@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
29 lines • 1.34 kB
JavaScript
import { convertToTensor } from '../tensor_util_env';
import { conv3DBackpropInput } from './conv3d_backprop_input';
import { op } from './operation';
/**
* Computes the transposed 3D convolution of a volume, also known as a
* deconvolution.
*
* @param x The input image, of rank 5 or rank 4, of shape
* `[batch, depth, height, width, inDepth]`. If rank 4, batch of 1 is assumed.
* @param filter The filter, rank 4, of shape
* `[depth, filterHeight, filterWidth, outDepth, inDepth]`.
* `inDepth` must match `inDepth` in `x`.
* @param outputShape Output shape, of rank 5 or rank 4:
* `[batch, depth, height, width, outDepth]`. If rank 3, batch of 1 is
* assumed.
* @param strides The strides of the original convolution:
* `[strideDepth, strideHeight, strideWidth]`.
* @param pad The type of padding algorithm used in the non-transpose version
* of the op.
*
* @doc {heading: 'Operations', subheading: 'Convolution'}
*/
function conv3dTranspose_(x, filter, outputShape, strides, pad) {
const $x = convertToTensor(x, 'x', 'conv3dTranspose');
const $filter = convertToTensor(filter, 'filter', 'conv3dTranspose');
return conv3DBackpropInput(outputShape, $x, $filter, strides, pad);
}
export const conv3dTranspose = op({ conv3dTranspose_ });
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