arrayfire-python/arrayfire/ml.py at approx_complex · syurkevi/arrayfire-python · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
#######################################################
# Copyright (c) 2020, ArrayFire
# All rights reserved.
#
# This file is distributed under 3-clause BSD license.
# The complete license agreement can be obtained at:
# http://arrayfire.com/licenses/BSD-3-Clause
########################################################

"""
Machine learning functions
    - Pool 2D, ND, maxpooling, minpooling, meanpooling
    - Forward and backward convolution passes
"""

from .library import *
from .array import *

def convolve2GradientNN(incoming_gradient, original_signal, original_kernel, convolved_output, stride = (1, 1), padding = (0, 0), dilation = (1, 1), gradType = CONV_GRADIENT.DEFAULT):
    """
    This version of convolution is consistent with the machine learning
    formulation that will spatially convolve a filter on 2-dimensions against a
    signal. Multiple signals and filters can be batched against each other.
    Furthermore, the signals and filters can be multi-dimensional however their
    dimensions must match.

    Example:
        Signals with dimensions: d0 x d1 x d2 x Ns
        Filters with dimensions: d0 x d1 x d2 x Nf

        Resulting Convolution:   d0 x d1 x Nf x Ns

    Parameters
    -----------

    signal: af.Array
            - A 2 dimensional signal or batch of 2 dimensional signals.

    kernel: af.Array
            - A 2 dimensional kernel or batch of 2 dimensional kernels.

    stride: tuple of ints. default: (1, 1).
            - Specifies how much to stride along each dimension

    padding: tuple of ints. default: (0, 0).
            - Specifies signal padding along each dimension

    dilation: tuple of ints. default: (1, 1).
            - Specifies how much to dilate kernel along each dimension before convolution

    Returns
    --------

    output: af.Array
          - Gradient wrt/requested gradient type

    """
    output = Array()
    stride_dim   = dim4(stride[0],   stride[1])
    padding_dim  = dim4(padding[0],  padding[1])
    dilation_dim = dim4(dilation[0], dilation[1])

    safe_call(backend.get().af_convolve2_gradient_nn(
                                            c_pointer(output.arr),
                                            incoming_gradient.arr,
                                            original_signal.arr,
                                            original_kernel.arr,
                                            convolved_output.arr,
                                            2, c_pointer(stride_dim),
                                            2, c_pointer(padding_dim),
                                            2, c_pointer(dilation_dim),
                                            gradType.value))
    return output