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

"""

Creating extensions using numpy and scipy

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

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

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 it’s implementation

2. Create a neural network layer that has learnable weights

- This calls into **SciPy** as part of it’s implementation

"""

import torch

from torch.autograd import Function

from torch.autograd import Variable

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

# 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):

def forward(self, input):

numpy_input = input.numpy()

result = abs(rfft2(numpy_input))

return torch.FloatTensor(result)

def backward(self, grad_output):

numpy_go = grad_output.numpy()

result = irfft2(numpy_go)

return torch.FloatTensor(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()(input)

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

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

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

result = incorrect_fft(input)

print(result.data)

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

print(input.grad)

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

# Parametrized example

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

#

# This implements a layer with learnable weights.

#

# It implements the Cross-correlation with a learnable kernel.

#

# In deep learning literature, it’s confusingly referred to as

# Convolution.

#

# The backward computes the gradients wrt the input and gradients wrt the

# filter.

#

# **Implementation:**

#

# *Please Note that the implementation serves as an illustration, and we

# did not verify it’s correctness*

from scipy.signal import convolve2d, correlate2d

from torch.nn.modules.module import Module

from torch.nn.parameter import Parameter

class ScipyConv2dFunction(Function):

def forward(self, input, filter):

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

self.save_for_backward(input, filter)

return torch.FloatTensor(result)

def backward(self, grad_output):

input, filter = self.saved_tensors

grad_input = convolve2d(grad_output.numpy(), filter.t().numpy(), mode='full')

grad_filter = convolve2d(input.numpy(), grad_output.numpy(), mode='valid')

return torch.FloatTensor(grad_input), torch.FloatTensor(grad_filter)

class ScipyConv2d(Module):

def __init__(self, kh, kw):

super(ScipyConv2d, self).__init__()

self.filter = Parameter(torch.randn(kh, kw))

def forward(self, input):

return ScipyConv2dFunction()(input, self.filter)

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

# **Example usage:**

module = ScipyConv2d(3, 3)

print(list(module.parameters()))

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

output = module(input)

print(output)

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

print(input.grad)