conv_general_transpose

ivy.conv_general_transpose(x, filters, strides, padding, /, *, dims=2, output_shape=None, filter_format='channel_last', data_format='channel_last', dilations=1, feature_group_count=1, bias=None, out=None)

Compute a 1-D, 2-D, and 3-D transpose convolution given 3-D, 4-D and 5-D input x respectively and filters arrays.

Parameters:

• x (Union[Array, NativeArray]) – Input image [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_out,d_in].

• strides (Union[int, Tuple[int], Tuple[int, 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).

• dims (int, default: 2) – Either 1, 2, or 3 corresponding to 1-D, 2-D, and 3-D convolution.

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

• filter_format (str, default: 'channel_last') – Either “channel_first” or “channel_last”. “channel_first” corresponds to “IODHW”,input data formats, while “channel_last” corresponds to “DHWOI”.

• data_format (str, default: 'channel_last') – Either “channel_first” or “channel_last”. “channel_first” corresponds to “NCW”, “NCHW”, “NCDHW” input data formatS for 1-D, 2-D, 3-D convolution respectively, while “channel_last” corresponds to “NWC”, “NHWC”, “NDHWC” respectively.

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

• feature_group_count (int, default: 1) – split input into groups, d_in should be divisible by the number of groups.

• bias (Optional[Union[Array, NativeArray]], default: None) – Bias array of shape [d_out].

• out (Optional[Array], default: None) – optional output array, for writing the result to. It must have a shape that the 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, 6, 3]) >>> y = ivy.conv3d_transpose(x, filters, [2, 2, 2], ‘SAME’) >>> print(y.shape) ivy.Shape(1, 6, 56, 56, 6) >>> x = ivy.random_normal(mean=0, std=1, shape=[1, 3, 64, 64, 3]) >>> filters = ivy.random_normal(mean=0, std=1, shape=[3, 3, 3, 6, 3]) >>> y = ivy.conv3d_transpose(x, filters, [2, 2, 2], ‘VALID’, dilations=[1, 1, 1]) >>> print(y.shape) ivy.Shape(1, 7, 129, 129, 6) With :class: ‘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=[6, 3, 3, 3, 3]) >>> d = ivy.random_normal(mean=0, std=1, shape=[6, 3, 3, 3, 3]) >>> x = ivy.Container(a=a, b=b) >>> filters = ivy.Container(c=c, d=d) >>> y = ivy.conv3d_transpose(x, filters, [2, 2, 2], ‘SAME’) >>> print(y.shape) {

a: {

c: ivy.Shape(1, 6, 28, 28, 3), d: ivy.Shape(1, 6, 28, 28, 3)

}, b: {

c: ivy.Shape(1, 6, 56, 56, 3), d: ivy.Shape(1, 6, 56, 56, 3)

}, c: {

c: ivy.Shape(6, 6, 6, 6, 3), d: ivy.Shape(6, 6, 6, 6, 3)

}, d: {

c: ivy.Shape(6, 6, 6, 6, 3), d: ivy.Shape(6, 6, 6, 6, 3)

}

} 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, 1, 1], ‘VALID’, dilations=[1, 1, 1]) >>> print(y.shape) {

a: ivy.Shape(1, 8, 8, 8, 1), b: ivy.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, 1, 1], ‘VALID’, dilations=[1, 1, 1]) >>> print(y.shape) {

a: ivy.Shape(1, 8, 8, 8, 1), b: ivy.Shape(1, 8, 8, 8, 1)

}