conv3d_transpose#

ivy.conv3d_transpose(x, filters, strides, padding, /, *, output_shape=None, data_format='NDHWC', dilations=1, out=None)[source]#

Compute a 3-D transpose convolution given 5-D input x and filters arrays.

Parameters:

  • x (Union[Array, NativeArray]) – Input volume [batch_size,d,h,w,d_in] or [batch_size,d_in,d,h,w].

  • filters (Union[Array, NativeArray]) – Convolution filters [fd,fh,fw,d_in,d_out].

  • strides (Union[int, Tuple[int, int, int]]) – The stride of the sliding window for each dimension of input.

  • padding (str) – Either ‘SAME’ (padding so that the output’s shape is the same as the input’s), or ‘VALID’ (padding so that the output’s shape is output_shape).

  • output_shape (Optional[Union[Shape, NativeShape]]) – Shape of the output (Default value = None) (default: None)

  • data_format (str) – The ordering of the dimensions in the input, one of “NDHWC” or “NCDHW”. “NDHWC” (default: 'NDHWC') corresponds to inputs with shape (batch_size, depth, height, width, channels), while “NCDHW” corresponds to input with shape (batch_size, channels, depth, height, width).

  • dilations (Union[int, Tuple[int, int, int]]) – The dilation factor for each dimension of input. (Default value = 1) (default: 1)

  • out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – The result of the transpose convolution operation.

Examples

With ivy.Array input:

>>> x = ivy.random_normal(mean=0, std=1, shape=[1, 3, 28, 28, 3])
>>> filters = ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 3, 6])
>>> y = ivy.conv3d_transpose(x, filters, 2, 'SAME')
>>> print(y.shape)
(1, 6, 56, 56, 6)

>>> x = ivy.random_normal(mean=0, std=1, shape=[1, 7, 256, 256, 64])
>>> filters = ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 64, 32])
>>> y = ivy.conv3d_transpose(x, filters, [1, 1, 1], 'VALID')
>>> print(y.shape)
(1, 9, 258, 258, 32)

With ivy.Container inputs:

>>> a = ivy.random_normal(mean=0, std=1, shape=[1, 3, 14, 14, 3])
>>> b = ivy.random_normal(mean=0, std=1, shape=[1, 3, 28, 28, 3]))
>>> c = ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 3, 6])
>>> d = ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 3, 6]))
>>> x = ivy.Container(a=a, b=b)
>>> filters = ivy.Container(c=c, d=d)
>>> y = ivy.conv3d_transpose(x, filters, 2, 'SAME')
>>> print(y.shape)
[1, 6, 56, 56, 6]

With a mix of ivy.Array and ivy.Container inputs:

>>> x = ivy.full((1, 6, 6, 6, 1), 2.7)
>>> a =  ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 1, 1])
>>> b =  ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 1, 1])
>>> filters = ivy.Container(a = a, b = b)
>>> y = ivy.conv3d_transpose(x, filters, 1, 'VALID', dilations=1)
>>> print(y.shape)
[1, 8, 8, 8, 1]

>>> x = ivy.full((1, 6, 6, 6, 1), 1.23)
>>> a =  ivy.array(ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 1, 1]))
>>> b =  ivy.array(ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 1, 1]))
>>> filters = ivy.Container(a = a, b = b)
>>> y = ivy.conv3d_transpose(x, filters, 1, 'VALID', dilations=1)
>>> print(y.shape)
[1, 8, 8, 8, 1]
Array.conv3d_transpose(self, filters, strides, padding, /, *, output_shape=None, data_format='NDHWC', dilations=1, out=None)#

ivy.Array instance method variant of ivy.conv3d_transpose. This method simply wraps the function, and so the docstring for ivy.conv3d_transpose also applies to this method with minimal changes.

Parameters:

  • self (Array) – Input volume [batch_size,d,h,w,d_in] or [batch_size,d_in,d,h,w].

  • filters (Union[Array, NativeArray]) – Convolution filters [fd,fh,fw,d_in,d_out].

  • strides (Union[int, Tuple[int], Tuple[int, int], Tuple[int, int, int]]) – The stride of the sliding window for each dimension of input.

  • padding (Union[str, List[int]]) – “SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

  • output_shape (Optional[Union[Shape, NativeShape]]) – Shape of the output (Default value = None) (default: None)

  • data_format (str) –

    The ordering of the dimensions in the input, one of “NDHWC” or (default: 'NDHWC') “NCDHW”. “NDHWC” corresponds to inputs with shape (batch_size,

    depth, height, width, channels), while “NCDHW” corresponds to input with shape (batch_size, channels, depth, height, width).

  • dilations (Union[int, Tuple[int], Tuple[int, int], Tuple[int, int, int]]) – The dilation factor for each dimension of input. (Default value = 1) (default: 1)

  • out (Optional[Array]) – optional output array, for writing the result to. It must have a (default: None) shape that the inputs broadcast to.

Return type:

Array

Returns:

ret – The result of the transpose convolution operation.

Examples

>>> x = ivy.random_normal(mean=0, std=1, shape=[1, 3, 28, 28, 3])
>>> filters = ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 3, 6])
>>> y = x.conv3d_transpose(filters, 2, 'SAME')
>>> print(y.shape)
(1, 6, 56, 56, 6)
Container.conv3d_transpose(self, filters, strides, padding, /, *, output_shape=None, data_format='NDHWC', dilations=1, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)#

ivy.Container instance method variant of ivy.conv3d_transpose. This method simply wraps the function, and so the docstring for ivy.conv3d_transpose also applies to this method with minimal changes.

Parameters:

  • self (Container) – Input container with leaves of volume [batch_size,d,h,w,d_in] or [batch_size,d_in,d,h,w].

  • filters (Union[Array, NativeArray, Container]) – Convolution filters [fd,fh,fw,d_in,d_out].

  • strides (Union[int, Tuple[int], Tuple[int, int], Tuple[int, int, int]]) – The stride of the sliding window for each dimension of input.

  • padding (Union[str, List[int]]) – “SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

  • output_shape (Optional[Union[Array, NativeArray, Container]]) – Shape of the output (Default value = None) (default: None)

  • data_format (str) –

    The ordering of the dimensions in the input, one of “NDHWC” or (default: 'NDHWC') “NCDHW”. “NDHWC” corresponds to inputs with shape (batch_size,

    depth, height, width, channels), while “NCDHW” corresponds to input with shape (batch_size, channels, depth, height, width).

  • dilations (Union[int, Tuple[int], Tuple[int, int], Tuple[int, int, int]]) – The dilation factor for each dimension of input. (Default value = 1) (default: 1)

  • out (Optional[Container]) – optional output container, for writing the result to. It must (default: None) have a shape that the inputs broadcast to.

Return type:

Container

Returns:

  • ret – The result of the transpose convolution operation in a container.

  • >>> a = ivy.random_normal(mean=0, std=1, shape=[1, 3, 14, 14, 3])

  • >>> b = ivy.random_normal(mean=0, std=1, shape=[1, 3, 28, 28, 3]))

  • >>> c = ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 3, 6])

  • >>> d = ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 3, 6]))

  • >>> x = ivy.Container(a=a, b=b)

  • >>> filters = ivy.Container(c=c, d=d)

  • >>> y = x.conv3d_transpose(filters, 2, ‘SAME’)

  • >>> print(y.shape)

  • { –

    a: {

    c: [1, 6, 28, 28, 6], d: [1, 6, 28, 28, 6]

    }, b: {

    c: [1, 6, 56, 56, 6], d: [1, 6, 56, 56, 6]

    }

  • }