Overview¶

This document lists the contrib routines of the symbolic expression package:

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.

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

Contrib¶

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.
Returns:	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.sym.Variable('true_cls')
>>> samples, exp_count_true, exp_count_sample = mx.sym.contrib.rand_zipfian(true_cls, 4, 5)
>>> samples.eval(true_cls=mx.nd.array([3]))[0].asnumpy()
array([1, 3, 3, 3])
>>> exp_count_true.eval(true_cls=mx.nd.array([3]))[0].asnumpy()
array([0.12453879])
>>> exp_count_sample.eval(true_cls=mx.nd.array([3]))[0].asnumpy()
array([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 nested lists of symbols.) – The initial values of the loop states. name (string.) – The name of the operator.
Returns:	outputs (a Symbol or nested lists of Symbols.) – The output data concatenated from the output of all iterations. states (a Symbol or nested lists 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 Symbol or nested lists 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) => (Symbol or nested List[Symbol] step_output, Symbol or nested 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 (a Symbol or nested lists of Symbol.) – The initial values of the loop variables. max_iterations (a python int.) – Maximum number of iterations.
Returns:	outputs (a Symbol or nested lists of Symbols) – stacked output from each step states (a Symbol or nested lists 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() => nested 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() => nested 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.
Returns:	outputs
Return type:	a Symbol or nested lists 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).

Defined in src/operator/contrib/adaptive_avg_pooling.cc:L214

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.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.contrib.BilinearResize2D(data=None, like=None, height=_Null, width=_Null, scale_height=_Null, scale_width=_Null, mode=_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:L193

Parameters:	data (Symbol) – Input data like (Symbol) – Resize data to it’s shape height (int, optional, default='1') – output height (required, but ignored if scale_height is defined or mode is not “size”) width (int, optional, default='1') – output width (required, but ignored if scale_width is defined or mode is not “size”) scale_height (float or None, optional, default=None) – sampling scale of the height (optional, used in modes “scale” and “odd_scale”) scale_width (float or None, optional, default=None) – sampling scale of the width (optional, used in modes “scale” and “odd_scale”) mode ({'like', 'odd_scale', 'size', 'to_even_down', 'to_even_up', 'to_odd_down', 'to_odd_up'},optional, default='size') – resizing mode. “simple” - output height equals parameter “height” if “scale_height” parameter is not defined or input height multiplied by “scale_height” otherwise. Same for width;”odd_scale” - if original height or width is odd, then result height is calculated like result_h = (original_h - 1) * scale + 1; for scale > 1 the result shape would be like if we did deconvolution with kernel = (1, 1) and stride = (height_scale, width_scale); and for scale < 1 shape would be like we did convolution with kernel = (1, 1) and stride = (int(1 / height_scale), int( 1/ width_scale);”like” - resize first input to the height and width of second input; “to_even_down” - resize input to nearest lower even height and width (if original height is odd then result height = original height - 1);”to_even_up” - resize input to nearest bigger even height and width (if original height is odd then result height = original height + 1);”to_odd_down” - resize input to nearest odd height and width (if original height is odd then result height = original height - 1);”to_odd_up” - resize input to nearest odd height and width (if original height is odd then result height = original height + 1); name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	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.

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 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/nn/ctc_loss.cc:L100

Parameters:	data (Symbol) – Input ndarray 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.
Returns:	The result symbol.
Return type:	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] * 2, height, width)
• weight: (num_filter, channel, kernel[0], kernel[1])
• bias: (num_filter,)
• out: (batch_size, num_filter, out_height, out_width).

Define:

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

then we have:

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

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

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

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

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

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, required) – Convolution filter(channel) number num_group (int, optional, default='1') – Number of group partitions. num_deformable_group (int, 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.
Returns:	The result symbol.
Return type:	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.
Returns:	The result symbol.
Return type:	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=0.00999999978) – 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.
Returns:	The result symbol.
Return type:	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.
Returns:	The result symbol.
Return type:	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.
Returns:	The result symbol.
Return type:	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 before applying NMS to RPN proposals rpn_post_nms_top_n (int, optional, default='300') – Number of top scoring boxes to keep after applying NMS to RPN proposals threshold (float, optional, default=0.699999988) – NMS value, below which to suppress. rpn_min_size (int, optional, default='16') – Minimum height or width in proposal scales (tuple of , optional, default=[4,8,16,32]) – Used to generate anchor windows by enumerating scales ratios (tuple of , optional, default=[0.5,1,2]) – Used to generate anchor windows by enumerating ratios feature_stride (int, optional, default='16') – The size of the receptive field each unit in the convolution layer of the rpn,for example the product of all stride’s prior to this layer. output_score (boolean, optional, default=0) – Add score to outputs iou_loss (boolean, optional, default=0) – Usage of IoU Loss name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	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.
Returns:	The result symbol.
Return type:	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 before applying NMS to RPN proposals rpn_post_nms_top_n (int, optional, default='300') – Number of top scoring boxes to keep after applying NMS to RPN proposals threshold (float, optional, default=0.699999988) – NMS value, below which to suppress. rpn_min_size (int, optional, default='16') – Minimum height or width in proposal scales (tuple of , optional, default=[4,8,16,32]) – Used to generate anchor windows by enumerating scales ratios (tuple of , optional, default=[0.5,1,2]) – Used to generate anchor windows by enumerating ratios feature_stride (int, optional, default='16') – The size of the receptive field each unit in the convolution layer of the rpn,for example the product of all stride’s prior to this layer. output_score (boolean, optional, default=0) – Add score to outputs iou_loss (boolean, optional, default=0) – Usage of IoU Loss name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.contrib.ROIAlign(data=None, rois=None, pooled_size=_Null, spatial_scale=_Null, sample_ratio=_Null, position_sensitive=_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.

References

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

Defined in src/operator/contrib/roi_align.cc:L538

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. position_sensitive (boolean, optional, default=0) – Whether to perform position-sensitive RoI pooling. PSRoIPooling is first proposaled by R-FCN and it can reduce the input channels by ph*pw times, where (ph, pw) is the pooled_size name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	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:L595

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.
Returns:	The result symbol.
Return type:	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=0.00100000005) – Epsilon to prevent div 0 momentum (float, optional, default=0.899999976) – 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, required) – 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.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.contrib.backward_gradientmultiplier(data=None, scalar=_Null, name=None, attr=None, out=None, **kwargs)¶

Parameters:	data (Symbol) – source input scalar (float) – scalar input name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.contrib.backward_hawkesll(name=None, attr=None, out=None, **kwargs)¶

Parameters:	name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.contrib.backward_index_copy(name=None, attr=None, out=None, **kwargs)¶

Parameters:	name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.contrib.backward_quadratic(name=None, attr=None, out=None, **kwargs)¶

Parameters:	name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	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:L180

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.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.contrib.boolean_mask(data=None, index=None, axis=_Null, name=None, attr=None, out=None, **kwargs)¶

Given an n-d NDArray data, and a 1-d NDArray index, the operator produces an un-predeterminable shaped n-d NDArray out, which stands for the rows in x where the corresonding element in index is non-zero.

>>> 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:L211

Parameters:	data (Symbol) – Data index (Symbol) – Mask axis (int, optional, default='0') – An integer that represents the axis in NDArray to mask from. name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	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:L134

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.
Returns:	The result symbol.
Return type:	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, background_id=_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, smaller scores and background boxes 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.

• background_id: optional, default=-1, class id for background boxes, useful when id_index >= 0 which means boxes with background id will be filtered before nms.

• 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:L93

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. background_id (int, optional, default='-1') – Optional, id of the background class which will be ignored in nms. 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.
Returns:	The result symbol.
Return type:	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, background_id=_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, smaller scores and background boxes 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.

• background_id: optional, default=-1, class id for background boxes, useful when id_index >= 0 which means boxes with background id will be filtered before nms.

• 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:L93

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. background_id (int, optional, default='-1') – Optional, id of the background class which will be ignored in nms. 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.
Returns:	The result symbol.
Return type:	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.
Returns:	The result symbol.
Return type:	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.

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 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/nn/ctc_loss.cc:L100

Parameters:	data (Symbol) – Input ndarray 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.
Returns:	The result symbol.
Return type:	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:L83

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.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.contrib.dgl_adjacency(data=None, name=None, attr=None, out=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:L1393

Parameters:	data (Symbol) – Input ndarray name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.contrib.dgl_csr_neighbor_non_uniform_sample(*seed_arrays, **kwargs)¶

This operator samples sub-graph from a csr graph via an non-uniform 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:L883 This function support variable length of positional input.

Parameters:	csr_matrix (Symbol) – csr matrix probability (Symbol) – probability vector seed_arrays (Symbol[]) – 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. name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.contrib.dgl_csr_neighbor_uniform_sample(*seed_arrays, **kwargs)¶

This operator samples sub-graphs 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:L784 This function support variable length of positional input.

Parameters:	csr_matrix (Symbol) – csr matrix seed_arrays (Symbol[]) – 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. name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.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:L1582 This function support variable length of positional input.

Parameters:	graph_data (Symbol[]) – 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. name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.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:L1140 This function support variable length of positional input.

Parameters:	graph (Symbol) – Input graph where we sample vertices. data (Symbol[]) – 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. name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	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.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.contrib.edge_id(data=None, u=None, v=None, name=None, attr=None, out=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:L1321

Parameters:	data (Symbol) – Input ndarray u (Symbol) – u ndarray v (Symbol) – v ndarray name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	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.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.contrib.getnnz(data=None, axis=_Null, name=None, attr=None, out=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:	data (Symbol) – Input axis (int or None, optional, default='None') – Select between the number of values across the whole matrix, in each column, or in each row. name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.contrib.gradientmultiplier(data=None, scalar=_Null, name=None, attr=None, out=None, **kwargs)¶

This operator implements the gradient multiplier function. In forward pass it acts as an identity transform. During backpropagation it multiplies the gradient from the subsequent level by a scalar factor lambda and passes it to the preceding layer.

Defined in src/operator/contrib/gradient_multiplier_op.cc:L78

Parameters:	data (Symbol) – The input array. scalar (float) – lambda multiplier name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.contrib.group_adagrad_update(weight=None, grad=None, history=None, lr=_Null, rescale_grad=_Null, clip_gradient=_Null, epsilon=_Null, name=None, attr=None, out=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 non-zero values for the weight decay option are not supported.

Defined in src/operator/contrib/optimizer_op.cc:L71

Parameters:	weight (Symbol) – Weight grad (Symbol) – Gradient history (Symbol) – 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=9.99999975e-06) – Epsilon for numerical stability name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.contrib.hawkesll(lda=None, alpha=None, beta=None, state=None, lags=None, marks=None, valid_length=None, max_time=None, name=None, attr=None, out=None, **kwargs)¶

Computes the log likelihood of a univariate Hawkes process.

The log likelihood is calculated on point process observations represented as ragged matrices for lags (interarrival times w.r.t. the previous point), and marks (identifiers for the process ID). Note that each mark is considered independent, i.e., computes the joint likelihood of a set of Hawkes processes determined by the conditional intensity:

\[\lambda_k^*(t) = \lambda_k + \alpha_k \sum_{\{t_i < t, y_i = k\}} \beta_k \exp(-\beta_k (t - t_i))\]

where \(\lambda_k\) specifies the background intensity lda, \(\alpha_k\) specifies the branching ratio or alpha, and \(\beta_k\) the delay density parameter beta.

lags and marks are two NDArrays of shape (N, T) and correspond to the representation of the point process observation, the first dimension corresponds to the batch index, and the second to the sequence. These are “left-aligned” ragged matrices (the first index of the second dimension is the beginning of every sequence. The length of each sequence is given by valid_length, of shape (N,) where valid_length[i] corresponds to the number of valid points in lags[i, :] and marks[i, :].

max_time is the length of the observation period of the point process. That is, specifying max_time[i] = 5 computes the likelihood of the i-th sample as observed on the time interval \((0, 5]\). Naturally, the sum of all valid lags[i, :valid_length[i]] must be less than or equal to 5.

The input state specifies the memory of the Hawkes process. Invoking the memoryless property of exponential decays, we compute the memory as

\[s_k(t) = \sum_{t_i < t} \exp(-\beta_k (t - t_i)).\]

The state to be provided is \(s_k(0)\) and carries the added intensity due to past events before the current batch. \(s_k(T)\) is returned from the function where \(T\) is max_time[T].

Example:

# define the Hawkes process parameters
lda = nd.array([1.5, 2.0, 3.0]).tile((N, 1))
alpha = nd.array([0.2, 0.3, 0.4])  # branching ratios should be < 1
beta = nd.array([1.0, 2.0, 3.0])

# the "data", or observations
ia_times = nd.array([[6, 7, 8, 9], [1, 2, 3, 4], [3, 4, 5, 6], [8, 9, 10, 11]])
marks = nd.zeros((N, T)).astype(np.int32)

# starting "state" of the process
states = nd.zeros((N, K))

valid_length = nd.array([1, 2, 3, 4])  # number of valid points in each sequence
max_time = nd.ones((N,)) * 100.0  # length of the observation period

A = nd.contrib.hawkesll(
    lda, alpha, beta, states, ia_times, marks, valid_length, max_time
)

References:

• Bacry, E., Mastromatteo, I., & Muzy, J. F. (2015). Hawkes processes in finance. Market Microstructure and Liquidity , 1(01), 1550005.

Defined in src/operator/contrib/hawkes_ll.cc:L84

Parameters:	lda (Symbol) – Shape (N, K) The intensity for each of the K processes, for each sample alpha (Symbol) – Shape (K,) The infectivity factor (branching ratio) for each process beta (Symbol) – Shape (K,) The decay parameter for each process state (Symbol) – Shape (N, K) the Hawkes state for each process lags (Symbol) – Shape (N, T) the interarrival times marks (Symbol) – Shape (N, T) the marks (process ids) valid_length (Symbol) – The number of valid points in the process max_time (Symbol) – the length of the interval where the processes were sampled name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	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.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.contrib.index_array(data=None, axes=_Null, name=None, attr=None, out=None, **kwargs)¶

Returns an array of indexes of the input array.

For an input array with shape \((d_1, d_2, ..., d_n)\), index_array returns a \((d_1, d_2, ..., d_n, n)\) array idx, where \(idx[i_1, i_2, ..., i_n, :] = [i_1, i_2, ..., i_n]\).

Additionally, when the parameter axes is specified, idx will be a \((d_1, d_2, ..., d_n, m)\) array where m is the length of axes, and the following equality will hold: \(idx[i_1, i_2, ..., i_n, j] = i_{axes[j]}\).

Examples:

x = mx.nd.ones((3, 2))

mx.nd.contrib.index_array(x) = [[[0 0]
                                 [0 1]]

                                [[1 0]
                                 [1 1]]

                                [[2 0]
                                 [2 1]]]

x = mx.nd.ones((3, 2, 2))

mx.nd.contrib.index_array(x, axes=(1, 0)) = [[[[0 0]
                                               [0 0]]

                                              [[1 0]
                                               [1 0]]]


                                             [[[0 1]
                                               [0 1]]

                                              [[1 1]
                                               [1 1]]]


                                             [[[0 2]
                                               [0 2]]

                                              [[1 2]
                                               [1 2]]]]

Defined in src/operator/contrib/index_array.cc:L118

Parameters:	data (Symbol) – Input data axes (Shape or None, optional, default=None) – The axes to include in the index array. Supports negative values. name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	Symbol

mxnet.symbol.contrib.index_copy(old_tensor=None, index_vector=None, new_tensor=None, name=None, attr=None, out=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:L183

Parameters:	old_tensor (Symbol) – Old tensor index_vector (Symbol) – Index vector new_tensor (Symbol) – New tensor to be copied name (string, optional.) – Name of the resulting symbol.
Returns:	The result symbol.
Return type:	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.

\[f(x) = ax^2+bx+c\]

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

Defined in src/operator/contrib/quadratic_op.cc:L50

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.
Returns:	The result symbol.
Return type:	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 propagation. 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.
Returns:	The result symbol.
Return type:	Symbol