Contrib NDArray API¶
Overview¶
This document lists the contrib routines of the ndimensional 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¶
AdaptiveAvgPooling2D 
Applies a 2D adaptive average pooling over a 4D input with the shape of (NCHW). 
BilinearResize2D 
Perform 2D resizing (upsampling or downsampling) for 4D input using bilinear interpolation. 
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 
ROIAlign 
This operator takes a 4D feature map as an input array and region proposals as rois, then align the feature map over subregions of input and produces a fixedsized output array. 
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. 
foreach 
Run a for loop with userdefined computation over NDArrays on dimension 0. 
while_loop 
Run a while loop with userdefined computation and loop condition. 
cond 
Run an ifthenelse using userdefined condition and computation 
isinf 
Performs an elementwise check to determine if the NDArray contains an infinite element or not. 
isfinite 
Performs an elementwise check to determine if the NDArray contains an infinite element or not. 
isnan 
Performs an elementwise check to determine if the NDArray contains a NaN element or not. 
index_copy 
Copies the elements of a new_tensor into the old_tensor. 
getnnz 
Number of stored values for a sparse tensor, including explicit zeros. 
edge_id 
This operator implements the edge_id function for a graph stored in a CSR matrix (the value of the CSR stores the edge Id of the graph). 
dgl_csr_neighbor_uniform_sample 
This operator samples subgraphs from a csr graph via an uniform probability. 
dgl_csr_neighbor_non_uniform_sample 
This operator samples subgraph from a csr graph via an nonuniform probability. 
dgl_subgraph 
This operator constructs an induced subgraph for a given set of vertices from a graph. 
dgl_adjacency 
This operator converts a CSR matrix whose values are edge Ids to an adjacency matrix whose values are ones. 
dgl_graph_compact 
This operator compacts a CSR matrix generated by dgl_csr_neighbor_uniform_sample and dgl_csr_neighbor_non_uniform_sample. 
API Reference¶
Contrib NDArray API of MXNet.

mxnet.ndarray.contrib.
rand_zipfian
(true_classes, num_sampled, range_max, ctx=None)[source]¶ Draw random samples from an approximately loguniform or Zipfian distribution.
This operation randomly samples num_sampled candidates the range of integers [0, range_max). The elements of sampled_candidates are drawn with replacement from the base distribution.
The base distribution for this operator is an approximately loguniform or Zipfian distribution:
P(class) = (log(class + 2)  log(class + 1)) / log(range_max + 1)
This sampler is useful when the true classes approximately follow such a distribution. For example, if the classes represent words in a lexicon sorted in decreasing order of frequency. If your classes are not ordered by decreasing frequency, do not use this op.
Additionaly, it also returns the number of times each of the true classes and the sampled classes is expected to occur.
Parameters:  true_classes (NDArray) – A 1D NDArray of the target classes.
 num_sampled (int) – The number of classes to randomly sample.
 range_max (int) – The number of possible classes.
 ctx (Context) – Device context of output. Default is current context.
Returns:  samples (NDArray) – The sampled candidate classes in 1D int64 dtype.
 expected_count_true (NDArray) – The expected count for true classes in 1D float64 dtype.
 expected_count_sample (NDArray) – The expected count for sampled candidates in 1D float64 dtype.
Examples
>>> true_cls = mx.nd.array([3]) >>> samples, exp_count_true, exp_count_sample = mx.nd.contrib.rand_zipfian(true_cls, 4, 5) >>> samples [1 3 3 3]
>>> exp_count_true [ 0.12453879] >>> exp_count_sample [ 0.22629439 0.12453879 0.12453879 0.12453879]

mxnet.ndarray.contrib.
foreach
(body, data, init_states)[source]¶ Run a for loop with userdefined computation over NDArrays on dimension 0.
This operator simulates a for loop and body has the computation for an iteration of the for loop. It runs the computation in body on each slice from the input NDArrays.
body takes two arguments as input and outputs a tuple of two elements, as illustrated below:
out, states = body(data1, states)
data1 can be either an NDArray or a list of NDArrays. If data is an NDArray, data1 is an NDArray. Otherwise, data1 is a list of NDArrays and has the same size as data. states is a list of NDArrays and have the same size as init_states. Similarly, out can be either an NDArray or a list of NDArrays, which are concatenated as the first output of foreach; states from the last execution of body are the second output of foreach.
The computation done by this operator is equivalent to the pseudo code below when the input data is NDArray:
states = init_states outs = [] for i in data.shape[0]: s = data[i] out, states = body(s, states) outs.append(out) outs = stack(*outs)
Parameters:  body (a Python function.) – Define computation in an iteration.
 data (an NDArray or a list of NDArrays.) – The input data.
 init_states (an NDArray or nested lists of NDArrays.) – The initial values of the loop states.
 name (string.) – The name of the operator.
Returns:  outputs (an NDArray or nested lists of NDArrays.) – The output data concatenated from the output of all iterations.
 states (an NDArray or nested lists of NDArrays.) – The loop states in the last iteration.
Examples
>>> step = lambda data, states: (data + states[0], [states[0] * 2]) >>> data = mx.nd.random.uniform(shape=(2, 10)) >>> states = [mx.nd.random.uniform(shape=(10))] >>> outs, states = mx.nd.contrib.foreach(step, data, states)

mxnet.ndarray.contrib.
while_loop
(cond, func, loop_vars, max_iterations=None)[source]¶ Run a while loop with userdefined computation and loop condition.
This operator simulates a while loop which iterately does customized computation as long as the condition is satisfied.
loop_vars is a list of NDArrays on which the computation uses.
cond is a userdefined function, used as the loop condition. It consumes loop_vars, and produces a scalar MXNet NDArray, indicating the termination of the loop. The loop ends when cond returns false (zero). The cond is variadic, and its signature should be cond(*loop_vars) => NDArray.
func is a userdefined function, used as the loop body. It also consumes loop_vars, and produces step_output and new_loop_vars at each step. In each step, step_output should contain the same number elements. Through all steps, the ith element of step_output should have the same shape and dtype. Also, new_loop_vars should contain the same number of elements as loop_vars, and the corresponding element should have the same shape and dtype. The func is variadic, and its signature should be func(*loop_vars) => (NDArray or nested List[NDArray] step_output, NDArray or nested List[NDArray] new_loop_vars).
max_iterations is a scalar that defines the maximum number of iterations allowed.
This function returns two lists. The first list has the length of step_output, in which the ith element are all ith elements of step_output from all steps, stacked along axis 0. The second list has the length of loop_vars, which represents final states of loop variables.
Warning
For now, the axis 0 of all NDArrays in the first list are max_iterations, due to lack of dynamic shape inference.
Warning
When cond is never satisfied, we assume step_output is empty, because it cannot be inferred. This is different from the symbolic version.
Parameters:  cond (a Python function.) – The loop condition.
 func (a Python function.) – The loop body.
 loop_vars (an NDArray or nested lists of NDArrays.) – The initial values of the loop variables.
 max_iterations (a python int.) – Maximum number of iterations.
Returns:  outputs (an NDArray or nested lists of NDArrays) – stacked output from each step
 states (an NDArray or nested lists of NDArrays) – final state
Examples
>>> cond = lambda i, s: i <= 5 >>> func = lambda i, s: ([i + s], [i + 1, s + i]) >>> loop_vars = (mx.nd.array([0], dtype="int64"), mx.nd.array([1], dtype="int64")) >>> outputs, states = mx.nd.contrib.while_loop(cond, func, loop_vars, max_iterations=10) >>> outputs [ [[ 1] [ 2] [ 4] [ 7] [11] [16] [...] # undefined value [...] [...] [...]]
] >>> states [ [6], [16]]

mxnet.ndarray.contrib.
cond
(pred, then_func, else_func)[source]¶ Run an ifthenelse using userdefined condition and computation
This operator simulates a iflike branch which chooses to do one of the two customized computations according to the specified condition.
pred is a scalar MXNet NDArray, indicating which branch of computation should be used.
then_func is a userdefined function, used as computation of the then branch. It produces outputs, which is a list of NDArrays. The signature of then_func should be then_func() => NDArray or nested List[NDArray].
else_func is a userdefined function, used as computation of the else branch. It produces outputs, which is a list of NDArrays. The signature of else_func should be else_func() => NDArray or nested List[NDArray].
The outputs produces by then_func and else_func should have the same number of elements, all of which should be in the same shape, of the same dtype and stype.
This function returns a list of symbols, representing the computation result.
Parameters:  pred (a MXNet NDArray representing a scalar.) – The branch condition.
 then_func (a Python function.) – The computation to be executed if pred is true.
 else_func (a Python function.) – The computation to be executed if pred is false.
Returns: outputs
Return type: an NDArray or nested lists of NDArrays, representing the result of computation.
Examples
>>> a, b = mx.nd.array([1]), mx.nd.array([2]) >>> pred = a * b < 5 >>> then_func = lambda: (a + 5) * (b + 5) >>> else_func = lambda: (a  5) * (b  5) >>> outputs = mx.nd.contrib.cond(pred, then_func, else_func) >>> outputs[0] [42.]

mxnet.ndarray.contrib.
isinf
(data)[source]¶ Performs an elementwise check to determine if the NDArray contains an infinite element or not.
Parameters: input (NDArray) – An ND NDArray. Returns: output – The output NDarray, with same shape as input, where 1 indicates the array element is equal to positive or negative infinity and 0 otherwise. Return type: NDArray Examples
>>> data = mx.nd.array([np.inf, np.inf, np.NINF, 1]) >>> output = mx.nd.contrib.isinf(data) >>> output [1. 1. 1. 0.]

mxnet.ndarray.contrib.
isfinite
(data)[source]¶ Performs an elementwise check to determine if the NDArray contains an infinite element or not.
Parameters: input (NDArray) – An ND NDArray. Returns: output – The output NDarray, with same shape as input, where 1 indicates the array element is finite i.e. not equal to positive or negative infinity and 0 in places where it is positive or negative infinity. Return type: NDArray Examples
>>> data = mx.nd.array([np.inf, np.inf, np.NINF, 1]) >>> output = mx.nd.contrib.isfinite(data) >>> output [0. 0. 0. 1.]

mxnet.ndarray.contrib.
isnan
(data)[source]¶ Performs an elementwise check to determine if the NDArray contains a NaN element or not.
Parameters: input (NDArray) – An ND NDArray. Returns: output – The output NDarray, with same shape as input, where 1 indicates the array element is NaN i.e. Not a Number and 0 otherwise. Return type: NDArray Examples
>>> data = mx.nd.array([np.nan, 1]) >>> output = mx.nd.contrib.isnan(data) >>> output [1. 0.]

mxnet.ndarray.contrib.
AdaptiveAvgPooling2D
(data=None, output_size=_Null, out=None, name=None, **kwargs)¶ Applies a 2D adaptive average pooling over a 4D input with the shape of (NCHW). The pooling kernel and stride sizes are automatically chosen for desired output sizes.
 If a single integer is provided for output_size, the output size is (N x C x output_size x output_size) for any input (NCHW).
 If a tuple of integers (height, width) are provided for output_size, the output size is (N x C x height x width) for any input (NCHW).
Defined in src/operator/contrib/adaptive_avg_pooling.cc:L214
Parameters: Returns: out – The output of this function.
Return type: NDArray or list of NDArrays

mxnet.ndarray.contrib.
BilinearResize2D
(data=None, height=_Null, width=_Null, out=None, name=None, **kwargs)¶ Perform 2D resizing (upsampling or downsampling) for 4D input using bilinear interpolation.
Expected input is a 4 dimensional NDArray (NCHW) and the output with the shape of (N x C x height x width). The key idea of bilinear interpolation is to perform linear interpolation first in one direction, and then again in the other direction. See the wikipedia of Bilinear interpolation for more details.
Defined in src/operator/contrib/bilinear_resize.cc:L175
Parameters: Returns: out – The output of this function.
Return type: NDArray or list of NDArrays

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.
Note
The existing alias
contrib_CTCLoss
is deprecated.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/nn/ctc_loss.cc:L100
Parameters:  data (NDArray) – Input ndarray
 label (NDArray) – Groundtruth 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, 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
.  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 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 (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 (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.
 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 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.
 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 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.
 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 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  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) – 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.  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_prob=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_prob (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 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
 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 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
 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_prob=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_prob (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 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
 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.
ROIAlign
(data=None, rois=None, pooled_size=_Null, spatial_scale=_Null, sample_ratio=_Null, position_sensitive=_Null, out=None, name=None, **kwargs)¶ This operator takes a 4D feature map as an input array and region proposals as rois, then align the feature map over subregions of input and produces a fixedsized output array. This operator is typically used in Faster RCNN & Mask RCNN networks.
Different from ROI pooling, ROI Align removes the harsh quantization, properly aligning the extracted features with the input. RoIAlign computes the value of each sampling point by bilinear interpolation from the nearby grid points on the feature map. No quantization is performed on any coordinates involved in the RoI, its bins, or the sampling points. Bilinear interpolation is used to compute the exact values of the input features at four regularly sampled locations in each RoI bin. Then the feature map can be aggregated by avgpooling.
References
He, Kaiming, et al. “Mask RCNN.” ICCV, 2017
Defined in src/operator/contrib/roi_align.cc:L538
Parameters:  data (NDArray) – Input data to the pooling operator, a 4D Feature maps
 rois (NDArray) – Bounding box coordinates, a 2D array
 pooled_size (Shape(tuple), required) – ROI Align output roi feature map height and width: (h, w)
 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
 sample_ratio (int, optional, default='1') – Optional sampling ratio of ROI align, using adaptive size by default.
 position_sensitive (boolean, optional, default=0) – Whether to perform positionsensitive RoI pooling. PSRoIPooling is first proposaled by RFCN and it can reduce the input channels by ph*pw times, where (ph, pw) is the pooled_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.
SparseEmbedding
(data=None, weight=None, input_dim=_Null, output_dim=_Null, dtype=_Null, sparse_grad=_Null, out=None, name=None, **kwargs)¶ Maps integer indices to vector representations (embeddings).
note::
contrib.SparseEmbedding
is deprecated, useEmbedding
instead.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 the gradient will be row_sparse.
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. When deterministic is set to True, the accumulation of gradients follows a deterministic order if a feature appears multiple times in the input. However, the accumulation is usually slower when the order is enforced on GPU. When the operator is used on the GPU, the recommended value for deterministic is True.
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:L595
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', 'int64', 'int8', 'uint8'},optional, default='float32') – Data type of weight.
 sparse_grad (boolean, optional, default=0) – Compute row sparse gradient in the backward calculation. If set to True, the grad’s storage type is row_sparse.
 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.
SyncBatchNorm
(data=None, gamma=None, beta=None, moving_mean=None, moving_var=None, eps=_Null, momentum=_Null, fix_gamma=_Null, use_global_stats=_Null, output_mean_var=_Null, ndev=_Null, key=_Null, out=None, name=None, **kwargs)¶ Batch normalization.
Normalizes a data batch by mean and variance, and applies a scale
gamma
as well as offsetbeta
. Standard BN [1] implementation only normalize the data within each device. SyncBN normalizes the input within the whole minibatch. We follow the synconece implmentation described in the paper [2].Assume the input has more than one dimension and we normalize along axis 1. We first compute the mean and variance along this axis:
\[\begin{split}data\_mean[i] = mean(data[:,i,:,...]) \\ data\_var[i] = var(data[:,i,:,...])\end{split}\]Then compute the normalized output, which has the same shape as input, as following:
\[out[:,i,:,...] = \frac{data[:,i,:,...]  data\_mean[i]}{\sqrt{data\_var[i]+\epsilon}} * gamma[i] + beta[i]\]Both mean and var returns a scalar by treating the input as a vector.
Assume the input has size k on axis 1, then both
gamma
andbeta
have shape (k,). Ifoutput_mean_var
is set to be true, then outputs bothdata_mean
anddata_var
as well, which are needed for the backward pass.Besides the inputs and the outputs, this operator accepts two auxiliary states,
moving_mean
andmoving_var
, which are klength vectors. They are global statistics for the whole dataset, which are updated by:moving_mean = moving_mean * momentum + data_mean * (1  momentum) moving_var = moving_var * momentum + data_var * (1  momentum)
If
use_global_stats
is set to be true, thenmoving_mean
andmoving_var
are used instead ofdata_mean
anddata_var
to compute the output. It is often used during inference.Both
gamma
andbeta
are learnable parameters. But iffix_gamma
is true, then setgamma
to 1 and its gradient to 0. Reference:
[1] Ioffe, Sergey, and Christian Szegedy. “Batch normalization: Accelerating deep network training by reducing internal covariate shift.” ICML 2015 [2] Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, and Amit Agrawal. “Context Encoding for Semantic Segmentation.” CVPR 2018
Defined in src/operator/contrib/sync_batch_norm.cc:L97
Parameters:  data (NDArray) – Input data to batch normalization
 gamma (NDArray) – gamma array
 beta (NDArray) – beta array
 moving_mean (NDArray) – running mean of input
 moving_var (NDArray) – running variance of input
 eps (float, optional, default=0.001) – Epsilon to prevent div 0
 momentum (float, optional, default=0.9) – Momentum for moving average
 fix_gamma (boolean, optional, default=1) – Fix gamma while training
 use_global_stats (boolean, optional, default=0) – Whether use global moving statistics instead of local batchnorm. This will force change batchnorm into a scale shift operator.
 output_mean_var (boolean, optional, default=0) – Output All,normal mean and var
 ndev (int, optional, default='1') – The count of GPU devices
 key (string, optional, default='') – Hash key for synchronization, please set the same hash key for same layer, Block.prefix is typically used as in
gluon.nn.contrib.SyncBatchNorm
.  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.
adamw_update
(weight=None, grad=None, mean=None, var=None, lr=_Null, beta1=_Null, beta2=_Null, epsilon=_Null, wd=_Null, eta=_Null, rescale_grad=_Null, clip_gradient=_Null, out=None, name=None, **kwargs)¶ Update function for AdamW optimizer. AdamW is seen as a modification of Adam by decoupling the weight decay from the optimization steps taken w.r.t. the loss function.
Adam update consists of the following steps, where g represents gradient and m, v are 1st and 2nd order moment estimates (mean and variance).
\[\begin{split}g_t = \nabla J(W_{t1})\\ m_t = \beta_1 m_{t1} + (1  \beta_1) g_t\\ v_t = \beta_2 v_{t1} + (1  \beta_2) g_t^2\\ W_t = W_{t1}  \eta_t (\alpha \frac{ m_t }{ \sqrt{ v_t } + \epsilon } + wd W_{t1})\end{split}\]It updates the weights using:
m = beta1*m + (1beta1)*grad v = beta2*v + (1beta2)*(grad**2) w = eta * (learning_rate * m / (sqrt(v) + epsilon) + w * wd)
Defined in src/operator/contrib/adamw.cc:L53
Parameters:  weight (NDArray) – Weight
 grad (NDArray) – Gradient
 mean (NDArray) – Moving mean
 var (NDArray) – Moving variance
 lr (float, required) – Learning rate
 beta1 (float, optional, default=0.9) – The decay rate for the 1st moment estimates.
 beta2 (float, optional, default=0.999) – The decay rate for the 2nd moment estimates.
 epsilon (float, optional, default=1e08) – A small constant for numerical stability.
 wd (float, optional, default=0) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.
 eta (float, required) – Learning rate schedule multiplier
 rescale_grad (float, optional, default=1) – Rescale gradient to grad = rescale_grad*grad.
 clip_gradient (float, optional, default=1) – Clip gradient to the range of [clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), clip_gradient).
 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.
backward_index_copy
(out=None, name=None, **kwargs)¶ Parameters: 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.
backward_quadratic
(out=None, name=None, **kwargs)¶ Parameters: 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.
bipartite_matching
(data=None, is_ascend=_Null, threshold=_Null, topk=_Null, out=None, name=None, **kwargs)¶  Compute bipartite matching.
The matching is performed on score matrix with shape [B, N, M]  B: batch_size  N: number of rows to match  M: number of columns as reference to be matched against.
Returns: x : matched column indices. 1 indicating nonmatched elements in rows. y : matched row indices.
Note:
Zero gradients are backpropagated in this op for now.
Example:
s = [[0.5, 0.6], [0.1, 0.2], [0.3, 0.4]] x, y = bipartite_matching(x, threshold=1e12, is_ascend=False) x = [1, 1, 0] y = [2, 0]
Defined in src/operator/contrib/bounding_box.cc:L176
Parameters:  data (NDArray) – The input
 is_ascend (boolean, optional, default=0) – Use ascend order for scores instead of descending. Please set threshold accordingly.
 threshold (float, required) – Ignore matching when score < thresh, if is_ascend=false, or ignore score > thresh, if is_ascend=true.
 topk (int, optional, default='1') – Limit the number of matches to topk, set 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.
boolean_mask
(data=None, index=None, axis=_Null, out=None, name=None, **kwargs)¶ Experimental CPUonly support for boolean masking. Given an nd NDArray data, and a 1d NDArray index, the operator produces an unpredeterminable shaped nd NDArray out, which stands for the rows in x where the corresonding element in index is nonzero.
>>> data = mx.nd.array([[1, 2, 3],[4, 5, 6],[7, 8, 9]]) >>> index = mx.nd.array([0, 1, 0]) >>> out = mx.nd.contrib.boolean_mask(data, index) >>> out
[[4. 5. 6.]]
Defined in src/operator/contrib/boolean_mask.cc:L93
Parameters: Returns: out – The output of this function.
Return type: NDArray or list of NDArrays

mxnet.ndarray.contrib.
box_iou
(lhs=None, rhs=None, format=_Null, out=None, name=None, **kwargs)¶  Bounding box overlap of two arrays.
The overlap is defined as IntersectionoverUnion, aka, IOU.  lhs: (a_1, a_2, ..., a_n, 4) array  rhs: (b_1, b_2, ..., b_n, 4) array  output: (a_1, a_2, ..., a_n, b_1, b_2, ..., b_n) array
Note:
Zero gradients are backpropagated in this op for now.
Example:
x = [[0.5, 0.5, 1.0, 1.0], [0.0, 0.0, 0.5, 0.5]] y = [[0.25, 0.25, 0.75, 0.75]] box_iou(x, y, format='corner') = [[0.1428], [0.1428]]
Defined in src/operator/contrib/bounding_box.cc:L130
Parameters:  lhs (NDArray) – The first input
 rhs (NDArray) – The second input
 format ({'center', 'corner'},optional, default='corner') – The box encoding type. “corner” means boxes are encoded as [xmin, ymin, xmax, ymax], “center” means boxes are encodes as [x, y, width, height].
 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.
box_nms
(data=None, overlap_thresh=_Null, valid_thresh=_Null, topk=_Null, coord_start=_Null, score_index=_Null, id_index=_Null, force_suppress=_Null, in_format=_Null, out_format=_Null, out=None, name=None, **kwargs)¶ Apply nonmaximum suppression to input.
The output will be sorted in descending order according to score. Boxes with overlaps larger than overlap_thresh and smaller scores will be removed and filled with 1, the corresponding position will be recorded for backward propogation.
During backpropagation, the gradient will be copied to the original position according to the input index. For positions that have been suppressed, the in_grad will be assigned 0. In summary, gradients are sticked to its boxes, will either be moved or discarded according to its original index in input.
Input requirements:
1. Input tensor have at least 2 dimensions, (n, k), any higher dims will be regarded as batch, e.g. (a, b, c, d, n, k) == (a*b*c*d, n, k) 2. n is the number of boxes in each batch 3. k is the width of each box item.
By default, a box is [id, score, xmin, ymin, xmax, ymax, ...], additional elements are allowed.
id_index: optional, use 1 to ignore, useful if force_suppress=False, which means we will skip highly overlapped boxes if one is apple while the other is car.
coord_start: required, default=2, the starting index of the 4 coordinates. Two formats are supported:
 corner: [xmin, ymin, xmax, ymax]
 center: [x, y, width, height]
score_index: required, default=1, box score/confidence. When two boxes overlap IOU > overlap_thresh, the one with smaller score will be suppressed.
in_format and out_format: default=’corner’, specify in/out box formats.
Examples:
x = [[0, 0.5, 0.1, 0.1, 0.2, 0.2], [1, 0.4, 0.1, 0.1, 0.2, 0.2], [0, 0.3, 0.1, 0.1, 0.14, 0.14], [2, 0.6, 0.5, 0.5, 0.7, 0.8]] box_nms(x, overlap_thresh=0.1, coord_start=2, score_index=1, id_index=0, force_suppress=True, in_format='corner', out_typ='corner') = [[2, 0.6, 0.5, 0.5, 0.7, 0.8], [0, 0.5, 0.1, 0.1, 0.2, 0.2], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]] out_grad = [[0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.2, 0.2, 0.2, 0.2, 0.2, 0.2], [0.3, 0.3, 0.3, 0.3, 0.3, 0.3], [0.4, 0.4, 0.4, 0.4, 0.4, 0.4]] # exe.backward in_grad = [[0.2, 0.2, 0.2, 0.2, 0.2, 0.2], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]]
Defined in src/operator/contrib/bounding_box.cc:L89
Parameters:  data (NDArray) – The input
 overlap_thresh (float, optional, default=0.5) – Overlapping(IoU) threshold to suppress object with smaller score.
 valid_thresh (float, optional, default=0) – Filter input boxes to those whose scores greater than valid_thresh.
 topk (int, optional, default='1') – Apply nms to topk boxes with descending scores, 1 to no restriction.
 coord_start (int, optional, default='2') – Start index of the consecutive 4 coordinates.
 score_index (int, optional, default='1') – Index of the scores/confidence of boxes.
 id_index (int, optional, default='1') – Optional, index of the class categories, 1 to disable.
 force_suppress (boolean, optional, default=0) – Optional, if set false and id_index is provided, nms will only apply to boxes belongs to the same category
 in_format ({'center', 'corner'},optional, default='corner') – The input box encoding type. “corner” means boxes are encoded as [xmin, ymin, xmax, ymax], “center” means boxes are encodes as [x, y, width, height].
 out_format ({'center', 'corner'},optional, default='corner') – The output box encoding type. “corner” means boxes are encoded as [xmin, ymin, xmax, ymax], “center” means boxes are encodes as [x, y, width, height].
 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.
box_non_maximum_suppression
(data=None, overlap_thresh=_Null, valid_thresh=_Null, topk=_Null, coord_start=_Null, score_index=_Null, id_index=_Null, force_suppress=_Null, in_format=_Null, out_format=_Null, out=None, name=None, **kwargs)¶ Apply nonmaximum suppression to input.
The output will be sorted in descending order according to score. Boxes with overlaps larger than overlap_thresh and smaller scores will be removed and filled with 1, the corresponding position will be recorded for backward propogation.
During backpropagation, the gradient will be copied to the original position according to the input index. For positions that have been suppressed, the in_grad will be assigned 0. In summary, gradients are sticked to its boxes, will either be moved or discarded according to its original index in input.
Input requirements:
1. Input tensor have at least 2 dimensions, (n, k), any higher dims will be regarded as batch, e.g. (a, b, c, d, n, k) == (a*b*c*d, n, k) 2. n is the number of boxes in each batch 3. k is the width of each box item.
By default, a box is [id, score, xmin, ymin, xmax, ymax, ...], additional elements are allowed.
id_index: optional, use 1 to ignore, useful if force_suppress=False, which means we will skip highly overlapped boxes if one is apple while the other is car.
coord_start: required, default=2, the starting index of the 4 coordinates. Two formats are supported:
 corner: [xmin, ymin, xmax, ymax]
 center: [x, y, width, height]
score_index: required, default=1, box score/confidence. When two boxes overlap IOU > overlap_thresh, the one with smaller score will be suppressed.
in_format and out_format: default=’corner’, specify in/out box formats.
Examples:
x = [[0, 0.5, 0.1, 0.1, 0.2, 0.2], [1, 0.4, 0.1, 0.1, 0.2, 0.2], [0, 0.3, 0.1, 0.1, 0.14, 0.14], [2, 0.6, 0.5, 0.5, 0.7, 0.8]] box_nms(x, overlap_thresh=0.1, coord_start=2, score_index=1, id_index=0, force_suppress=True, in_format='corner', out_typ='corner') = [[2, 0.6, 0.5, 0.5, 0.7, 0.8], [0, 0.5, 0.1, 0.1, 0.2, 0.2], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]] out_grad = [[0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.2, 0.2, 0.2, 0.2, 0.2, 0.2], [0.3, 0.3, 0.3, 0.3, 0.3, 0.3], [0.4, 0.4, 0.4, 0.4, 0.4, 0.4]] # exe.backward in_grad = [[0.2, 0.2, 0.2, 0.2, 0.2, 0.2], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]]
Defined in src/operator/contrib/bounding_box.cc:L89
Parameters:  data (NDArray) – The input
 overlap_thresh (float, optional, default=0.5) – Overlapping(IoU) threshold to suppress object with smaller score.
 valid_thresh (float, optional, default=0) – Filter input boxes to those whose scores greater than valid_thresh.
 topk (int, optional, default='1') – Apply nms to topk boxes with descending scores, 1 to no restriction.
 coord_start (int, optional, default='2') – Start index of the consecutive 4 coordinates.
 score_index (int, optional, default='1') – Index of the scores/confidence of boxes.
 id_index (int, optional, default='1') – Optional, index of the class categories, 1 to disable.
 force_suppress (boolean, optional, default=0) – Optional, if set false and id_index is provided, nms will only apply to boxes belongs to the same category
 in_format ({'center', 'corner'},optional, default='corner') – The input box encoding type. “corner” means boxes are encoded as [xmin, ymin, xmax, ymax], “center” means boxes are encodes as [x, y, width, height].
 out_format ({'center', 'corner'},optional, default='corner') – The output box encoding type. “corner” means boxes are encoded as [xmin, ymin, xmax, ymax], “center” means boxes are encodes as [x, y, width, height].
 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 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 (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.
Note
The existing alias
contrib_CTCLoss
is deprecated.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/nn/ctc_loss.cc:L100
Parameters:  data (NDArray) – Input ndarray
 label (NDArray) – Groundtruth 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, 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
.  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
(data=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 and max_range are scalar floats that specify the range for the output data.
When input data type is uint8, the output is calculated using the following equation:
out[i] = in[i] * (max_range  min_range) / 255.0,
When input data type is int8, the output is calculate using the following equation by keep zero centered for the quantized value:
out[i] = in[i] * MaxAbs(min_range, max_range) / 127.0,
Note
This operator only supports forward propogation. DO NOT use it in training.
Defined in src/operator/quantization/dequantize.cc:L67
Parameters:  data (NDArray) – A ndarray/symbol of type uint8
 min_range (NDArray) – The minimum scalar value possibly produced for the input in float32
 max_range (NDArray) – The maximum scalar value possibly produced for the input in float32
 out_type ({'float32'},optional, default='float32') – 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.
dgl_adjacency
(data=None, out=None, name=None, **kwargs)¶ This operator converts a CSR matrix whose values are edge Ids to an adjacency matrix whose values are ones. The output CSR matrix always has the data value of float32.
Example
x = [[ 1, 0, 0 ], [ 0, 2, 0 ], [ 0, 0, 3 ]] dgl_adjacency(x) = [[ 1, 0, 0 ], [ 0, 1, 0 ], [ 0, 0, 1 ]]
Defined in src/operator/contrib/dgl_graph.cc:L1407
Parameters: Returns: out – The output of this function.
Return type: NDArray or list of NDArrays

mxnet.ndarray.contrib.
dgl_csr_neighbor_non_uniform_sample
(*seed_arrays, **kwargs)¶ This operator samples subgraph from a csr graph via an nonuniform probability. The operator is designed for DGL.
The operator outputs four sets of NDArrays to represent the sampled results (the number of NDArrays in each set is the same as the number of seed NDArrays): 1) a set of 1D NDArrays containing the sampled vertices, 2) a set of CSRNDArrays representing the sampled edges, 3) a set of 1D NDArrays with the probability that vertices are sampled, 4) a set of 1D NDArrays indicating the layer where a vertex is sampled. The first set of 1D NDArrays have a length of max_num_vertices+1. The last element in an NDArray indicate the acutal number of vertices in a subgraph. The third and fourth set of NDArrays have a length of max_num_vertices, and the valid number of vertices is the same as the ones in the first set.
Example
shape = (5, 5) prob = mx.nd.array([0.9, 0.8, 0.2, 0.4, 0.1], dtype=np.float32) data_np = np.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20], dtype=np.int64) indices_np = np.array([1,2,3,4,0,2,3,4,0,1,3,4,0,1,2,4,0,1,2,3], dtype=np.int64) indptr_np = np.array([0,4,8,12,16,20], dtype=np.int64) a = mx.nd.sparse.csr_matrix((data_np, indices_np, indptr_np), shape=shape) seed = mx.nd.array([0,1,2,3,4], dtype=np.int64) out = mx.nd.contrib.dgl_csr_neighbor_non_uniform_sample(a, prob, seed, num_args=3, num_hops=1, num_neighbor=2, max_num_vertices=5) out[0] [0 1 2 3 4 5] <NDArray 6 @cpu(0)> out[1].asnumpy() array([[ 0, 1, 2, 0, 0], [ 5, 0, 6, 0, 0], [ 9, 10, 0, 0, 0], [13, 14, 0, 0, 0], [ 0, 18, 19, 0, 0]]) out[2] [0.9 0.8 0.2 0.4 0.1] <NDArray 5 @cpu(0)> out[3] [0 0 0 0 0] <NDArray 5 @cpu(0)>
Defined in src/operator/contrib/dgl_graph.cc:L897
Parameters:  csr_matrix (NDArray) – csr matrix
 probability (NDArray) – probability vector
 seed_arrays (NDArray[]) – seed vertices
 num_hops (, optional, default=1) – Number of hops.
 num_neighbor (, optional, default=2) – Number of neighbor.
 max_num_vertices (, optional, default=100) – Max number of vertices.
 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.
dgl_csr_neighbor_uniform_sample
(*seed_arrays, **kwargs)¶ This operator samples subgraphs from a csr graph via an uniform probability. The operator is designed for DGL.
The operator outputs three sets of NDArrays to represent the sampled results (the number of NDArrays in each set is the same as the number of seed NDArrays): 1) a set of 1D NDArrays containing the sampled vertices, 2) a set of CSRNDArrays representing the sampled edges, 3) a set of 1D NDArrays indicating the layer where a vertex is sampled. The first set of 1D NDArrays have a length of max_num_vertices+1. The last element in an NDArray indicate the acutal number of vertices in a subgraph. The third set of NDArrays have a length of max_num_vertices, and the valid number of vertices is the same as the ones in the first set.
Example
shape = (5, 5) data_np = np.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20], dtype=np.int64) indices_np = np.array([1,2,3,4,0,2,3,4,0,1,3,4,0,1,2,4,0,1,2,3], dtype=np.int64) indptr_np = np.array([0,4,8,12,16,20], dtype=np.int64) a = mx.nd.sparse.csr_matrix((data_np, indices_np, indptr_np), shape=shape) a.asnumpy() seed = mx.nd.array([0,1,2,3,4], dtype=np.int64) out = mx.nd.contrib.dgl_csr_neighbor_uniform_sample(a, seed, num_args=2, num_hops=1, num_neighbor=2, max_num_vertices=5) out[0] [0 1 2 3 4 5] <NDArray 6 @cpu(0)> out[1].asnumpy() array([[ 0, 1, 0, 3, 0], [ 5, 0, 0, 7, 0], [ 9, 0, 0, 11, 0], [13, 0, 15, 0, 0], [17, 0, 19, 0, 0]]) out[2] [0 0 0 0 0] <NDArray 5 @cpu(0)>
Defined in src/operator/contrib/dgl_graph.cc:L798
Parameters:  csr_matrix (NDArray) – csr matrix
 seed_arrays (NDArray[]) – seed vertices
 num_hops (, optional, default=1) – Number of hops.
 num_neighbor (, optional, default=2) – Number of neighbor.
 max_num_vertices (, optional, default=100) – Max number of vertices.
 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.
dgl_graph_compact
(*graph_data, **kwargs)¶ This operator compacts a CSR matrix generated by dgl_csr_neighbor_uniform_sample and dgl_csr_neighbor_non_uniform_sample. The CSR matrices generated by these two operators may have many empty rows at the end and many empty columns. This operator removes these empty rows and empty columns.
Example
shape = (5, 5) data_np = np.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20], dtype=np.int64) indices_np = np.array([1,2,3,4,0,2,3,4,0,1,3,4,0,1,2,4,0,1,2,3], dtype=np.int64) indptr_np = np.array([0,4,8,12,16,20], dtype=np.int64) a = mx.nd.sparse.csr_matrix((data_np, indices_np, indptr_np), shape=shape) seed = mx.nd.array([0,1,2,3,4], dtype=np.int64) out = mx.nd.contrib.dgl_csr_neighbor_uniform_sample(a, seed, num_args=2, num_hops=1, num_neighbor=2, max_num_vertices=6) subg_v = out[0] subg = out[1] compact = mx.nd.contrib.dgl_graph_compact(subg, subg_v, graph_sizes=(subg_v[1].asnumpy()[0]), return_mapping=False) compact.asnumpy() array([[0, 0, 0, 1, 0], [2, 0, 3, 0, 0], [0, 4, 0, 0, 5], [0, 6, 0, 0, 7], [8, 9, 0, 0, 0]])
Defined in src/operator/contrib/dgl_graph.cc:L1596
Parameters:  graph_data (NDArray[]) – Input graphs and input vertex Ids.
 return_mapping (boolean, required) – Return mapping of vid and eid between the subgraph and the parent graph.
 graph_sizes (tuple of <>, required) – the number of vertices in each graph.
 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.
dgl_subgraph
(*data, **kwargs)¶ This operator constructs an induced subgraph for a given set of vertices from a graph. The operator accepts multiple sets of vertices as input. For each set of vertices, it returns a pair of CSR matrices if return_mapping is True: the first matrix contains edges with new edge Ids, the second matrix contains edges with the original edge Ids.
Example
x=[[1, 0, 0, 2], [3, 0, 4, 0], [0, 5, 0, 0], [0, 6, 7, 0]] v = [0, 1, 2] dgl_subgraph(x, v, return_mapping=True) = [[1, 0, 0], [2, 0, 3], [0, 4, 0]], [[1, 0, 0], [3, 0, 4], [0, 5, 0]]
Defined in src/operator/contrib/dgl_graph.cc:L1154
Parameters:  graph (NDArray) – Input graph where we sample vertices.
 data (NDArray[]) – The input arrays that include data arrays and states.
 return_mapping (boolean, required) – Return mapping of vid and eid between the subgraph and the parent graph.
 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.
div_sqrt_dim
(data=None, out=None, name=None, **kwargs)¶ Rescale the input by the square root of the channel dimension.
out = data / sqrt(data.shape[1])Defined in src/operator/contrib/transformer.cc:L38
Parameters: Returns: out – The output of this function.
Return type: NDArray or list of NDArrays

mxnet.ndarray.contrib.
edge_id
(data=None, u=None, v=None, out=None, name=None, **kwargs)¶ This operator implements the edge_id function for a graph stored in a CSR matrix (the value of the CSR stores the edge Id of the graph). output[i] = input[u[i], v[i]] if there is an edge between u[i] and v[i]], otherwise output[i] will be 1. Both u and v should be 1D vectors.
Example
x = [[ 1, 0, 0 ], [ 0, 2, 0 ], [ 0, 0, 3 ]] u = [ 0, 0, 1, 1, 2, 2 ] v = [ 0, 1, 1, 2, 0, 2 ] edge_id(x, u, v) = [ 1, 1, 2, 1, 1, 3 ]
 The storage type of
edge_id
output depends on storage types of inputs  edge_id(csr, default, default) = default
 default and rsp inputs are not supported
Defined in src/operator/contrib/dgl_graph.cc:L1335
Parameters: Returns: out – The output of this function.
Return type: NDArray or list of NDArrays
 The storage type of

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: Returns: out – The output of this function.
Return type: NDArray or list of NDArrays

mxnet.ndarray.contrib.
getnnz
(data=None, axis=_Null, out=None, name=None, **kwargs)¶ Number of stored values for a sparse tensor, including explicit zeros.
This operator only supports CSR matrix on CPU.
Defined in src/operator/contrib/nnz.cc:L177
Parameters: Returns: out – The output of this function.
Return type: NDArray or list of NDArrays

mxnet.ndarray.contrib.
group_adagrad_update
(weight=None, grad=None, history=None, lr=_Null, rescale_grad=_Null, clip_gradient=_Null, epsilon=_Null, out=None, name=None, **kwargs)¶ Update function for Group AdaGrad optimizer.
Referenced from Adaptive Subgradient Methods for Online Learning and Stochastic Optimization, and available at http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf but uses only a single learning rate for every row of the parameter array.
Updates are applied by:
grad = clip(grad * rescale_grad, clip_gradient) history += mean(square(grad), axis=1, keepdims=True) div = grad / sqrt(history + float_stable_eps) weight = div * lr
Weights are updated lazily if the gradient is sparse.
Note that nonzero values for the weight decay option are not supported.
Defined in src/operator/contrib/optimizer_op.cc:L71
Parameters:  weight (NDArray) – Weight
 grad (NDArray) – Gradient
 history (NDArray) – History
 lr (float, required) – Learning rate
 rescale_grad (float, optional, default=1) – Rescale gradient to grad = rescale_grad*grad.
 clip_gradient (float, optional, default=1) – Clip gradient to the range of [clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), clip_gradient).
 epsilon (float, optional, default=1e05) – Epsilon for numerical stability
 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: Returns: out – The output of this function.
Return type: NDArray or list of NDArrays

mxnet.ndarray.contrib.
index_copy
(old_tensor=None, index_vector=None, new_tensor=None, out=None, name=None, **kwargs)¶ Copies the elements of a new_tensor into the old_tensor.
This operator copies the elements by selecting the indices in the order given in index. The output will be a new tensor containing the rest elements of old tensor and the copied elements of new tensor. For example, if index[i] == j, then the i th row of new_tensor is copied to the j th row of output.
The index must be a vector and it must have the same size with the 0 th dimension of new_tensor. Also, the 0 th dimension of old_tensor must >= the 0 th dimension of new_tensor, or an error will be raised.
Examples:
x = mx.nd.zeros((5,3)) t = mx.nd.array([[1,2,3],[4,5,6],[7,8,9]]) index = mx.nd.array([0,4,2]) mx.nd.contrib.index_copy(x, index, t) [[1. 2. 3.] [0. 0. 0.] [7. 8. 9.] [0. 0. 0.] [4. 5. 6.]] <NDArray 5x3 @cpu(0)>
Defined in src/operator/contrib/index_copy.cc:L67
Parameters: Returns: out – The output of this function.
Return type: NDArray or list of NDArrays

mxnet.ndarray.contrib.
quadratic
(data=None, a=_Null, b=_Null, c=_Null, out=None, name=None, **kwargs)¶ This operators implements the quadratic function.
\[f(x) = ax^2+bx+c\]where \(x\) is an input tensor and all operations in the function are elementwise.
Example:
x = [[1, 2], [3, 4]] y = quadratic(data=x, a=1, b=2, c=3) y = [[6, 11], [18, 27]]
 The storage type of
quadratic
output depends on storage types of inputs  quadratic(csr, a, b, 0) = csr
 quadratic(default, a, b, c) = default
Defined in src/operator/contrib/quadratic_op.cc:L50
Parameters:  data (NDArray) – Input ndarray
 a (float, optional, default=0) – Coefficient of the quadratic term in the quadratic function.
 b (float, optional, default=0) – Coefficient of the linear term in the quadratic function.
 c (float, optional, default=0) – Constant term in the quadratic function.
 out (NDArray, optional) – The output NDArray to hold the result.
Returns: out – The output of this function.
Return type: NDArray or list of NDArrays
 The storage type of

mxnet.ndarray.contrib.
quantize
(data=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 userspecified min_range and max_range.
min_range and max_range are scalar floats that specify the range for the input data.
When out_type is uint8, the output is calculated using the following equation:
out[i] = (in[i]  min_range) * range(OUTPUT_TYPE) / (max_range  min_range) + 0.5,
where range(T) = numeric_limits
::max()  numeric_limits .::min() When out_type is int8, the output is calculate using the following equation by keep zero centered for the quantized value:
out[i] = sign(in[i]) * min(abs(in[i] * scale + 0.5f, quantized_range),
where quantized_range = MinAbs(max(int8), min(int8)) and scale = quantized_range / MaxAbs(min_range, max_range).
Note
This operator only supports forward propogation. DO NOT use it in training.
Defined in src/operator/quantization/quantize.cc:L74
Parameters:  data (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 ({'int8', '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

mxnet.ndarray.contrib.
quantized_concat
(*data, **kwargs)¶ Joins input arrays along a given axis.
The dimensions of the input arrays should be the same except the axis along which they will be concatenated. The dimension of the output array along the concatenated axis will be equal to the sum of the corresponding dimensions of the input arrays. All inputs with different min/max will be rescaled by using largest [min, max] pairs. If any input holds int8, then the output will be int8. Otherwise output will be uint8.
Defined in src/operator/quantization/quantized_concat.cc:L108
Parameters: Returns: out – The output of this function.
Return type: NDArray or list of NDArrays

mxnet.ndarray.contrib.
quantized_conv
(data=None, weight=None, bias=None, min_data=None, max_data=None, min_weight=None, max_weight=None, min_bias=None, max_bias=None, kernel=_Null, stride=_Null, dilate=_Null, pad=_Null, num_filter=_Null, num_group=_Null, workspace=_Null, no_bias=_Null, cudnn_tune=_Null, cudnn_off=_Null, layout=_Null, out=None, name=None, **kwargs)¶ Convolution operator for input, weight and bias data type of int8, and accumulates in type int32 for the output. For each argument, two more arguments of type float32 must be provided representing the thresholds of quantizing argument from data type float32 to int8. The final outputs contain the convolution result in int32, and min and max thresholds representing the threholds for quantizing the float32 output into int32.
Note
This operator only supports forward propogation. DO NOT use it in training.
Defined in src/operator/quantization/quantized_conv.cc:L137
Parameters:  data (NDArray) – Input data.
 weight (NDArray) – weight.
 bias (NDArray) – bias.
 min_data (NDArray) – Minimum value of data.
 max_data (NDArray) – Maximum value of data.
 min_weight (NDArray) – Minimum value of weight.
 max_weight (NDArray) – Maximum value of weight.
 min_bias (NDArray) – Minimum value of bias.
 max_bias (NDArray) – Maximum value of bias.
 kernel (Shape(tuple), required) – Convolution kernel size: (w,), (h, w) or (d, h, w)
 stride (Shape(tuple), optional, default=[]) – Convolution stride: (w,), (h, w) or (d, h, w). Defaults to 1 for each dimension.
 dilate (Shape(tuple), optional, default=[]) – Convolution dilate: (w,), (h, w) or (d, h, w). Defaults to 1 for each dimension.
 pad (Shape(tuple), optional, default=[]) – Zero pad for convolution: (w,), (h, w) or (d, h, w). Defaults to no padding.
 num_filter (int (nonnegative), required) – Convolution filter(channel) number
 num_group (int (nonnegative), optional, default=1) – Number of group partitions.
 workspace (long (nonnegative), optional, default=1024) – Maximum temporary workspace allowed (MB) in convolution.This parameter has two usages. When CUDNN is not used, it determines the effective batch size of the convolution kernel. When CUDNN is used, it controls the maximum temporary storage used for tuning the best CUDNN kernel when limited_workspace strategy is used.
 no_bias (boolean, optional, default=0) – Whether to disable bias parameter.
 cudnn_tune ({None, 'fastest', 'limited_workspace', 'off'},optional, default='None') – Whether to pick convolution algo by running performance test.
 cudnn_off (boolean, optional, default=0) – Turn off cudnn for this layer.
 layout ({None, 'NCDHW', 'NCHW', 'NCW', 'NDHWC', 'NHWC'},optional, default='None') – Set layout for input, output and weight. Empty for default layout: NCW for 1d, NCHW for 2d and NCDHW for 3d.NHWC and NDHWC are only supported on GPU.
 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.
quantized_flatten
(data=None, min_data=None, max_data=None, out=None, name=None, **kwargs)¶ Parameters: Returns: out – The output of this function.
Return type: NDArray or list of NDArrays

mxnet.ndarray.contrib.
quantized_fully_connected
(data=None, weight=None, bias=None, min_data=None, max_data=None, min_weight=None, max_weight=None, min_bias=None, max_bias=None, num_hidden=_Null, no_bias=_Null, flatten=_Null, out=None, name=None, **kwargs)¶ Fully Connected operator for input, weight and bias data type of int8, and accumulates in type int32 for the output. For each argument, two more arguments of type float32 must be provided representing the thresholds of quantizing argument from data type float32 to int8. The final outputs contain the convolution result in int32, and min and max thresholds representing the threholds for quantizing the float32 output into int32.
Note
This operator only supports forward propogation. DO NOT use it in training.
Defined in src/operator/quantization/quantized_fully_connected.cc:L241
Parameters:  data (NDArray) – Input data.
 weight (NDArray) – weight.
 bias (NDArray) – bias.
 min_data (NDArray) – Minimum value of data.
 max_data (NDArray) – Maximum value of data.
 min_weight (NDArray) – Minimum value of weight.
 max_weight (NDArray) – Maximum value of weight.
 min_bias (NDArray) – Minimum value of bias.
 max_bias (NDArray) – Maximum value of bias.
 num_hidden (int, required) – Number of hidden nodes of the output.
 no_bias (boolean, optional, default=0) – Whether to disable bias parameter.
 flatten (boolean, optional, default=1) – Whether to collapse all but the first axis of the input data tensor.
 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.
quantized_pooling
(data=None, min_data=None, max_data=None, kernel=_Null, pool_type=_Null, global_pool=_Null, cudnn_off=_Null, pooling_convention=_Null, stride=_Null, pad=_Null, p_value=_Null, count_include_pad=_Null, out=None, name=None, **kwargs)¶ Pooling operator for input and output data type of int8. The input and output data comes with min and max thresholds for quantizing the float32 data into int8.
Note
This operator only supports forward propogation. DO NOT use it in training. This operator only supports pool_type of avg or max.
Defined in src/operator/quantization/quantized_pooling.cc:L142
Parameters:  data (NDArray) – Input data.
 min_data (NDArray) – Minimum value of data.
 max_data (NDArray) – Maximum value of data.
 kernel (Shape(tuple), optional, default=[]) – Pooling kernel size: (y, x) or (d, y, x)
 pool_type ({'avg', 'lp', 'max', 'sum'},optional, default='max') – Pooling type to be applied.
 global_pool (boolean, optional, default=0) – Ignore kernel size, do global pooling based on current input feature map.
 cudnn_off (boolean, optional, default=0) – Turn off cudnn pooling and use MXNet pooling operator.
 pooling_convention ({'full', 'same', 'valid'},optional, default='valid') – Pooling convention to be applied.
 stride (Shape(tuple), optional, default=[]) – Stride: for pooling (y, x) or (d, y, x). Defaults to 1 for each dimension.
 pad (Shape(tuple), optional, default=[]) – Pad for pooling: (y, x) or (d, y, x). Defaults to no padding.
 p_value (int or None, optional, default='None') – Value of p for Lp pooling, can be 1 or 2, required for Lp Pooling.
 count_include_pad (boolean or None, optional, default=None) – Only used for AvgPool, specify whether to count padding elements for averagecalculation. For example, with a 5*5 kernel on a 3*3 corner of a image,the sum of the 9 valid elements will be divided by 25 if this is set to true,or it will be divided by 9 if this is set to false. Defaults to true.
 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.
requantize
(data=None, min_range=None, max_range=None, min_calib_range=_Null, max_calib_range=_Null, out=None, name=None, **kwargs)¶ Given data that is quantized in int32 and the corresponding thresholds, requantize the data into int8 using min and max thresholds either calculated at runtime or from calibration. It’s highly recommended to precalucate the min and max thresholds through calibration since it is able to save the runtime of the operator and improve the inference accuracy.
Note
This operator only supports forward propogation. DO NOT use it in training.
Defined in src/operator/quantization/requantize.cc:L60
Parameters:  data (NDArray) – A ndarray/symbol of type int32
 min_range (NDArray) – The original minimum scalar value in the form of float32 used for quantizing data into int32.
 max_range (NDArray) – The original maximum scalar value in the form of float32 used for quantizing data into int32.
 min_calib_range (float or None, optional, default=None) – The minimum scalar value in the form of float32 obtained through calibration. If present, it will be used to requantize the int32 data into int8.
 max_calib_range (float or None, optional, default=None) – The maximum scalar value in the form of float32 obtained through calibration. If present, it will be used to requantize the int32 data into int8.
 out (NDArray, optional) – The output NDArray to hold the result.
Returns: out – The output of this function.
Return type: NDArray or list of NDArrays