#

# **Layer Implementation**

from numpy.fft import rfft2, irfft2

class BadFFTFunction(Function):

@staticmethod

def forward(ctx, input):

numpy_input = input.detach().numpy()

result = abs(rfft2(numpy_input))

return input.new(result)

@staticmethod

def backward(ctx, grad_output):

numpy_go = grad_output.numpy()

result = irfft2(numpy_go)

return grad_output.new(result)

# since this layer does not have any parameters, we can

# simply declare this as a function, rather than as an nn.Module class

def incorrect_fft(input):

return BadFFTFunction.apply(input)

###############################################################

# **Example usage of the created layer:**

input = torch.randn(8, 8, requires_grad=True)

result = incorrect_fft(input)

print(result)

result.backward(torch.randn(result.size()))

print(input)

###############################################################

# Parametrized example

# --------------------

#

# In deep learning literature, this layer is confusingly referred

# to as convolution while the actual operation is cross-correlation

# (the only difference is that filter is flipped for convolution,

# which is not the case for cross-correlation).

#

# Implementation of a layer with learnable weights, where cross-correlation

# has a filter (kernel) that represents weights.

#

# The backward pass computes the gradient wrt the input and the gradient wrt the filter.

from numpy import flip

import numpy as np

from scipy.signal import convolve2d, correlate2d

from torch.nn.modules.module import Module

from torch.nn.parameter import Parameter

class ScipyConv2dFunction(Function):

@staticmethod

def forward(ctx, input, filter, bias):

# detach so we can cast to NumPy

input, filter, bias = input.detach(), filter.detach(), bias.detach()

result = correlate2d(input.numpy(), filter.numpy(), mode='valid')

result += bias.numpy()

ctx.save_for_backward(input, filter, bias)

return torch.as_tensor(result, dtype=input.dtype)

@staticmethod

def backward(ctx, grad_output):

grad_output = grad_output.detach()

input, filter, bias = ctx.saved_tensors

grad_output = grad_output.numpy()

grad_bias = np.sum(grad_output, keepdims=True)

grad_input = convolve2d(grad_output, filter.numpy(), mode='full')

# the previous line can be expressed equivalently as:

# grad_input = correlate2d(grad_output, flip(flip(filter.numpy(), axis=0), axis=1), mode='full')

grad_filter = correlate2d(input.numpy(), grad_output, mode='valid')

return torch.from_numpy(grad_input), torch.from_numpy(grad_filter).to(torch.float), torch.from_numpy(grad_bias).to(torch.float)

class ScipyConv2d(Module):

def __init__(self, filter_width, filter_height):

super(ScipyConv2d, self).__init__()

self.filter = Parameter(torch.randn(filter_width, filter_height))

self.bias = Parameter(torch.randn(1, 1))

def forward(self, input):

return ScipyConv2dFunction.apply(input, self.filter, self.bias)

###############################################################

# **Example usage:**

module = ScipyConv2d(3, 3)

print("Filter and bias: ", list(module.parameters()))

input = torch.randn(10, 10, requires_grad=True)

output = module(input)

print("Output from the convolution: ", output)

output.backward(torch.randn(8, 8))

print("Gradient for the input map: ", input.grad)

###############################################################

# **Check the gradients:**

from torch.autograd.gradcheck import gradcheck

moduleConv = ScipyConv2d(3, 3)

input = [torch.randn(20, 20, dtype=torch.double, requires_grad=True)]

test = gradcheck(moduleConv, input, eps=1e-6, atol=1e-4)

print("Are the gradients correct: ", test)