Contrib NDArray API¶

Overview¶

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

mxnet.ndarray.contrib Contrib NDArray API of MXNet.

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

Warning

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

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

Contrib¶

`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
`count_sketch`	Apply CountSketch to input: map a d-dimension data to k-dimension data”
`ctc_loss`	Connectionist Temporal Classification Loss.
`dequantize`	Dequantize the input tensor into a float tensor.
`fft`	Apply 1D FFT to input”
`ifft`	Apply 1D ifft to input”
`quantize`	Quantize a input tensor from float to out_type, with user-specified min_range and max_range.

API Reference¶

Contrib NDArray API of MXNet.

mxnet.ndarray.contrib.rand_zipfian(true_classes, num_sampled, range_max, ctx=None)[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 (NDArray) – A 1-D NDArray of the target classes.
num_sampled (int) – The number of classes to randomly sample.
range_max (int) – The number of possible classes.
ctx (Context) – Device context of output. Default is current context. Overridden by mu.context when mu is an NDArray.

Returns:

samples (NDArray) – The sampled candidate classes in 1-D int64 dtype.
expected_count_true (NDArray) – The expected count for true classes in 1-D float64 dtype.
expected_count_sample (NDArray) – 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.ndarray.contrib.AdaptiveAvgPooling2D(data=None, output_size=_Null, out=None, name=None, **kwargs)¶

Applies a 2D adaptive average pooling over a 4D input with the shape of (NCHW). The pooling kernel and stride sizes are automatically chosen for desired output sizes.

If a single integer is provided for output_size, the output size is

(N x C x output_size x output_size) for any input (NCHW).

If a tuple of integers (height, width) are provided for output_size, the output size is

(N x C x height x width) for any input (NCHW).

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

Parameters:	data (NDArray) – Input data output_size (Shape(tuple), optional, default=[]) – int (output size) or a tuple of int for output (height, width). out (NDArray, optional) – The output NDArray to hold the result.
Returns:	out – The output of this function.
Return type:	NDArray or list of NDArrays

mxnet.ndarray.contrib.BilinearResize2D(data=None, height=_Null, width=_Null, out=None, name=None, **kwargs)¶

Perform 2D resizing (upsampling or downsampling) for 4D input using bilinear interpolation.

Expected input is a 4 dimensional NDArray (NCHW) and the output with the shape of (N x C x height x width). The key idea of bilinear interpolation is to perform linear interpolation first in one direction, and then again in the other direction. See the wikipedia of Bilinear interpolation for more details.

Defined in src/operator/contrib/bilinear_resize.cc:L175

Parameters:	data (NDArray) – Input data height (int, required) – output height (required) width (int, required) – output width (required) out (NDArray, optional) – The output NDArray to hold the result.
Returns:	out – The output of this function.
Return type:	NDArray or list of NDArrays

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

Connectionist Temporal Classification Loss.

The shapes of the inputs and outputs:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

For 2-D deformable convolution, the shapes are

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

Define:

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

then we have:

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

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

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

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

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

Both weight and bias are learnable parameters.

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

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

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

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

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

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

Convert multibox detection predictions.

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

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

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

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

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

Compute Multibox training targets

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

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

Generate region proposals via RPN

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

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

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

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

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

Generate region proposals via RPN

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

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

Maps integer indices to vector representations (embeddings).

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

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

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

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

Note

SparseEmbedding is designed for the use case where input_dim is very large (e.g. 100k). The operator is available on both CPU and GPU. 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. When the operator is used in recurrent neural network models 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:L301

Parameters:	data (NDArray) – The input array to the embedding operator. weight (NDArray) – The embedding weight matrix. input_dim (int, required) – Vocabulary size of the input indices. output_dim (int, required) – Dimension of the embedding vectors. dtype ({'float16', 'float32', 'float64', 'int32', 'int64', 'int8', 'uint8'},optional, default='float32') – Data type of weight. out (NDArray, optional) – The output NDArray to hold the result.
Returns:	out – The output of this function.
Return type:	NDArray or list of NDArrays

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

Parameters:	out (NDArray, optional) – The output NDArray to hold the result.
Returns:	out – The output of this function.
Return type:	NDArray or list of NDArrays

mxnet.ndarray.contrib.bipartite_matching(data=None, is_ascend=_Null, threshold=_Null, topk=_Null, out=None, name=None, **kwargs)¶

Compute bipartite matching.

The matching is performed on score matrix with shape [B, N, M] - B: batch_size - N: number of rows to match - M: number of columns as reference to be matched against.

Returns: x : matched column indices. -1 indicating 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 (NDArray) – The input is_ascend (boolean, optional, default=0) – Use ascend order for scores instead of descending. Please set threshold accordingly. threshold (float, required) – Ignore matching when score < thresh, if is_ascend=false, or ignore score > thresh, if is_ascend=true. topk (int, optional, default='-1') – Limit the number of matches to topk, set -1 for no limit out (NDArray, optional) – The output NDArray to hold the result.
Returns:	out – The output of this function.
Return type:	NDArray or list of NDArrays

mxnet.ndarray.contrib.box_iou(lhs=None, rhs=None, format=_Null, out=None, name=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 (NDArray) – The first input rhs (NDArray) – The second input format ({'center', 'corner'},optional, default='corner') – The box encoding type. “corner” means boxes are encoded as [xmin, ymin, xmax, ymax], “center” means boxes are encodes as [x, y, width, height]. out (NDArray, optional) – The output NDArray to hold the result.
Returns:	out – The output of this function.
Return type:	NDArray or list of NDArrays

mxnet.ndarray.contrib.box_nms(data=None, overlap_thresh=_Null, topk=_Null, coord_start=_Null, score_index=_Null, id_index=_Null, force_suppress=_Null, in_format=_Null, out_format=_Null, out=None, name=None, **kwargs)¶

Apply 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 (NDArray) – The input overlap_thresh (float, optional, default=0.5) – Overlapping(IoU) threshold to suppress object with smaller score. topk (int, optional, default='-1') – Apply nms to topk boxes with descending scores, -1 to no restriction. coord_start (int, optional, default='2') – Start index of the consecutive 4 coordinates. score_index (int, optional, default='1') – Index of the scores/confidence of boxes. id_index (int, optional, default='-1') – Optional, index of the class categories, -1 to disable. force_suppress (boolean, optional, default=0) – Optional, if set false and id_index is provided, nms will only apply to boxes belongs to the same category in_format ({'center', 'corner'},optional, default='corner') – The input box encoding type. “corner” means boxes are encoded as [xmin, ymin, xmax, ymax], “center” means boxes are encodes as [x, y, width, height]. out_format ({'center', 'corner'},optional, default='corner') – The output box encoding type. “corner” means boxes are encoded as [xmin, ymin, xmax, ymax], “center” means boxes are encodes as [x, y, width, height]. out (NDArray, optional) – The output NDArray to hold the result.
Returns:	out – The output of this function.
Return type:	NDArray or list of NDArrays

mxnet.ndarray.contrib.box_non_maximum_suppression(data=None, overlap_thresh=_Null, topk=_Null, coord_start=_Null, score_index=_Null, id_index=_Null, force_suppress=_Null, in_format=_Null, out_format=_Null, out=None, name=None, **kwargs)¶

Apply 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 (NDArray) – The input overlap_thresh (float, optional, default=0.5) – Overlapping(IoU) threshold to suppress object with smaller score. topk (int, optional, default='-1') – Apply nms to topk boxes with descending scores, -1 to no restriction. coord_start (int, optional, default='2') – Start index of the consecutive 4 coordinates. score_index (int, optional, default='1') – Index of the scores/confidence of boxes. id_index (int, optional, default='-1') – Optional, index of the class categories, -1 to disable. force_suppress (boolean, optional, default=0) – Optional, if set false and id_index is provided, nms will only apply to boxes belongs to the same category in_format ({'center', 'corner'},optional, default='corner') – The input box encoding type. “corner” means boxes are encoded as [xmin, ymin, xmax, ymax], “center” means boxes are encodes as [x, y, width, height]. out_format ({'center', 'corner'},optional, default='corner') – The output box encoding type. “corner” means boxes are encoded as [xmin, ymin, xmax, ymax], “center” means boxes are encodes as [x, y, width, height]. out (NDArray, optional) – The output NDArray to hold the result.
Returns:	out – The output of this function.
Return type:	NDArray or list of NDArrays

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

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

Note

count_sketch is only available on GPU.

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

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

Example:

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

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

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

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

Connectionist Temporal Classification Loss.

The shapes of the inputs and outputs:

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

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

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

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

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

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

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

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

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

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

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

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

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

Dequantize the input tensor into a float tensor. min_range and max_range are scalar floats that specify the range for the output data.

When input data type is uint8, the output is calculated using the following equation:

out[i] = in[i] * (max_range - min_range) / 255.0,

When input data type is int8, the output is calculate using the following equation by keep zero centered for the quantized value:

out[i] = in[i] * MaxAbs(min_range, max_range) / 127.0,

Note

This operator only supports forward propogation. DO NOT use it in training.

Defined in src/operator/quantization/dequantize.cc:L47

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

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

Apply 1D FFT to input”

Note

fft is only available on GPU.

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

Example:

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

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

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

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

Apply 1D ifft to input”

Note

ifft is only available on GPU.

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

Example:

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

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

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

mxnet.ndarray.contrib.quadratic(data=None, a=_Null, b=_Null, c=_Null, out=None, name=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

Defined in src/operator/contrib/quadratic_op.cc:L46

Parameters:	data (NDArray) – Input ndarray a (float, optional, default=0) – Coefficient of the quadratic term in the quadratic function. b (float, optional, default=0) – Coefficient of the linear term in the quadratic function. c (float, optional, default=0) – Constant term in the quadratic function. out (NDArray, optional) – The output NDArray to hold the result.
Returns:	out – The output of this function.
Return type:	NDArray or list of NDArrays

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

Quantize a input tensor from float to out_type, with 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:L54

Parameters:	data (NDArray) – A ndarray/symbol of type float32 min_range (NDArray) – The minimum scalar value possibly produced for the input max_range (NDArray) – The maximum scalar value possibly produced for the input out_type ({'int8', 'uint8'},optional, default='uint8') – Output data type. out (NDArray, optional) – The output NDArray to hold the result.
Returns:	out – The output of this function.
Return type:	NDArray or list of NDArrays

mxnet.ndarray.contrib.quantized_conv(data=None, weight=None, bias=None, min_data=None, max_data=None, min_weight=None, max_weight=None, min_bias=None, max_bias=None, kernel=_Null, stride=_Null, dilate=_Null, pad=_Null, num_filter=_Null, num_group=_Null, workspace=_Null, no_bias=_Null, cudnn_tune=_Null, cudnn_off=_Null, layout=_Null, out=None, name=None, **kwargs)¶

Convolution operator for input, weight and bias data type of int8, and accumulates in type int32 for the output. For each argument, two more arguments of type float32 must be provided representing the thresholds of quantizing argument from data type float32 to int8. The final outputs contain the convolution result in int32, and min and max thresholds representing the threholds for quantizing the float32 output into int32.

Note

This operator only supports forward propogation. DO NOT use it in training.

Defined in src/operator/quantization/quantized_conv.cc:L115

Parameters:	data (NDArray) – Input data. weight (NDArray) – weight. bias (NDArray) – bias. min_data (NDArray) – Minimum value of data. max_data (NDArray) – Maximum value of data. min_weight (NDArray) – Minimum value of weight. max_weight (NDArray) – Maximum value of weight. min_bias (NDArray) – Minimum value of bias. max_bias (NDArray) – Maximum value of bias. kernel (Shape(tuple), required) – Convolution kernel size: (w,), (h, w) or (d, h, w) stride (Shape(tuple), optional, default=[]) – Convolution stride: (w,), (h, w) or (d, h, w). Defaults to 1 for each dimension. dilate (Shape(tuple), optional, default=[]) – Convolution dilate: (w,), (h, w) or (d, h, w). Defaults to 1 for each dimension. pad (Shape(tuple), optional, default=[]) – Zero pad for convolution: (w,), (h, w) or (d, h, w). Defaults to no padding. num_filter (int (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. out (NDArray, optional) – The output NDArray to hold the result.
Returns:	out – The output of this function.
Return type:	NDArray or list of NDArrays

mxnet.ndarray.contrib.quantized_flatten(data=None, min_data=None, max_data=None, out=None, name=None, **kwargs)¶

Parameters:	data (NDArray) – A ndarray/symbol of type float32 min_data (NDArray) – The minimum scalar value possibly produced for the data max_data (NDArray) – The maximum scalar value possibly produced for the data out (NDArray, optional) – The output NDArray to hold the result.
Returns:	out – The output of this function.
Return type:	NDArray or list of NDArrays

mxnet.ndarray.contrib.quantized_fully_connected(data=None, weight=None, bias=None, min_data=None, max_data=None, min_weight=None, max_weight=None, min_bias=None, max_bias=None, num_hidden=_Null, no_bias=_Null, flatten=_Null, out=None, name=None, **kwargs)¶

Fully Connected operator for input, weight and bias data type of int8, and accumulates in type int32 for the output. For each argument, two more arguments of type float32 must be provided representing the thresholds of quantizing argument from data type float32 to int8. The final outputs contain the convolution result in int32, and min and max thresholds representing the threholds for quantizing the float32 output into int32.

Note

This operator only supports forward propogation. DO NOT use it in training.

Defined in src/operator/quantization/quantized_fully_connected.cc:L90

Parameters:	data (NDArray) – Input data. weight (NDArray) – weight. bias (NDArray) – bias. min_data (NDArray) – Minimum value of data. max_data (NDArray) – Maximum value of data. min_weight (NDArray) – Minimum value of weight. max_weight (NDArray) – Maximum value of weight. min_bias (NDArray) – Minimum value of bias. max_bias (NDArray) – Maximum value of bias. num_hidden (int, required) – Number of hidden nodes of the output. no_bias (boolean, optional, default=0) – Whether to disable bias parameter. flatten (boolean, optional, default=1) – Whether to collapse all but the first axis of the input data tensor. out (NDArray, optional) – The output NDArray to hold the result.
Returns:	out – The output of this function.
Return type:	NDArray or list of NDArrays

mxnet.ndarray.contrib.quantized_pooling(data=None, min_data=None, max_data=None, kernel=_Null, pool_type=_Null, global_pool=_Null, cudnn_off=_Null, pooling_convention=_Null, stride=_Null, pad=_Null, out=None, name=None, **kwargs)¶

Parameters:	data (NDArray) – Input data. min_data (NDArray) – Minimum value of data. max_data (NDArray) – Maximum value of data. kernel (Shape(tuple), optional, default=[]) – Pooling kernel size: (y, x) or (d, y, x) pool_type ({'avg', '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. out (NDArray, optional) – The output NDArray to hold the result.
Returns:	out – The output of this function.
Return type:	NDArray or list of NDArrays

mxnet.ndarray.contrib.requantize(data=None, min_range=None, max_range=None, min_calib_range=_Null, max_calib_range=_Null, out=None, name=None, **kwargs)¶

Given data that is quantized in int32 and the corresponding thresholds, requantize the data into int8 using min and max thresholds either calculated at runtime or from calibration. It’s highly recommended to 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:L40

Parameters:	data (NDArray) – A ndarray/symbol of type int32 min_range (NDArray) – The original minimum scalar value in the form of float32 used for quantizing data into int32. max_range (NDArray) – The original maximum scalar value in the form of float32 used for quantizing data into int32. min_calib_range (float or None, optional, default=None) – The minimum scalar value in the form of float32 obtained through calibration. If present, it will be used to requantize the int32 data into int8. max_calib_range (float or None, optional, default=None) – The maximum scalar value in the form of float32 obtained through calibration. If present, it will be used to requantize the int32 data into int8. out (NDArray, optional) – The output NDArray to hold the result.
Returns:	out – The output of this function.
Return type:	NDArray or list of NDArrays