# 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¶

 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 2-D deformable convolution on 4-D input. DeformablePSROIPooling Performs deformable position-sensitive region-of-interest pooling on inputs. MultiBoxDetection Convert multibox detection predictions. MultiBoxPrior Generate prior(anchor) boxes from data, sizes and ratios. MultiBoxTarget Compute Multibox training targets MultiProposal Generate region proposals via RPN PSROIPooling Performs region-of-interest pooling on inputs. Proposal Generate region proposals via RPN ROIAlign This operator takes a 4D feature map as an input array and region proposals as rois, then align the feature map over sub-regions of input and produces a fixed-sized output array. count_sketch Apply CountSketch to input: map a d-dimension data to k-dimension data” ctc_loss Connectionist Temporal Classification Loss. dequantize Dequantize the input tensor into a float tensor. fft Apply 1D FFT to input” ifft Apply 1D ifft to input” quantize Quantize a input tensor from float to out_type, with user-specified min_range and max_range. foreach Run a for loop with user-defined computation over Symbols on dimension 0. while_loop Run a while loop with user-defined computation and loop condition. cond Run an if-then-else using user-defined condition and computation

## API Reference¶

Contrib Symbol API of MXNet.

mxnet.symbol.contrib.rand_zipfian(true_classes, num_sampled, range_max)[source]

Draw random samples from an approximately log-uniform 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 log-uniform 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 (Symbol) – The target classes in 1-D. num_sampled (int) – The number of classes to randomly sample. range_max (int) – The number of possible classes. samples (Symbol) – The sampled candidate classes in 1-D int64 dtype. expected_count_true (Symbol) – The expected count for true classes in 1-D float64 dtype. expected_count_sample (Symbol) – The expected count for sampled candidates in 1-D 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.symbol.contrib.foreach(body, data, init_states, name='foreach')[source]

Run a for loop with user-defined computation over Symbols 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 a symbol or a list of symbols. If data is a symbol, data1 is a symbol. Otherwise, data1 is a list of symbols and has the same size as data. states is a list of symbols and have the same size as init_states. Similarly, out can be either a symbol or a list of symbols, which are concatenated as the first output of foreach; states from the last execution of body are the second output of foreach.

foreach can output only output data or states. If a user only wants states, the body function can return ([], states). Similarly, if a user only wants output data, the body function can return (out, []).

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 (a symbol or a list of symbols.) – The input data. init_states (a symbol or a list of symbols.) – The initial values of the loop states. name (string.) – The name of the operator. outputs (a Symbol or a list of Symbols.) – The output data concatenated from the output of all iterations. states (a list of Symbols.) – The loop states in the last iteration.

Examples

>>> step = lambda data, states: (data + states[0], [states[0] * 2])
>>> data = mx.sym.var('data')
>>> states = [mx.sym.var('state')]
>>> outs, states = mx.sym.contrib.foreach(step, data, states)

mxnet.symbol.contrib.while_loop(cond, func, loop_vars, max_iterations=None, name='while_loop')[source]

Run a while loop with user-defined 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 Symbols on which the computation uses.

cond is a user-defined function, used as the loop condition. It consumes loop_vars, and produces a scalar MXNet symbol, 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) => Symbol.

func is a user-defined 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 i-th 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) => (List[Symbol] step_output, List[Symbol] 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 i-th element are all i-th 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 Symbols in the first list are max_iterations, due to lack of dynamic shape inference.

Warning

Even if cond is never satisfied, while_loop returns a list of outputs with inferred dtype and shape. This is different from the Symbol version, where in this case step_outputs are assumed as an empty list.

Parameters: cond (a Python function.) – The loop condition. func (a Python function.) – The loop body. loop_vars (list of Symbol.) – The initial values of the loop variables. max_iterations (a python int.) – Maximum number of iterations. outputs (list of Symbols) – stacked output from each step states (list of Symbols) – final state

Examples

>>> cond = lambda i, s: i <= 5
>>> func = lambda i, s: ([i + s], [i + 1, s + i])
>>> loop_vars = (mx.sym.var('i'), mx.sym.var('s'))
>>> outputs, states = mx.sym.contrib.while_loop(cond, func, loop_vars, max_iterations=10)

mxnet.symbol.contrib.cond(pred, then_func, else_func, name='cond')[source]

Run an if-then-else using user-defined condition and computation

This operator simulates a if-like branch which chooses to do one of the two customized computations according to the specified condition.

pred is a scalar MXNet Symbol, indicating which branch of computation should be used.

then_func is a user-defined function, used as computation of the then branch. It produces outputs, which is a list of Symbols. The signature of then_func should be then_func() => List[Symbol].

else_func is a user-defined function, used as computation of the else branch. It produces outputs, which is a list of Symbols. The signature of else_func should be else_func() => List[Symbol].

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 Symbol 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. outputs a list of Symbols, representing the result of computation.

Examples

>>> a, b = mx.sym.var('a'), mx.sym.var('b')
>>> pred = a * b < 5
>>> then_func = lambda: (a + 5) * (b + 5)
>>> else_func = lambda: (a - 5) * (b - 5)
>>> outputs = mx.sym.contrib.cond(pred, then_func, else_func)

mxnet.symbol.contrib.AdaptiveAvgPooling2D(data=None, output_size=_Null, name=None, attr=None, out=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).

Parameters: data (Symbol) – Input data output_size (Shape(tuple), optional, default=[]) – int (output size) or a tuple of int for output (height, width). name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
mxnet.symbol.contrib.BilinearResize2D(data=None, height=_Null, width=_Null, name=None, attr=None, out=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: data (Symbol) – Input data height (int, required) – output height (required) width (int, required) – output width (required) name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
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 i-th channel in the last dimension corresponding to i-th label for i between 0 and alphabet_size-1 (i.e always 0-indexed). Alphabet size should include one additional value reserved for blank label. When blank_label is "first", the 0-th channel is be reserved for activation of blank label, or otherwise if it is “last”, (alphabet_size-1)-th channel should be reserved for blank label.

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

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

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

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


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

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


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

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

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

Parameters: data (Symbol) – Input data to the ctc_loss op. label (Symbol) – Ground-truth 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, and -1 when last label is reserved for blank. See blank_label. blank_label ({'first', 'last'},optional, default='first') – Set the label that is reserved for blank label.If “first”, 0-th label is reserved, and label values for tokens in the vocabulary are between 1 and alphabet_size-1, and the padding mask is -1. If “last”, last label value alphabet_size-1 is reserved for blank label instead, and label values for tokens in the vocabulary are between 0 and alphabet_size-2, and the padding mask is 0. name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
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 2-D deformable convolution on 4-D input.

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

For 2-D deformable convolution, the shapes are

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

Define:

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


then we have:

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


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

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

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

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

Both weight and bias are learnable parameters.

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

Parameters: data (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). Defaults to 1 for each dimension. dilate (Shape(tuple), optional, default=[]) – Convolution dilate: (h, w) or (d, h, w). Defaults to 1 for each dimension. pad (Shape(tuple), optional, default=[]) – Zero pad for convolution: (h, w) or (d, h, w). Defaults to no padding. num_filter (int (non-negative), required) – Convolution filter(channel) number num_group (int (non-negative), optional, default=1) – Number of group partitions. num_deformable_group (int (non-negative), optional, default=1) – Number of deformable group partitions. workspace (long (non-negative), optional, default=1024) – Maximum temperal workspace allowed for convolution (MB). no_bias (boolean, optional, default=0) – Whether to disable bias parameter. layout ({None, 'NCDHW', 'NCHW', 'NCW'},optional, default='None') – Set layout for input, output and weight. Empty for default layout: NCW for 1d, NCHW for 2d and NCDHW for 3d. name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
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 position-sensitive region-of-interest pooling on inputs. The DeformablePSROIPooling operation is described in https://arxiv.org/abs/1703.06211 .batch_size will change to the number of region bounding boxes after DeformablePSROIPooling

Parameters: data (Symbol) – Input data to the pooling operator, a 4D Feature maps rois (Symbol) – Bounding box coordinates, a 2D array of [[batch_index, x1, y1, x2, y2]]. (x1, y1) and (x2, y2) are top left and down right corners of designated region of interest. batch_index indicates the index of corresponding image in the input data trans (Symbol) – transition parameter spatial_scale (float, required) – Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal of total stride in convolutional layers output_dim (int, required) – fix output dim group_size (int, required) – fix group size pooled_size (int, required) – fix pooled size part_size (int, optional, default='0') – fix part size sample_per_part (int, optional, default='1') – fix samples per part trans_std (float, optional, default=0) – fix transition std no_trans (boolean, optional, default=0) – Whether to disable trans parameter. name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
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 out-of-boundary boxes. threshold (float, optional, default=0.01) – Threshold to be a positive prediction. background_id (int, optional, default='0') – Background id. nms_threshold (float, optional, default=0.5) – Non-maximum suppression threshold. force_suppress (boolean, optional, default=0) – Suppress all detections regardless of class_id. variances (tuple of , optional, default=[0.1,0.1,0.2,0.2]) – Variances to be decoded from box regression output. nms_topk (int, optional, default='-1') – Keep maximum top k detections before nms, -1 for no limit. name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
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 out-of-boundary boxes. steps (tuple of , optional, default=[-1,-1]) – Priorbox step across y and x, -1 for auto calculation. offsets (tuple of , optional, default=[0.5,0.5]) – Priorbox center offsets, y and x respectively name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
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) – Anchor-GT overlap threshold to be regarded as a positive match. ignore_label (float, optional, default=-1) – Label for ignored anchors. negative_mining_ratio (float, optional, default=-1) – Max negative to positive samples ratio, use -1 to disable mining negative_mining_thresh (float, optional, default=0.5) – Threshold used for negative mining. minimum_negative_samples (int, optional, default='0') – Minimum number of negative samples. variances (tuple of , optional, default=[0.1,0.1,0.2,0.2]) – Variances to be encoded in box regression target. name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
mxnet.symbol.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, name=None, attr=None, out=None, **kwargs)

Generate region proposals via RPN

Parameters: cls_prob (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 non-maximumsuppresion(suppress boxes with IoU >= this threshold threshold (float, optional, default=0.7) – NMS value, below which to suppress. rpn_min_size (int, optional, default='16') – Minimum height or width in proposal scales (tuple of , optional, default=[4,8,16,32]) – Used to generate anchor windows by enumerating scales ratios (tuple of , optional, default=[0.5,1,2]) – Used to generate anchor windows by enumerating ratios feature_stride (int, optional, default='16') – The size of the receptive field each unit in the convolution layer of the rpn,for example the product of all stride’s prior to this layer. output_score (boolean, optional, default=0) – Add score to outputs iou_loss (boolean, optional, default=0) – Usage of IoU Loss name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
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 region-of-interest pooling on inputs. Resize bounding box coordinates by spatial_scale and crop input feature maps accordingly. The cropped feature maps are pooled by max pooling to a fixed size output indicated by pooled_size. batch_size will change to the number of region bounding boxes after PSROIPooling

Parameters: data (Symbol) – Input data to the pooling operator, a 4D Feature maps rois (Symbol) – Bounding box coordinates, a 2D array of [[batch_index, x1, y1, x2, y2]]. (x1, y1) and (x2, y2) are top left and down right corners of designated region of interest. batch_index indicates the index of corresponding image in the input data spatial_scale (float, required) – Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal of total stride in convolutional layers output_dim (int, required) – fix output dim pooled_size (int, required) – fix pooled size group_size (int, optional, default='0') – fix group size name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
mxnet.symbol.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, name=None, attr=None, out=None, **kwargs)

Generate region proposals via RPN

Parameters: cls_prob (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 non-maximumsuppresion(suppress boxes with IoU >= this threshold threshold (float, optional, default=0.7) – NMS value, below which to suppress. rpn_min_size (int, optional, default='16') – Minimum height or width in proposal scales (tuple of , optional, default=[4,8,16,32]) – Used to generate anchor windows by enumerating scales ratios (tuple of , optional, default=[0.5,1,2]) – Used to generate anchor windows by enumerating ratios feature_stride (int, optional, default='16') – The size of the receptive field each unit in the convolution layer of the rpn,for example the product of all stride’s prior to this layer. output_score (boolean, optional, default=0) – Add score to outputs iou_loss (boolean, optional, default=0) – Usage of IoU Loss name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
mxnet.symbol.contrib.ROIAlign(data=None, rois=None, pooled_size=_Null, spatial_scale=_Null, sample_ratio=_Null, name=None, attr=None, out=None, **kwargs)

This operator takes a 4D feature map as an input array and region proposals as rois, then align the feature map over sub-regions of input and produces a fixed-sized output array. This operator is typically used in Faster R-CNN & Mask R-CNN 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.

He, Kaiming, et al. “Mask R-CNN.” ICCV, 2017

Defined in src/operator/contrib/roi_align.cc:L522

Parameters: data (Symbol) – Input data to the pooling operator, a 4D Feature maps rois (Symbol) – 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. name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
mxnet.symbol.contrib.SparseEmbedding(data=None, weight=None, input_dim=_Null, output_dim=_Null, dtype=_Null, sparse_grad=_Null, name=None, attr=None, out=None, **kwargs)

Maps integer indices to vector representations (embeddings).

note:: contrib.SparseEmbedding is deprecated, use Embedding instead.

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

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

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

The storage type of 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 n-grams(2-gram). So, x = [(w1,w3), (w0,w2)]
x = [[ 1.,  3.],
[ 0.,  2.]]

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

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


Defined in src/operator/tensor/indexing_op.cc:L343

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', '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. name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
mxnet.symbol.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, name=None, attr=None, out=None, **kwargs)

Batch normalization.

Normalizes a data batch by mean and variance, and applies a scale gamma as well as offset beta. Standard BN [1] implementation only normalize the data within each device. SyncBN normalizes the input within the whole mini-batch. We follow the sync-onece 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 and beta have shape (k,). If output_mean_var is set to be true, then outputs both data_mean and data_var as well, which are needed for the backward pass.

Besides the inputs and the outputs, this operator accepts two auxiliary states, moving_mean and moving_var, which are k-length 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, then moving_mean and moving_var are used instead of data_mean and data_var to compute the output. It is often used during inference.

Both gamma and beta are learnable parameters. But if fix_gamma is true, then set gamma 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 (Symbol) – Input data to batch normalization gamma (Symbol) – gamma array beta (Symbol) – beta array moving_mean (Symbol) – running mean of input moving_var (Symbol) – 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 batch-norm. This will force change batch-norm 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. name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
mxnet.symbol.contrib.backward_quadratic(name=None, attr=None, out=None, **kwargs)
Parameters: name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
mxnet.symbol.contrib.bipartite_matching(data=None, is_ascend=_Null, threshold=_Null, topk=_Null, name=None, attr=None, out=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 non-matched elements in rows. y : matched row indices.

Note:

Zero gradients are back-propagated 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=1e-12, is_ascend=False)
x = [1, -1, 0]
y = [2, 0]


Defined in src/operator/contrib/bounding_box.cc:L169

Parameters: data (Symbol) – 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 name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
mxnet.symbol.contrib.box_iou(lhs=None, rhs=None, format=_Null, name=None, attr=None, out=None, **kwargs)
Bounding box overlap of two arrays.

The overlap is defined as Intersection-over-Union, 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 back-propagated 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:L123

Parameters: lhs (Symbol) – The first input rhs (Symbol) – 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]. name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
mxnet.symbol.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, name=None, attr=None, out=None, **kwargs)

Apply non-maximum 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 back-propagation, 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:L82

Parameters: data (Symbol) – 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]. name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
mxnet.symbol.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, name=None, attr=None, out=None, **kwargs)

Apply non-maximum 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 back-propagation, 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:L82

Parameters: data (Symbol) – 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]. name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
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 d-dimension data to k-dimension data”

Note

count_sketch is only available on GPU.

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

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

Example:

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


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

Parameters: data (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. The result symbol. Symbol
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 i-th channel in the last dimension corresponding to i-th label for i between 0 and alphabet_size-1 (i.e always 0-indexed). Alphabet size should include one additional value reserved for blank label. When blank_label is "first", the 0-th channel is be reserved for activation of blank label, or otherwise if it is “last”, (alphabet_size-1)-th channel should be reserved for blank label.

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

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

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

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


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

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


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

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

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

Parameters: data (Symbol) – Input data to the ctc_loss op. label (Symbol) – Ground-truth 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, and -1 when last label is reserved for blank. See blank_label. blank_label ({'first', 'last'},optional, default='first') – Set the label that is reserved for blank label.If “first”, 0-th label is reserved, and label values for tokens in the vocabulary are between 1 and alphabet_size-1, and the padding mask is -1. If “last”, last label value alphabet_size-1 is reserved for blank label instead, and label values for tokens in the vocabulary are between 0 and alphabet_size-2, and the padding mask is 0. name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
mxnet.symbol.contrib.dequantize(data=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 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 (Symbol) – A ndarray/symbol of type uint8 min_range (Symbol) – The minimum scalar value possibly produced for the input in float32 max_range (Symbol) – The maximum scalar value possibly produced for the input in float32 out_type ({'float32'},optional, default='float32') – Output data type. name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
mxnet.symbol.contrib.div_sqrt_dim(data=None, name=None, attr=None, out=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: data (Symbol) – The input array. name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
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 sub-batch to be forwarded at one time name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
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 sub-batch to be forwarded at one time name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
mxnet.symbol.contrib.quadratic(data=None, a=_Null, b=_Null, c=_Null, name=None, attr=None, out=None, **kwargs)

This operators implements the quadratic function: .. math:

f(x) = ax^2+bx+c


where $$x$$ is an input tensor and all operations in the function are element-wise. 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

Parameters: data (Symbol) – 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. name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
mxnet.symbol.contrib.quantize(data=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 user-specified 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 (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 ({'int8', 'uint8'},optional, default='uint8') – Output data type. name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
mxnet.symbol.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, name=None, attr=None, out=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 (Symbol) – Input data. weight (Symbol) – weight. bias (Symbol) – bias. min_data (Symbol) – Minimum value of data. max_data (Symbol) – Maximum value of data. min_weight (Symbol) – Minimum value of weight. max_weight (Symbol) – Maximum value of weight. min_bias (Symbol) – Minimum value of bias. max_bias (Symbol) – 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 (non-negative), required) – Convolution filter(channel) number num_group (int (non-negative), optional, default=1) – Number of group partitions. workspace (long (non-negative), 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. name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
mxnet.symbol.contrib.quantized_flatten(data=None, min_data=None, max_data=None, name=None, attr=None, out=None, **kwargs)
Parameters: data (Symbol) – A ndarray/symbol of type float32 min_data (Symbol) – The minimum scalar value possibly produced for the data max_data (Symbol) – The maximum scalar value possibly produced for the data name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
mxnet.symbol.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, name=None, attr=None, out=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:L90

Parameters: data (Symbol) – Input data. weight (Symbol) – weight. bias (Symbol) – bias. min_data (Symbol) – Minimum value of data. max_data (Symbol) – Maximum value of data. min_weight (Symbol) – Minimum value of weight. max_weight (Symbol) – Maximum value of weight. min_bias (Symbol) – Minimum value of bias. max_bias (Symbol) – 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. name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
mxnet.symbol.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, name=None, attr=None, out=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:L129

Parameters: data (Symbol) – Input data. min_data (Symbol) – Minimum value of data. max_data (Symbol) – 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', '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. name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol
mxnet.symbol.contrib.requantize(data=None, min_range=None, max_range=None, min_calib_range=_Null, max_calib_range=_Null, name=None, attr=None, out=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 pre-calucate 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 (Symbol) – A ndarray/symbol of type int32 min_range (Symbol) – The original minimum scalar value in the form of float32 used for quantizing data into int32. max_range (Symbol) – 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. name (string, optional.) – Name of the resulting symbol. The result symbol. Symbol