Contrib NDArray API

Overview

This document lists the contrib routines of the n-dimensional array package:

mxnet.ndarray.contrib Contrib NDArray API of MXNet.

The Contrib NDArray API, defined in the ndarray.contrib package, provides many useful experimental APIs for new features. This is a place for the community to try out the new features, so that feature contributors can receive feedback.

Warning

This package contains experimental APIs and may change in the near future.

In the rest of this document, we list routines provided by the ndarray.contrib package.

Contrib

CTCLoss Connectionist Temporal Classification Loss.
DeformableConvolution Compute 2-D deformable convolution on 4-D input.
DeformablePSROIPooling Performs deformable position-sensitive region-of-interest pooling on inputs.
MultiBoxDetection Convert multibox detection predictions.
MultiBoxPrior Generate prior(anchor) boxes from data, sizes and ratios.
MultiBoxTarget Compute Multibox training targets
MultiProposal Generate region proposals via RPN
PSROIPooling Performs region-of-interest pooling on inputs.
Proposal Generate region proposals via RPN
count_sketch Apply CountSketch to input: map a d-dimension data to k-dimension data”
ctc_loss Connectionist Temporal Classification Loss.
dequantize Dequantize the input tensor into a float tensor.
fft Apply 1D FFT to input”
ifft Apply 1D ifft to input”
quantize Quantize a input tensor from float to out_type, with user-specified min_range and max_range.

API Reference

Contrib NDArray API of MXNet.

mxnet.ndarray.contrib.CTCLoss(data=None, label=None, data_lengths=None, label_lengths=None, use_data_lengths=_Null, use_label_lengths=_Null, blank_label=_Null, out=None, name=None, **kwargs)

Connectionist Temporal Classification Loss.

The shapes of the inputs and outputs:

  • data: (sequence_length, batch_size, alphabet_size)
  • label: (batch_size, label_sequence_length)
  • out: (batch_size)

The data tensor consists of sequences of activation vectors (without applying softmax), with i-th channel in the last dimension corresponding to i-th label for i between 0 and alphabet_size-1 (i.e always 0-indexed). Alphabet size should include one additional value reserved for blank label. When blank_label is "first", the 0-th channel is be reserved for activation of blank label, or otherwise if it is “last”, (alphabet_size-1)-th channel should be reserved for blank label.

label is an index matrix of integers. When blank_label is "first", the value 0 is then reserved for blank label, and should not be passed in this matrix. Otherwise, when blank_label is "last", the value (alphabet_size-1) is reserved for blank label.

If a sequence of labels is shorter than label_sequence_length, use the special padding value at the end of the sequence to conform it to the correct length. The padding value is 0 when blank_label is "first", and -1 otherwise.

For example, suppose the vocabulary is [a, b, c], and in one batch we have three sequences ‘ba’, ‘cbb’, and ‘abac’. When blank_label is "first", we can index the labels as {‘a’: 1, ‘b’: 2, ‘c’: 3}, and we reserve the 0-th channel for blank label in data tensor. The resulting label tensor should be padded to be:

[[2, 1, 0, 0], [3, 2, 2, 0], [1, 2, 1, 3]]

When blank_label is "last", we can index the labels as {‘a’: 0, ‘b’: 1, ‘c’: 2}, and we reserve the channel index 3 for blank label in data tensor. The resulting label tensor should be padded to be:

[[1, 0, -1, -1], [2, 1, 1, -1], [0, 1, 0, 2]]

out is a list of CTC loss values, one per example in the batch.

See Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks, A. Graves et al. for more information on the definition and the algorithm.

Defined in src/operator/contrib/ctc_loss.cc:L115

Parameters:
  • data (NDArray) – Input data to the ctc_loss op.
  • label (NDArray) – Ground-truth labels for the loss.
  • data_lengths (NDArray) – Lengths of data for each of the samples. Only required when use_data_lengths is true.
  • label_lengths (NDArray) – Lengths of labels for each of the samples. Only required when use_label_lengths is true.
  • use_data_lengths (boolean, optional, default=0) – Whether the data lenghts are decided by data_lengths. If false, the lengths are equal to the max sequence length.
  • use_label_lengths (boolean, optional, default=0) – Whether the label lenghts are decided by label_lengths, or derived from padding_mask. If false, the lengths are derived from the first occurrence of the value of padding_mask. The value of padding_mask is 0 when first CTC label is reserved for blank, and -1 when last label is reserved for blank. See blank_label.
  • blank_label ({'first', 'last'},optional, default='first') – Set the label that is reserved for blank label.If “first”, 0-th label is reserved, and label values for tokens in the vocabulary are between 1 and alphabet_size-1, and the padding mask is -1. If “last”, last label value alphabet_size-1 is reserved for blank label instead, and label values for tokens in the vocabulary are between 0 and alphabet_size-2, and the padding mask is 0.
  • out (NDArray, optional) – The output NDArray to hold the result.
Returns:

out – The output of this function.

Return type:

NDArray or list of NDArrays

mxnet.ndarray.contrib.DeformableConvolution(data=None, offset=None, weight=None, bias=None, kernel=_Null, stride=_Null, dilate=_Null, pad=_Null, num_filter=_Null, num_group=_Null, num_deformable_group=_Null, workspace=_Null, no_bias=_Null, layout=_Null, out=None, name=None, **kwargs)

Compute 2-D deformable convolution on 4-D input.

The deformable convolution operation is described in https://arxiv.org/abs/1703.06211

For 2-D deformable convolution, the shapes are

  • data: (batch_size, channel, height, width)
  • offset: (batch_size, num_deformable_group * kernel[0] * kernel[1], height, width)
  • weight: (num_filter, channel, kernel[0], kernel[1])
  • bias: (num_filter,)
  • out: (batch_size, num_filter, out_height, out_width).

Define:

f(x,k,p,s,d) = floor((x+2*p-d*(k-1)-1)/s)+1

then we have:

out_height=f(height, kernel[0], pad[0], stride[0], dilate[0])
out_width=f(width, kernel[1], pad[1], stride[1], dilate[1])

If no_bias is set to be true, then the bias term is ignored.

The default data layout is NCHW, namely (batch_size, channle, height, width).

If num_group is larger than 1, denoted by g, then split the input data evenly into g parts along the channel axis, and also evenly split weight along the first dimension. Next compute the convolution on the i-th part of the data with the i-th weight part. The output is obtained by concating all the g results.

If num_deformable_group is larger than 1, denoted by dg, then split the input offset evenly into dg parts along the channel axis, and also evenly split out evenly into dg parts along the channel axis. Next compute the deformable convolution, apply the i-th part of the offset part on the i-th out.

Both weight and bias are learnable parameters.

Defined in src/operator/contrib/deformable_convolution.cc:L100

Parameters:
  • data (NDArray) – Input data to the DeformableConvolutionOp.
  • offset (NDArray) – Input offset to the DeformableConvolutionOp.
  • weight (NDArray) – Weight matrix.
  • bias (NDArray) – Bias parameter.
  • kernel (Shape(tuple), required) – Convolution kernel size: (h, w) or (d, h, w)
  • stride (Shape(tuple), optional, default=[]) – Convolution stride: (h, w) or (d, h, w). Defaults to 1 for each dimension.
  • dilate (Shape(tuple), optional, default=[]) – Convolution dilate: (h, w) or (d, h, w). Defaults to 1 for each dimension.
  • pad (Shape(tuple), optional, default=[]) – Zero pad for convolution: (h, w) or (d, h, w). Defaults to no padding.
  • num_filter (int (non-negative), required) – Convolution filter(channel) number
  • num_group (int (non-negative), optional, default=1) – Number of group partitions.
  • num_deformable_group (int (non-negative), optional, default=1) – Number of deformable group partitions.
  • workspace (long (non-negative), optional, default=1024) – Maximum temperal workspace allowed for convolution (MB).
  • no_bias (boolean, optional, default=0) – Whether to disable bias parameter.
  • layout ({None, 'NCDHW', 'NCHW', 'NCW'},optional, default='None') – Set layout for input, output and weight. Empty for default layout: NCW for 1d, NCHW for 2d and NCDHW for 3d.
  • out (NDArray, optional) – The output NDArray to hold the result.
Returns:

out – The output of this function.

Return type:

NDArray or list of NDArrays

mxnet.ndarray.contrib.DeformablePSROIPooling(data=None, rois=None, trans=None, spatial_scale=_Null, output_dim=_Null, group_size=_Null, pooled_size=_Null, part_size=_Null, sample_per_part=_Null, trans_std=_Null, no_trans=_Null, out=None, name=None, **kwargs)

Performs deformable position-sensitive region-of-interest pooling on inputs. The DeformablePSROIPooling operation is described in https://arxiv.org/abs/1703.06211 .batch_size will change to the number of region bounding boxes after DeformablePSROIPooling

Parameters:
  • data (Symbol) – Input data to the pooling operator, a 4D Feature maps
  • rois (Symbol) – Bounding box coordinates, a 2D array of [[batch_index, x1, y1, x2, y2]]. (x1, y1) and (x2, y2) are top left and down right corners of designated region of interest. batch_index indicates the index of corresponding image in the input data
  • trans (Symbol) – transition parameter
  • spatial_scale (float, required) – Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal of total stride in convolutional layers
  • output_dim (int, required) – fix output dim
  • group_size (int, required) – fix group size
  • pooled_size (int, required) – fix pooled size
  • part_size (int, optional, default='0') – fix part size
  • sample_per_part (int, optional, default='1') – fix samples per part
  • trans_std (float, optional, default=0) – fix transition std
  • no_trans (boolean, optional, default=0) – Whether to disable trans parameter.
  • out (NDArray, optional) – The output NDArray to hold the result.
Returns:

out – The output of this function.

Return type:

NDArray or list of NDArrays

mxnet.ndarray.contrib.MultiBoxDetection(cls_prob=None, loc_pred=None, anchor=None, clip=_Null, threshold=_Null, background_id=_Null, nms_threshold=_Null, force_suppress=_Null, variances=_Null, nms_topk=_Null, out=None, name=None, **kwargs)

Convert multibox detection predictions.

Parameters:
  • cls_prob (NDArray) – Class probabilities.
  • loc_pred (NDArray) – Location regression predictions.
  • anchor (NDArray) – Multibox prior anchor boxes
  • clip (boolean, optional, default=1) – Clip out-of-boundary boxes.
  • threshold (float, optional, default=0.01) – Threshold to be a positive prediction.
  • background_id (int, optional, default='0') – Background id.
  • nms_threshold (float, optional, default=0.5) – Non-maximum suppression threshold.
  • force_suppress (boolean, optional, default=0) – Suppress all detections regardless of class_id.
  • variances (tuple of , optional, default=[0.1,0.1,0.2,0.2]) – Variances to be decoded from box regression output.
  • nms_topk (int, optional, default='-1') – Keep maximum top k detections before nms, -1 for no limit.
  • out (NDArray, optional) – The output NDArray to hold the result.
Returns:

out – The output of this function.

Return type:

NDArray or list of NDArrays

mxnet.ndarray.contrib.MultiBoxPrior(data=None, sizes=_Null, ratios=_Null, clip=_Null, steps=_Null, offsets=_Null, out=None, name=None, **kwargs)

Generate prior(anchor) boxes from data, sizes and ratios.

Parameters:
  • data (NDArray) – Input data.
  • sizes (tuple of , optional, default=[1]) – List of sizes of generated MultiBoxPriores.
  • ratios (tuple of , optional, default=[1]) – List of aspect ratios of generated MultiBoxPriores.
  • clip (boolean, optional, default=0) – Whether to clip out-of-boundary boxes.
  • steps (tuple of , optional, default=[-1,-1]) – Priorbox step across y and x, -1 for auto calculation.
  • offsets (tuple of , optional, default=[0.5,0.5]) – Priorbox center offsets, y and x respectively
  • out (NDArray, optional) – The output NDArray to hold the result.
Returns:

out – The output of this function.

Return type:

NDArray or list of NDArrays

mxnet.ndarray.contrib.MultiBoxTarget(anchor=None, label=None, cls_pred=None, overlap_threshold=_Null, ignore_label=_Null, negative_mining_ratio=_Null, negative_mining_thresh=_Null, minimum_negative_samples=_Null, variances=_Null, out=None, name=None, **kwargs)

Compute Multibox training targets

Parameters:
  • anchor (NDArray) – Generated anchor boxes.
  • label (NDArray) – Object detection labels.
  • cls_pred (NDArray) – Class predictions.
  • overlap_threshold (float, optional, default=0.5) – Anchor-GT overlap threshold to be regarded as a positive match.
  • ignore_label (float, optional, default=-1) – Label for ignored anchors.
  • negative_mining_ratio (float, optional, default=-1) – Max negative to positive samples ratio, use -1 to disable mining
  • negative_mining_thresh (float, optional, default=0.5) – Threshold used for negative mining.
  • minimum_negative_samples (int, optional, default='0') – Minimum number of negative samples.
  • variances (tuple of , optional, default=[0.1,0.1,0.2,0.2]) – Variances to be encoded in box regression target.
  • out (NDArray, optional) – The output NDArray to hold the result.
Returns:

out – The output of this function.

Return type:

NDArray or list of NDArrays

mxnet.ndarray.contrib.MultiProposal(cls_score=None, bbox_pred=None, im_info=None, rpn_pre_nms_top_n=_Null, rpn_post_nms_top_n=_Null, threshold=_Null, rpn_min_size=_Null, scales=_Null, ratios=_Null, feature_stride=_Null, output_score=_Null, iou_loss=_Null, out=None, name=None, **kwargs)

Generate region proposals via RPN

Parameters:
  • cls_score (NDArray) – Score of how likely proposal is object.
  • bbox_pred (NDArray) – BBox Predicted deltas from anchors for proposals
  • im_info (NDArray) – Image size and scale.
  • rpn_pre_nms_top_n (int, optional, default='6000') – Number of top scoring boxes to keep after applying NMS to RPN proposals
  • rpn_post_nms_top_n (int, optional, default='300') – Overlap threshold used for non-maximumsuppresion(suppress boxes with IoU >= this threshold
  • threshold (float, optional, default=0.7) – NMS value, below which to suppress.
  • rpn_min_size (int, optional, default='16') – Minimum height or width in proposal
  • scales (tuple of , optional, default=[4,8,16,32]) – Used to generate anchor windows by enumerating scales
  • ratios (tuple of , optional, default=[0.5,1,2]) – Used to generate anchor windows by enumerating ratios
  • feature_stride (int, optional, default='16') – The size of the receptive field each unit in the convolution layer of the rpn,for example the product of all stride’s prior to this layer.
  • output_score (boolean, optional, default=0) – Add score to outputs
  • iou_loss (boolean, optional, default=0) – Usage of IoU Loss
  • out (NDArray, optional) – The output NDArray to hold the result.
Returns:

out – The output of this function.

Return type:

NDArray or list of NDArrays

mxnet.ndarray.contrib.PSROIPooling(data=None, rois=None, spatial_scale=_Null, output_dim=_Null, pooled_size=_Null, group_size=_Null, out=None, name=None, **kwargs)

Performs region-of-interest pooling on inputs. Resize bounding box coordinates by spatial_scale and crop input feature maps accordingly. The cropped feature maps are pooled by max pooling to a fixed size output indicated by pooled_size. batch_size will change to the number of region bounding boxes after PSROIPooling

Parameters:
  • data (Symbol) – Input data to the pooling operator, a 4D Feature maps
  • rois (Symbol) – Bounding box coordinates, a 2D array of [[batch_index, x1, y1, x2, y2]]. (x1, y1) and (x2, y2) are top left and down right corners of designated region of interest. batch_index indicates the index of corresponding image in the input data
  • spatial_scale (float, required) – Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal of total stride in convolutional layers
  • output_dim (int, required) – fix output dim
  • pooled_size (int, required) – fix pooled size
  • group_size (int, optional, default='0') – fix group size
  • out (NDArray, optional) – The output NDArray to hold the result.
Returns:

out – The output of this function.

Return type:

NDArray or list of NDArrays

mxnet.ndarray.contrib.Proposal(cls_score=None, bbox_pred=None, im_info=None, rpn_pre_nms_top_n=_Null, rpn_post_nms_top_n=_Null, threshold=_Null, rpn_min_size=_Null, scales=_Null, ratios=_Null, feature_stride=_Null, output_score=_Null, iou_loss=_Null, out=None, name=None, **kwargs)

Generate region proposals via RPN

Parameters:
  • cls_score (NDArray) – Score of how likely proposal is object.
  • bbox_pred (NDArray) – BBox Predicted deltas from anchors for proposals
  • im_info (NDArray) – Image size and scale.
  • rpn_pre_nms_top_n (int, optional, default='6000') – Number of top scoring boxes to keep after applying NMS to RPN proposals
  • rpn_post_nms_top_n (int, optional, default='300') – Overlap threshold used for non-maximumsuppresion(suppress boxes with IoU >= this threshold
  • threshold (float, optional, default=0.7) – NMS value, below which to suppress.
  • rpn_min_size (int, optional, default='16') – Minimum height or width in proposal
  • scales (tuple of , optional, default=[4,8,16,32]) – Used to generate anchor windows by enumerating scales
  • ratios (tuple of , optional, default=[0.5,1,2]) – Used to generate anchor windows by enumerating ratios
  • feature_stride (int, optional, default='16') – The size of the receptive field each unit in the convolution layer of the rpn,for example the product of all stride’s prior to this layer.
  • output_score (boolean, optional, default=0) – Add score to outputs
  • iou_loss (boolean, optional, default=0) – Usage of IoU Loss
  • out (NDArray, optional) – The output NDArray to hold the result.
Returns:

out – The output of this function.

Return type:

NDArray or list of NDArrays

mxnet.ndarray.contrib.SparseEmbedding(data=None, weight=None, input_dim=_Null, output_dim=_Null, dtype=_Null, out=None, name=None, **kwargs)

Maps integer indices to vector representations (embeddings).

This operator maps words to real-valued vectors in a high-dimensional space, called word embeddings. These embeddings can capture semantic and syntactic properties of the words. For example, it has been noted that in the learned embedding spaces, similar words tend to be close to each other and dissimilar words far apart.

For an input array of shape (d1, ..., dK), the shape of an output array is (d1, ..., dK, output_dim). All the input values should be integers in the range [0, input_dim).

If the input_dim is ip0 and output_dim is op0, then shape of the embedding weight matrix must be (ip0, op0).

The storage type of weight must be row_sparse, and the gradient of the weight will be of row_sparse storage type, too.

Note

SparseEmbedding is designed for the use case where input_dim is very large (e.g. 100k). The operator is available on both CPU and GPU.

Examples:

input_dim = 4
output_dim = 5

// Each row in weight matrix y represents a word. So, y = (w0,w1,w2,w3)
y = [[  0.,   1.,   2.,   3.,   4.],
     [  5.,   6.,   7.,   8.,   9.],
     [ 10.,  11.,  12.,  13.,  14.],
     [ 15.,  16.,  17.,  18.,  19.]]

// Input array x represents n-grams(2-gram). So, x = [(w1,w3), (w0,w2)]
x = [[ 1.,  3.],
     [ 0.,  2.]]

// Mapped input x to its vector representation y.
SparseEmbedding(x, y, 4, 5) = [[[  5.,   6.,   7.,   8.,   9.],
                               [ 15.,  16.,  17.,  18.,  19.]],

                              [[  0.,   1.,   2.,   3.,   4.],
                               [ 10.,  11.,  12.,  13.,  14.]]]

Defined in src/operator/tensor/indexing_op.cc:L253

Parameters:
  • data (NDArray) – The input array to the embedding operator.
  • weight (NDArray) – The embedding weight matrix.
  • input_dim (int, required) – Vocabulary size of the input indices.
  • output_dim (int, required) – Dimension of the embedding vectors.
  • dtype ({'float16', 'float32', 'float64', 'int32', 'uint8'},optional, default='float32') – Data type of weight.
  • out (NDArray, optional) – The output NDArray to hold the result.
Returns:

out – The output of this function.

Return type:

NDArray or list of NDArrays

mxnet.ndarray.contrib.count_sketch(data=None, h=None, s=None, out_dim=_Null, processing_batch_size=_Null, out=None, name=None, **kwargs)

Apply CountSketch to input: map a d-dimension data to k-dimension data”

Note

count_sketch is only available on GPU.

Assume input data has shape (N, d), sign hash table s has shape (N, d), index hash table h has shape (N, d) and mapping dimension out_dim = k, each element in s is either +1 or -1, each element in h is random integer from 0 to k-1. Then the operator computs:

\[out[h[i]] += data[i] * s[i]\]

Example:

out_dim = 5
x = [[1.2, 2.5, 3.4],[3.2, 5.7, 6.6]]
h = [[0, 3, 4]]
s = [[1, -1, 1]]
mx.contrib.ndarray.count_sketch(data=x, h=h, s=s, out_dim = 5) = [[1.2, 0, 0, -2.5, 3.4],
                                                                  [3.2, 0, 0, -5.7, 6.6]]

Defined in src/operator/contrib/count_sketch.cc:L67

Parameters:
  • data (NDArray) – Input data to the CountSketchOp.
  • h (NDArray) – The index vector
  • s (NDArray) – The sign vector
  • out_dim (int, required) – The output dimension.
  • processing_batch_size (int, optional, default='32') – How many sketch vectors to process at one time.
  • out (NDArray, optional) – The output NDArray to hold the result.
Returns:

out – The output of this function.

Return type:

NDArray or list of NDArrays

mxnet.ndarray.contrib.ctc_loss(data=None, label=None, data_lengths=None, label_lengths=None, use_data_lengths=_Null, use_label_lengths=_Null, blank_label=_Null, out=None, name=None, **kwargs)

Connectionist Temporal Classification Loss.

The shapes of the inputs and outputs:

  • data: (sequence_length, batch_size, alphabet_size)
  • label: (batch_size, label_sequence_length)
  • out: (batch_size)

The data tensor consists of sequences of activation vectors (without applying softmax), with i-th channel in the last dimension corresponding to i-th label for i between 0 and alphabet_size-1 (i.e always 0-indexed). Alphabet size should include one additional value reserved for blank label. When blank_label is "first", the 0-th channel is be reserved for activation of blank label, or otherwise if it is “last”, (alphabet_size-1)-th channel should be reserved for blank label.

label is an index matrix of integers. When blank_label is "first", the value 0 is then reserved for blank label, and should not be passed in this matrix. Otherwise, when blank_label is "last", the value (alphabet_size-1) is reserved for blank label.

If a sequence of labels is shorter than label_sequence_length, use the special padding value at the end of the sequence to conform it to the correct length. The padding value is 0 when blank_label is "first", and -1 otherwise.

For example, suppose the vocabulary is [a, b, c], and in one batch we have three sequences ‘ba’, ‘cbb’, and ‘abac’. When blank_label is "first", we can index the labels as {‘a’: 1, ‘b’: 2, ‘c’: 3}, and we reserve the 0-th channel for blank label in data tensor. The resulting label tensor should be padded to be:

[[2, 1, 0, 0], [3, 2, 2, 0], [1, 2, 1, 3]]

When blank_label is "last", we can index the labels as {‘a’: 0, ‘b’: 1, ‘c’: 2}, and we reserve the channel index 3 for blank label in data tensor. The resulting label tensor should be padded to be:

[[1, 0, -1, -1], [2, 1, 1, -1], [0, 1, 0, 2]]

out is a list of CTC loss values, one per example in the batch.

See Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks, A. Graves et al. for more information on the definition and the algorithm.

Defined in src/operator/contrib/ctc_loss.cc:L115

Parameters:
  • data (NDArray) – Input data to the ctc_loss op.
  • label (NDArray) – Ground-truth labels for the loss.
  • data_lengths (NDArray) – Lengths of data for each of the samples. Only required when use_data_lengths is true.
  • label_lengths (NDArray) – Lengths of labels for each of the samples. Only required when use_label_lengths is true.
  • use_data_lengths (boolean, optional, default=0) – Whether the data lenghts are decided by data_lengths. If false, the lengths are equal to the max sequence length.
  • use_label_lengths (boolean, optional, default=0) – Whether the label lenghts are decided by label_lengths, or derived from padding_mask. If false, the lengths are derived from the first occurrence of the value of padding_mask. The value of padding_mask is 0 when first CTC label is reserved for blank, and -1 when last label is reserved for blank. See blank_label.
  • blank_label ({'first', 'last'},optional, default='first') – Set the label that is reserved for blank label.If “first”, 0-th label is reserved, and label values for tokens in the vocabulary are between 1 and alphabet_size-1, and the padding mask is -1. If “last”, last label value alphabet_size-1 is reserved for blank label instead, and label values for tokens in the vocabulary are between 0 and alphabet_size-2, and the padding mask is 0.
  • out (NDArray, optional) – The output NDArray to hold the result.
Returns:

out – The output of this function.

Return type:

NDArray or list of NDArrays

mxnet.ndarray.contrib.dequantize(input=None, min_range=None, max_range=None, out_type=_Null, out=None, name=None, **kwargs)

Dequantize the input tensor into a float tensor. [min_range, max_range] are scalar floats that spcify the range for the output data.

Each value of the tensor will undergo the following:

out[i] = min_range + (in[i] * (max_range - min_range) / range(INPUT_TYPE))

here range(T) = numeric_limits::max() - numeric_limits::min()

Defined in src/operator/contrib/dequantize.cc:L41

Parameters:
  • input (NDArray) – A ndarray/symbol of type uint8
  • min_range (NDArray) – The minimum scalar value possibly produced for the input
  • max_range (NDArray) – The maximum scalar value possibly produced for the input
  • out_type ({'float32'}, required) – Output data type.
  • out (NDArray, optional) – The output NDArray to hold the result.
Returns:

out – The output of this function.

Return type:

NDArray or list of NDArrays

mxnet.ndarray.contrib.fft(data=None, compute_size=_Null, out=None, name=None, **kwargs)

Apply 1D FFT to input”

Note

fft is only available on GPU.

Currently accept 2 input data shapes: (N, d) or (N1, N2, N3, d), data can only be real numbers. The output data has shape: (N, 2*d) or (N1, N2, N3, 2*d). The format is: [real0, imag0, real1, imag1, ...].

Example:

data = np.random.normal(0,1,(3,4))
out = mx.contrib.ndarray.fft(data = mx.nd.array(data,ctx = mx.gpu(0)))

Defined in src/operator/contrib/fft.cc:L56

Parameters:
  • data (NDArray) – Input data to the FFTOp.
  • compute_size (int, optional, default='128') – Maximum size of sub-batch to be forwarded at one time
  • out (NDArray, optional) – The output NDArray to hold the result.
Returns:

out – The output of this function.

Return type:

NDArray or list of NDArrays

mxnet.ndarray.contrib.ifft(data=None, compute_size=_Null, out=None, name=None, **kwargs)

Apply 1D ifft to input”

Note

ifft is only available on GPU.

Currently accept 2 input data shapes: (N, d) or (N1, N2, N3, d). Data is in format: [real0, imag0, real1, imag1, ...]. Last dimension must be an even number. The output data has shape: (N, d/2) or (N1, N2, N3, d/2). It is only the real part of the result.

Example:

data = np.random.normal(0,1,(3,4))
out = mx.contrib.ndarray.ifft(data = mx.nd.array(data,ctx = mx.gpu(0)))

Defined in src/operator/contrib/ifft.cc:L58

Parameters:
  • data (NDArray) – Input data to the IFFTOp.
  • compute_size (int, optional, default='128') – Maximum size of sub-batch to be forwarded at one time
  • out (NDArray, optional) – The output NDArray to hold the result.
Returns:

out – The output of this function.

Return type:

NDArray or list of NDArrays

mxnet.ndarray.contrib.quantize(input=None, min_range=None, max_range=None, out_type=_Null, out=None, name=None, **kwargs)

Quantize a input tensor from float to out_type, with user-specified min_range and max_range.

[min_range, max_range] are scalar floats that spcify the range for the input data. Each value of the tensor will undergo the following:

out[i] = (in[i] - min_range) * range(OUTPUT_TYPE) / (max_range - min_range)

here range(T) = numeric_limits::max() - numeric_limits::min()

Defined in src/operator/contrib/quantize.cc:L41

Parameters:
  • input (NDArray) – A ndarray/symbol of type float32
  • min_range (NDArray) – The minimum scalar value possibly produced for the input
  • max_range (NDArray) – The maximum scalar value possibly produced for the input
  • out_type ({'uint8'},optional, default='uint8') – Output data type.
  • out (NDArray, optional) – The output NDArray to hold the result.
Returns:

out – The output of this function.

Return type:

NDArray or list of NDArrays