def compute_convolution_nd(data, kernel, dimension: int, mode=ConvolutionMode.valid, element_wise: bool=False):
mode_string = __get_convolution_mode_string(mode)
result = []
data_prefix_shape = data.shape[:-2]
kernel_prefix_shape = kernel.shape[:-2]
if element_wise:
final_shape = element_wise_shape(data_prefix_shape, kernel_prefix_shape)[0]
data = numpy.broadcast_to(data, final_shape + data.shape[-2:])
kernel = numpy.broadcast_to(kernel, final_shape + kernel.shape[-2:])
if final_shape:
for index in array_index_traversal(final_shape):
result.append(__compute_convolution_nd(data[index], kernel[index], dimension, mode_string))
return numpy.array(result).reshape(final_shape + result[0].shape)
else:
return __compute_convolution_nd(data, kernel, dimension, mode_string)
else:
if data_prefix_shape:
for data_index in array_index_traversal(data_prefix_shape):
if kernel_prefix_shape:
for kernel_index in array_index_traversal(kernel_prefix_shape):
result.append(__compute_convolution_nd(data[data_index], kernel[kernel_index], dimension, mode_string))
else:
result.append(__compute_convolution_nd(data[data_index], kernel, dimension, mode_string))
final_shape = data_prefix_shape + kernel_prefix_shape + basic_convolution_shape(data.shape[-2:], kernel.shape[-2:], 2, mode_string)
return numpy.array(result).reshape(final_shape)
else:
if kernel_prefix_shape:
for kernel_index in array_index_traversal(kernel_prefix_shape):
result.append(__compute_convolution_nd(data, kernel[kernel_index], dimension, mode_string))
final_shape = data_prefix_shape + kernel_prefix_shape + basic_convolution_shape(data.shape[-2:], kernel.shape[-2:], 2, mode_string)
return numpy.array(result).reshape(final_shape)
else:
return __compute_convolution_nd(data, kernel, dimension, mode_string)
def max_unpooling_shape(shape_data, shape_pooling, dimension):
prefix_shape, prefix_broadcast_data, prefix_broadcast_kernel = element_wise_shape(
shape_data[:-dimension], shape_pooling[:-dimension])
new_shape = prefix_shape + tuple(shape_data[i]
for i in range(-dimension, 0))
return new_shape, prefix_broadcast_data + (
0, ) * dimension, prefix_broadcast_kernel + (0, ) * dimension
def element_wise_convolution_nd_shape(shape_data, shape_kernel, dimension, mode):
prefix_shape, prefix_broadcast_data, prefix_broadcast_kernel = element_wise_shape(shape_data[:-dimension], shape_kernel[:-dimension])
if mode == 'valid' or mode == ConvolutionMode.valid:
new_shape = prefix_shape + tuple(shape_data[i] - shape_kernel[i] + 1 for i in range(-dimension, 0))
elif mode == 'full' or mode == ConvolutionMode.full:
new_shape = prefix_shape + tuple(shape_data[i] + shape_kernel[i] - 1 for i in range(-dimension, 0))
else:
raise ValueError('Invalid convolution mode: {}'.format(mode))
return new_shape, prefix_broadcast_data + (0,) * dimension, prefix_broadcast_kernel + (0,) * dimension
def unpooling_shape(shape_pooling, size, step, unpooling_size, dimension):
prefix_shape, prefix_broadcast_data, prefix_broadcast_kernel = element_wise_shape(
shape_pooling[:-dimension], shape_pooling[:-dimension])
new_shape = prefix_shape + tuple(
(size[i] + (shape_pooling[i] - 1) *
step[i]) if unpooling_size is None else unpooling_size[i]
for i in range(-dimension, 0))
return new_shape, prefix_broadcast_data + (
0, ) * dimension, prefix_broadcast_kernel + (0, ) * dimension
def compute_max_unpooling_nd(data, pooling, size, step, dimension: int):
result = []
data_prefix_shape = data.shape[:-dimension]
kernel_prefix_shape = pooling.shape[:-dimension]
final_shape = element_wise_shape(data_prefix_shape, kernel_prefix_shape)[0]
data = numpy.broadcast_to(data, final_shape + data.shape[-dimension:])
pooling = numpy.broadcast_to(pooling, final_shape + pooling.shape[-dimension:])
if final_shape:
for key in array_index_traversal(final_shape):
result.append(__compute_max_unpooling_nd(data[key], pooling[key], size, step, dimension))
return numpy.array(result).reshape(final_shape + result[0].shape)
else:
return __compute_max_unpooling_nd(data, pooling, size, step, dimension)
def compute_convolution_nd(data,
kernel,
dimension: int,
mode=ConvolutionMode.valid,
element_wise: bool = False):
mode_string = __get_recurrent_mode_string(mode)
result = []
data_prefix_shape = data.shape[:-dimension]
kernel_prefix_shape = kernel.shape[:-dimension]
if element_wise:
final_shape = element_wise_shape(data_prefix_shape,
kernel_prefix_shape)[0]
data = numpy.broadcast_to(data, final_shape + data.shape[-2:])
kernel = numpy.broadcast_to(kernel, final_shape + kernel.shape[-2:])
if final_shape:
for index in array_index_traversal(final_shape):
result.append(
__compute_convolution_nd(data[index], kernel[index],
dimension, mode_string))
return numpy.array(result).reshape(final_shape + result[0].shape)
else:
return __compute_convolution_nd(data, kernel, dimension,
mode_string)
else:
if kernel_prefix_shape:
final_shape = data_prefix_shape + kernel_prefix_shape + basic_convolution_shape(
data.shape[-dimension:], kernel.shape[-dimension:], dimension,
mode_string)
result = numpy.zeros(final_shape)
for kernel_index in array_index_traversal(kernel_prefix_shape):
sub_result_index = tuple(
slice(None)
for _ in data_prefix_shape) + kernel_index + tuple(
slice(None) for _ in range(dimension))
result[sub_result_index] = __compute_convolution_nd(
data, kernel[kernel_index], dimension, mode_string)
return result
else:
return __compute_convolution_nd(data, kernel, dimension,
mode_string)