Contrib Symbol API¶
Overview¶
This document lists the contrib routines of the symbolic expression package:
mxnet.symbol.contrib 
Contrib Symbol API of MXNet. 
The Contrib Symbol
API, defined in the symbol.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 symbol.contrib
package.
Contrib¶
CTCLoss 
Connectionist Temporal Classification Loss. 
DeformableConvolution 
Compute 2D deformable convolution on 4D input. 
DeformablePSROIPooling 
Performs deformable positionsensitive regionofinterest 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 regionofinterest pooling on inputs. 
Proposal 
Generate region proposals via RPN 
count_sketch 
Apply CountSketch to input: map a ddimension data to kdimension 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 userspecified min_range and max_range. 
API Reference¶
Contrib Symbol API of MXNet.

mxnet.symbol.contrib.
CTCLoss
(data=None, label=None, data_lengths=None, label_lengths=None, use_data_lengths=_Null, use_label_lengths=_Null, blank_label=_Null, name=None, attr=None, out=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 ith channel in the last dimension corresponding to ith label for i between 0 and alphabet_size1 (i.e always 0indexed). Alphabet size should include one additional value reserved for blank label. When blank_label is
"first"
, the0
th channel is be reserved for activation of blank label, or otherwise if it is “last”,(alphabet_size1)
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_size1) 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 0th 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 (Symbol) – Input data to the ctc_loss op.
 label (Symbol) – Groundtruth labels for the loss.
 data_lengths (Symbol) – Lengths of data for each of the samples. Only required when use_data_lengths is true.
 label_lengths (Symbol) – 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, and1
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”, 0th label is reserved, and label values for tokens in the vocabulary are between
1
andalphabet_size1
, and the padding mask is1
. If “last”, last label valuealphabet_size1
is reserved for blank label instead, and label values for tokens in the vocabulary are between0
andalphabet_size2
, and the padding mask is0
.  name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:

mxnet.symbol.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, name=None, attr=None, out=None, **kwargs)¶ Compute 2D deformable convolution on 4D input.
The deformable convolution operation is described in https://arxiv.org/abs/1703.06211
For 2D 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*pd*(k1)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 thebias
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 inputdata
evenly into g parts along the channel axis, and also evenly splitweight
along the first dimension. Next compute the convolution on the ith part of the data with the ith 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 inputoffset
evenly into dg parts along the channel axis, and also evenly splitout
evenly into dg parts along the channel axis. Next compute the deformable convolution, apply the ith part of the offset part on the ith out.Both
weight
andbias
are learnable parameters.Defined in src/operator/contrib/deformable_convolution.cc:L100
Parameters:  data (Symbol) – Input data to the DeformableConvolutionOp.
 offset (Symbol) – Input offset to the DeformableConvolutionOp.
 weight (Symbol) – Weight matrix.
 bias (Symbol) – 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)
 dilate (Shape(tuple), optional, default=[]) – convolution dilate: (h, w) or (d, h, w)
 pad (Shape(tuple), optional, default=[]) – pad for convolution: (h, w) or (d, h, w)
 num_filter (int (nonnegative), required) – convolution filter(channel) number
 num_group (int (nonnegative), optional, default=1) – Number of group partitions.
 num_deformable_group (int (nonnegative), optional, default=1) – Number of deformable group partitions.
 workspace (long (nonnegative), 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.
 name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:

mxnet.symbol.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, name=None, attr=None, out=None, **kwargs)¶ Performs deformable positionsensitive regionofinterest 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.
 name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:

mxnet.symbol.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, name=None, attr=None, out=None, **kwargs)¶ Convert multibox detection predictions.
Parameters:  cls_prob (Symbol) – Class probabilities.
 loc_pred (Symbol) – Location regression predictions.
 anchor (Symbol) – Multibox prior anchor boxes
 clip (boolean, optional, default=1) – Clip outofboundary 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) – Nonmaximum 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.
 name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:

mxnet.symbol.contrib.
MultiBoxPrior
(data=None, sizes=_Null, ratios=_Null, clip=_Null, steps=_Null, offsets=_Null, name=None, attr=None, out=None, **kwargs)¶ Generate prior(anchor) boxes from data, sizes and ratios.
Parameters:  data (Symbol) – 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 outofboundary 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  name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:

mxnet.symbol.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, name=None, attr=None, out=None, **kwargs)¶ Compute Multibox training targets
Parameters:  anchor (Symbol) – Generated anchor boxes.
 label (Symbol) – Object detection labels.
 cls_pred (Symbol) – Class predictions.
 overlap_threshold (float, optional, default=0.5) – AnchorGT 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.  name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:

mxnet.symbol.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, name=None, attr=None, out=None, **kwargs)¶ Generate region proposals via RPN
Parameters:  cls_score (Symbol) – Score of how likely proposal is object.
 bbox_pred (Symbol) – BBox Predicted deltas from anchors for proposals
 im_info (Symbol) – 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 nonmaximumsuppresion(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
 name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:

mxnet.symbol.contrib.
PSROIPooling
(data=None, rois=None, spatial_scale=_Null, output_dim=_Null, pooled_size=_Null, group_size=_Null, name=None, attr=None, out=None, **kwargs)¶ Performs regionofinterest 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
 name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:

mxnet.symbol.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, name=None, attr=None, out=None, **kwargs)¶ Generate region proposals via RPN
Parameters:  cls_score (Symbol) – Score of how likely proposal is object.
 bbox_pred (Symbol) – BBox Predicted deltas from anchors for proposals
 im_info (Symbol) – 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 nonmaximumsuppresion(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
 name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:

mxnet.symbol.contrib.
SparseEmbedding
(data=None, weight=None, input_dim=_Null, output_dim=_Null, dtype=_Null, name=None, attr=None, out=None, **kwargs)¶ Maps integer indices to vector representations (embeddings).
This operator maps words to realvalued vectors in a highdimensional 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 ngrams(2gram). 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:L293
Parameters:  data (Symbol) – The input array to the embedding operator.
 weight (Symbol) – 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.
 name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:

mxnet.symbol.contrib.
count_sketch
(data=None, h=None, s=None, out_dim=_Null, processing_batch_size=_Null, name=None, attr=None, out=None, **kwargs)¶ Apply CountSketch to input: map a ddimension data to kdimension 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 k1. 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 (Symbol) – Input data to the CountSketchOp.
 h (Symbol) – The index vector
 s (Symbol) – 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.
 name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:

mxnet.symbol.contrib.
ctc_loss
(data=None, label=None, data_lengths=None, label_lengths=None, use_data_lengths=_Null, use_label_lengths=_Null, blank_label=_Null, name=None, attr=None, out=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 ith channel in the last dimension corresponding to ith label for i between 0 and alphabet_size1 (i.e always 0indexed). Alphabet size should include one additional value reserved for blank label. When blank_label is
"first"
, the0
th channel is be reserved for activation of blank label, or otherwise if it is “last”,(alphabet_size1)
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_size1) 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 0th 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 (Symbol) – Input data to the ctc_loss op.
 label (Symbol) – Groundtruth labels for the loss.
 data_lengths (Symbol) – Lengths of data for each of the samples. Only required when use_data_lengths is true.
 label_lengths (Symbol) – 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, and1
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”, 0th label is reserved, and label values for tokens in the vocabulary are between
1
andalphabet_size1
, and the padding mask is1
. If “last”, last label valuealphabet_size1
is reserved for blank label instead, and label values for tokens in the vocabulary are between0
andalphabet_size2
, and the padding mask is0
.  name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:

mxnet.symbol.contrib.
dequantize
(input=None, min_range=None, max_range=None, out_type=_Null, name=None, attr=None, out=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 (Symbol) – A ndarray/symbol of type uint8
 min_range (Symbol) – The minimum scalar value possibly produced for the input
 max_range (Symbol) – The maximum scalar value possibly produced for the input
 out_type ({'float32'}, required) – Output data type.
 name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:

mxnet.symbol.contrib.
fft
(data=None, compute_size=_Null, name=None, attr=None, out=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 (Symbol) – Input data to the FFTOp.
 compute_size (int, optional, default='128') – Maximum size of subbatch to be forwarded at one time
 name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:

mxnet.symbol.contrib.
ifft
(data=None, compute_size=_Null, name=None, attr=None, out=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 (Symbol) – Input data to the IFFTOp.
 compute_size (int, optional, default='128') – Maximum size of subbatch to be forwarded at one time
 name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:

mxnet.symbol.contrib.
quantize
(input=None, min_range=None, max_range=None, out_type=_Null, name=None, attr=None, out=None, **kwargs)¶ Quantize a input tensor from float to out_type, with userspecified 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 (Symbol) – A ndarray/symbol of type float32
 min_range (Symbol) – The minimum scalar value possibly produced for the input
 max_range (Symbol) – The maximum scalar value possibly produced for the input
 out_type ({'uint8'},optional, default='uint8') – Output data type.
 name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type: