v]c4@sddlmZddlmZdledldldZdldleeeeedldld Zdldldldleeedldld ZdldldldleeeeeeeeeedldldZdldldleeeeeeeedldld Z dldldleeeeeeedldld Z dleeeeedldld Z dldldleeeeeedldld Z dldldleeeeeeeeedldld Z dldleeeedldld ZdldldleeeeeeeeedldldZdldleeeedldldZdldleeeedldldZdldldldldleeeeeeedldldZdledldldZdldldZdldldZdldldZdleeedldldZdldledldldZdldledldldZdleeeeeeeeeedldld Zdleeeeeeeeeedldld ZdldldleedldldZdldldldleeedldld ZdldldledldldZdldldldZdZ d Z!d!Z"d"Z#dldldld#Z$dldldldldld$Z%dledldld%Z&dledldld&Z'dledldld'Z(dldldleeeedldld( Z)dldldldldldldldldldld) Z*dledldld*Z+dledldld+Z,dldldldldld,Z-dleeedldld-Z.dldldledldld.Z/dleeedldld/Z0dldldledldld0Z1d1Z2dldldldldldldldldleeeeeeeeeeedldld2Z3dldldldldldldldld3Z4dldldldldld4Z5dldldldldldldldldleeedldld5Z6dldldleeeeeeeeeedldld6Z7dldldleeedldld7Z8d8d9d:d;d<d=d>d?d@dAdBdCdDdEdFdGdHdIdJdKdLdMdNdOdPdQdRdSdTdUdVdWdXdYdZd[d\d]d^d_d`dadbdcdddedfdgdhdidjdkg4Z9dlS(mi(t NDArrayBasei(t_NullcKsdS(s 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 : NDArray or list of NDArrays The output of this function. i(i((tdatat output_sizetouttnametkwargs((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytAdaptiveAvgPooling2Dsc KsdS(s 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 : NDArray Input data like : NDArray 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); out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i(( Rtliketheighttwidtht scale_heightt scale_widthtmodeRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytBilinearResize2D&s'c KsdS(s~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 : NDArray Input ndarray 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 : NDArray or list of NDArrays The output of this function. i(i(( Rtlabelt data_lengthst label_lengthstuse_data_lengthstuse_label_lengthst blank_labelRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytCTCLossOsKcKsdS(s 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 : 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, 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. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((Rtoffsettweighttbiastkerneltstridetdilatetpadt num_filtert num_grouptnum_deformable_groupt workspacetno_biastlayoutRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytDeformableConvolutionsWc KsdS(sPerforms 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 : NDArray or list of NDArrays The output of this function. i(i((Rtroisttranst spatial_scalet output_dimt group_sizet pooled_sizet part_sizetsample_per_partt trans_stdtno_transRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytDeformablePSROIPoolings%c KsdS(sVConvert 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=0.00999999978 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. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i(( tcls_probtloc_predtanchortclipt thresholdt background_idt nms_thresholdtforce_suppresst variancestnms_topkRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytMultiBoxDetections"c KsdS(sMGenerate 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 : NDArray or list of NDArrays The output of this function. i(i(( RtsizestratiosR2tstepstoffsetsRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pyt MultiBoxPrior@sc KsdS(s-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 : NDArray or list of NDArrays The output of this function. i(i(( R1Rtcls_predtoverlap_thresholdt ignore_labeltnegative_mining_ratiotnegative_mining_threshtminimum_negative_samplesR7RRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytMultiBoxTarget\s cKsdS(sGenerate region proposals via RPN Parameters ---------- cls_prob : NDArray Score of how likely proposal is object. bbox_pred : NDArray BBox Predicted deltas from anchors for proposals im_info : NDArray Image size and scale. rpn_pre_nms_top_n : int, optional, default='6000' Number of top scoring boxes to keep 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 out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((R/t bbox_predtim_infotrpn_pre_nms_top_ntrpn_post_nms_top_nR3t rpn_min_sizetscalesR;tfeature_stridet output_scoretiou_lossRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pyt MultiProposal~s&c KsdS(sPerforms 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 : NDArray or list of NDArrays The output of this function. i(i(( RR$R&R'R)R(RRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pyt PSROIPoolingscKsdS(sGenerate region proposals via RPN Parameters ---------- cls_prob : NDArray Score of how likely proposal is object. bbox_pred : NDArray BBox Predicted deltas from anchors for proposals im_info : NDArray Image size and scale. rpn_pre_nms_top_n : int, optional, default='6000' Number of top scoring boxes to keep 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 out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((R/RFRGRHRIR3RJRKR;RLRMRNRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytProposals&c KsdS(s 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 : NDArray Input data to the pooling operator, a 4D Feature maps rois : NDArray Bounding box coordinates, a 2D array pooled_size : Shape(tuple), required ROI Align output roi feature map height and width: (h, w) spatial_scale : float, required Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal of total stride in convolutional layers sample_ratio : int, optional, default='-1' Optional sampling ratio of ROI align, using adaptive size by default. position_sensitive : boolean, optional, default=0 Whether to perform 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 out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i(( RR$R)R&t sample_ratiotposition_sensitiveRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytROIAligns.c KsdS(s 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 : NDArray The input array to the embedding operator. weight : NDArray The embedding weight matrix. input_dim : int, required Vocabulary size of the input indices. output_dim : int, required Dimension of the embedding vectors. dtype : {'float16', 'float32', 'float64', 'int32', 'int64', 'int8', 'uint8'},optional, default='float32' Data type of weight. sparse_grad : boolean, optional, default=0 Compute row sparse gradient in the backward calculation. If set to True, the grad's storage type is row_sparse. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i(( RRt input_dimR'tdtypet sparse_gradRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytSparseEmbeddingsMcKsdS(s 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: .. math:: data\_mean[i] = mean(data[:,i,:,...]) \\ data\_var[i] = var(data[:,i,:,...]) Then compute the normalized output, which has the same shape as input, as following: .. math:: 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 : NDArray Input data to batch normalization gamma : NDArray gamma array beta : NDArray beta array moving_mean : NDArray running mean of input moving_var : NDArray running variance of input eps : float, optional, default=0.00100000005 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 :class:`gluon.nn.contrib.SyncBatchNorm`. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((Rtgammatbetat moving_meant moving_vartepstmomentumt fix_gammatuse_global_statstoutput_mean_vartndevtkeyRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pyt SyncBatchNormisXcKsdS(s$ Parameters ---------- data : NDArray source input scalar : float scalar input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((RtscalarRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytbackward_gradientmultiplierscKsdS(s Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((RRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytbackward_hawkesllscKsdS(s Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((RRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytbackward_index_copyscKsdS(s Parameters ---------- out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((RRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytbackward_quadraticscKsdS(sCompute 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 : 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 : NDArray or list of NDArrays The output of this function. i(i((Rt is_ascendR3ttopkRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytbipartite_matching s-cKsdS(sf 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 : NDArray Data index : NDArray Mask axis : int, optional, default='0' An integer that represents the axis in NDArray to mask from. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((RtindextaxisRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pyt boolean_mask9s#cKsdS(s9Bounding 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 : 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 : NDArray or list of NDArrays The output of this function. i(i((tlhstrhstformatRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytbox_iou^s'c KsdS(sApply 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 : NDArray The input overlap_thresh : float, optional, default=0.5 Overlapping(IoU) threshold to suppress object with smaller score. valid_thresh : float, optional, default=0 Filter input boxes to those whose scores greater than valid_thresh. topk : int, optional, default='-1' Apply nms to topk boxes with descending scores, -1 to no restriction. coord_start : int, optional, default='2' Start index of the consecutive 4 coordinates. score_index : int, optional, default='1' Index of the scores/confidence of boxes. id_index : int, optional, default='-1' Optional, index of the class categories, -1 to disable. 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]. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((Rtoverlap_thresht valid_threshRkt coord_startt score_indextid_indexR4R6t in_formatt out_formatRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytbox_nmss_c KsdS(sApply 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 : NDArray The input overlap_thresh : float, optional, default=0.5 Overlapping(IoU) threshold to suppress object with smaller score. valid_thresh : float, optional, default=0 Filter input boxes to those whose scores greater than valid_thresh. topk : int, optional, default='-1' Apply nms to topk boxes with descending scores, -1 to no restriction. coord_start : int, optional, default='2' Start index of the consecutive 4 coordinates. score_index : int, optional, default='1' Index of the scores/confidence of boxes. id_index : int, optional, default='-1' Optional, index of the class categories, -1 to disable. 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]. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((RRtRuRkRvRwRxR4R6RyRzRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytbox_non_maximum_suppressions_cKsdS(sLApply 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: .. math:: 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 : NDArray or list of NDArrays The output of this function. i(i((Rthtstout_dimtprocessing_batch_sizeRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pyt count_sketchIs/c KsdS(s~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 : NDArray Input ndarray 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 : NDArray or list of NDArrays The output of this function. i(i(( RRRRRRRRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytctc_losszsKcKsdS(sDequantize 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 : 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 : NDArray or list of NDArrays The output of this function. i(i((Rt min_ranget max_rangetout_typeRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pyt dequantizes'cKsdS(sThis 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: .. code:: python 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 : NDArray Input ndarray out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((RRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pyt dgl_adjacencys"cOsdS(s 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: .. code:: python 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] 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] out[3] [0 0 0 0 0] Defined in src/operator/contrib/dgl_graph.cc:L883 Parameters ---------- csr_matrix : NDArray csr matrix probability : NDArray probability vector seed_arrays : NDArray[] seed vertices num_hops : , optional, default=1 Number of hops. num_neighbor : , optional, default=2 Number of neighbor. max_num_vertices : , optional, default=100 Max number of vertices. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((t seed_arraysR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pyt#dgl_csr_neighbor_non_uniform_samplesHcOsdS(sThis 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: .. code:: python 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] 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] Defined in src/operator/contrib/dgl_graph.cc:L784 Parameters ---------- csr_matrix : NDArray csr matrix seed_arrays : NDArray[] seed vertices num_hops : , optional, default=1 Number of hops. num_neighbor : , optional, default=2 Number of neighbor. max_num_vertices : , optional, default=100 Max number of vertices. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((RR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytdgl_csr_neighbor_uniform_sample^sAcOsdS(sThis 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: .. code:: python 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 Parameters ---------- graph_data : NDArray[] Input graphs and input vertex Ids. return_mapping : boolean, required Return mapping of vid and eid between the subgraph and the parent graph. graph_sizes : tuple of <>, required the number of vertices in each graph. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((t graph_dataR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytdgl_graph_compacts4cOsdS(sThis 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: .. code:: python 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 Parameters ---------- graph : NDArray Input graph where we sample vertices. data : NDArray[] The input arrays that include data arrays and states. return_mapping : boolean, required Return mapping of vid and eid between the subgraph and the parent graph. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((RR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pyt dgl_subgraphs.cKsdS(sRescale 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 : NDArray The input array. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((RRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pyt div_sqrt_dimscKsdS(sDThis 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: .. code:: python 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 : NDArray Input ndarray u : NDArray u ndarray v : NDArray v ndarray out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((RtutvRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytedge_ids*cKsdS(sJApply 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 : NDArray or list of NDArrays The output of this function. i(i((Rt compute_sizeRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytfftKs cKsdS(s8Number 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 : NDArray 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. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((RRnRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytgetnnzmscKsdS(suThis 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 : NDArray The input array. scalar : float lambda multiplier out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((RReRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytgradientmultipliersc KsdS(sUpdate 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 : NDArray Weight grad : NDArray Gradient history : NDArray History lr : float, required Learning rate rescale_grad : float, optional, default=1 Rescale gradient to grad = rescale_grad*grad. clip_gradient : float, optional, default=-1 Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient). epsilon : float, optional, default=9.99999975e-06 Epsilon for numerical stability out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i(( Rtgradthistorytlrt rescale_gradt clip_gradienttepsilonRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytgroup_adagrad_updates/c KsdS(sComputes 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: .. math:: \lambda_k^*(t) = \lambda_k + \alpha_k \sum_{\{t_i < t, y_i = k\}} \beta_k \exp(-\beta_k (t - t_i)) where :math:`\lambda_k` specifies the background intensity ``lda``, :math:`\alpha_k` specifies the *branching ratio* or ``alpha``, and :math:`\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 :math:`(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 .. math:: s_k(t) = \sum_{t_i < t} \exp(-\beta_k (t - t_i)). The ``state`` to be provided is :math:`s_k(0)` and carries the added intensity due to past events before the current batch. :math:`s_k(T)` is returned from the function where :math:`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 : NDArray Shape (N, K) The intensity for each of the K processes, for each sample alpha : NDArray Shape (K,) The infectivity factor (branching ratio) for each process beta : NDArray Shape (K,) The decay parameter for each process state : NDArray Shape (N, K) the Hawkes state for each process lags : NDArray Shape (N, T) the interarrival times marks : NDArray Shape (N, T) the marks (process ids) valid_length : NDArray The number of valid points in the process max_time : NDArray the length of the interval where the processes were sampled out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i(( tldatalphaRZtstatetlagstmarkst valid_lengthtmax_timeRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pythawkesllsScKsdS(sApply 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 : NDArray or list of NDArrays The output of this function. i(i((RRRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytifft's!cKsdS(sReturns an array of indexes of the input array. For an input array with shape :math:`(d_1, d_2, ..., d_n)`, `index_array` returns a :math:`(d_1, d_2, ..., d_n, n)` array `idx`, where :math:`idx[i_1, i_2, ..., i_n, :] = [i_1, i_2, ..., i_n]`. Additionally, when the parameter `axes` is specified, `idx` will be a :math:`(d_1, d_2, ..., d_n, m)` array where `m` is the length of `axes`, and the following equality will hold: :math:`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 : NDArray Input data axes : Shape or None, optional, default=None The axes to include in the index array. Supports negative values. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((RtaxesRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pyt index_arrayJsAcKsdS(sPCopies 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.]] Defined in src/operator/contrib/index_copy.cc:L183 Parameters ---------- old_tensor : NDArray Old tensor index_vector : NDArray Index vector new_tensor : NDArray New tensor to be copied out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((t old_tensort index_vectort new_tensorRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pyt index_copys1cKsdS(s,This operators implements the quadratic function. .. math:: f(x) = ax^2+bx+c where :math:`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 : 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 : NDArray or list of NDArrays The output of this function. i(i((RtatbtcRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pyt quadratics*cKsdS(sQuantize 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 : 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 : NDArray or list of NDArrays The output of this function. i(i((RRRRRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytquantizes.cKsdS(sBQuantize a input tensor from float to `out_type`, with user-specified `min_calib_range` and `max_calib_range` or the input range collected at runtime. Output `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).` When out_type is `auto`, the output type is automatically determined by min_calib_range if presented. If min_calib_range < 0.0f, the output type will be int8, otherwise will be uint8. If min_calib_range isn't presented, the output type will be int8. .. Note:: This operator only supports forward propagation. DO NOT use it in training. Defined in src/operator/quantization/quantize_v2.cc:L92 Parameters ---------- data : NDArray A ndarray/symbol of type `float32` out_type : {'auto', 'int8', 'uint8'},optional, default='int8' Output data type. `auto` can be specified to automatically determine output type according to min_calib_range. min_calib_range : float or None, optional, default=None The minimum scalar value in the form of float32. If present, it will be used to quantize the fp32 data into int8 or uint8. max_calib_range : float or None, optional, default=None The maximum scalar value in the form of float32. If present, it will be used to quantize the fp32 data into int8 or uint8. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((RRtmin_calib_rangetmax_calib_rangeRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pyt quantize_v2s1cKsdS(s_Activation operator for input and output data type of int8. The input and output data comes with min and max thresholds for quantizing the float32 data into int8. .. Note:: This operator only supports forward propogation. DO NOT use it in training. This operator only supports `relu` Defined in src/operator/quantization/quantized_activation.cc:L91 Parameters ---------- data : NDArray Input data. min_data : NDArray Minimum value of data. max_data : NDArray Maximum value of data. act_type : {'relu', 'sigmoid', 'softrelu', 'softsign', 'tanh'}, required Activation function to be applied. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((Rtmin_datatmax_datatact_typeRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pyt quantized_actOscOsdS(sJoins input arrays along a given axis. The dimensions of the input arrays should be the same except the axis along which they will be concatenated. The dimension of the output array along the concatenated axis will be equal to the sum of the corresponding dimensions of the input arrays. All inputs with different min/max will be rescaled by using largest [min, max] pairs. If any input holds int8, then the output will be int8. Otherwise output will be uint8. Defined in src/operator/quantization/quantized_concat.cc:L108 Parameters ---------- data : NDArray[] List of arrays to concatenate dim : int, optional, default='1' the dimension to be concated. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((RR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytquantized_concatoscKsdS(sZ Convolution operator for input, weight and bias data type of int8, and accumulates in type int32 for the output. For each argument, two more arguments of type float32 must be provided representing the thresholds of quantizing argument from data type float32 to int8. The final outputs contain the convolution result in int32, and min and max thresholds representing the threholds for quantizing the float32 output into int32. .. Note:: This operator only supports forward propogation. DO NOT use it in training. Defined in src/operator/quantization/quantized_conv.cc:L137 Parameters ---------- data : NDArray Input data. weight : NDArray weight. bias : NDArray bias. min_data : NDArray Minimum value of data. max_data : NDArray Maximum value of data. min_weight : NDArray Minimum value of weight. max_weight : NDArray Maximum value of weight. min_bias : NDArray Minimum value of bias. max_bias : NDArray Maximum value of bias. kernel : Shape(tuple), required Convolution kernel size: (w,), (h, w) or (d, h, w) stride : Shape(tuple), optional, default=[] Convolution stride: (w,), (h, w) or (d, h, w). Defaults to 1 for each dimension. dilate : Shape(tuple), optional, default=[] Convolution dilate: (w,), (h, w) or (d, h, w). Defaults to 1 for each dimension. pad : Shape(tuple), optional, default=[] Zero pad for convolution: (w,), (h, w) or (d, h, w). Defaults to no padding. num_filter : int (non-negative), required Convolution filter(channel) number num_group : int (non-negative), optional, default=1 Number of group partitions. workspace : long (non-negative), optional, default=1024 Maximum temporary workspace allowed (MB) in convolution.This parameter has two usages. When CUDNN is not used, it determines the effective batch size of the convolution kernel. When CUDNN is used, it controls the maximum temporary storage used for tuning the best CUDNN kernel when `limited_workspace` strategy is used. no_bias : boolean, optional, default=0 Whether to disable bias parameter. cudnn_tune : {None, 'fastest', 'limited_workspace', 'off'},optional, default='None' Whether to pick convolution algo by running performance test. cudnn_off : boolean, optional, default=0 Turn off cudnn for this layer. layout : {None, 'NCDHW', 'NCHW', 'NCW', 'NDHWC', 'NHWC'},optional, default='None' Set layout for input, output and weight. Empty for default layout: NCW for 1d, NCHW for 2d and NCDHW for 3d.NHWC and NDHWC are only supported on GPU. out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i((RRRRRt min_weightt max_weighttmin_biastmax_biasRRRRRRR R!t cudnn_tunet cudnn_offR"RRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytquantized_convs@c KsdS(selemwise_add operator for input dataA and input dataB 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 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. Parameters ---------- lhs : NDArray first input rhs : NDArray second input lhs_min : NDArray 3rd input lhs_max : NDArray 4th input rhs_min : NDArray 5th input rhs_max : NDArray 6th input out : NDArray, optional The output NDArray to hold the result. Returns ------- out : NDArray or list of NDArrays The output of this function. i(i(( RpRqtlhs_mintlhs_maxtrhs_mintrhs_maxRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytquantized_elemwise_adds#cKsdS(s 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 : NDArray or list of NDArrays The output of this function. i(i((RRRRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytquantized_flattenscKsdS(sFully 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:L313 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 : NDArray or list of NDArrays The output of this function. i(i((RRRRRRRRRt num_hiddenR!tflattenRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytquantized_fully_connected s/cKsdS(sPooling operator for input and output data type of int8. The input and output data comes with min and max thresholds for quantizing the float32 data into int8. .. Note:: This operator only supports forward propogation. DO NOT use it in training. This operator only supports `pool_type` of `avg` or `max`. Defined in src/operator/quantization/quantized_pooling.cc:L145 Parameters ---------- data : NDArray Input data. min_data : NDArray Minimum value of data. max_data : NDArray Maximum value of data. kernel : Shape(tuple), optional, default=[] Pooling kernel size: (y, x) or (d, y, x) pool_type : {'avg', 'lp', 'max', 'sum'},optional, default='max' Pooling type to be applied. global_pool : boolean, optional, default=0 Ignore kernel size, do global pooling based on current input feature map. cudnn_off : boolean, optional, default=0 Turn off cudnn pooling and use MXNet pooling operator. pooling_convention : {'full', 'same', 'valid'},optional, default='valid' Pooling convention to be applied. stride : Shape(tuple), optional, default=[] Stride: for pooling (y, x) or (d, y, x). Defaults to 1 for each dimension. pad : Shape(tuple), optional, default=[] Pad for pooling: (y, x) or (d, y, x). Defaults to no padding. p_value : int or None, optional, default='None' Value of p for Lp pooling, can be 1 or 2, required for Lp Pooling. count_include_pad : boolean or None, optional, default=None Only used for AvgPool, specify whether to count padding elements for averagecalculation. For example, with a 5*5 kernel on a 3*3 corner of a image,the sum of the 9 valid elements will be divided by 25 if this is set to true,or it will be divided by 9 if this is set to false. Defaults to true. layout : {None, 'NCDHW', 'NCHW', 'NCW', 'NDHWC', 'NHWC', 'NWC'},optional, default='None' Set layout for input and output. 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 : NDArray or list of NDArrays The output of this function. i(i((RRRRt pool_typet global_poolRtpooling_conventionRRtp_valuetcount_include_padR"RRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytquantized_pooling< s1c KsdS(sGiven data that is quantized in int32 and the corresponding thresholds, requantize the data into int8 using min and max thresholds either calculated at runtime or from calibration. It's highly recommended to pre-calucate the min and max thresholds through calibration since it is able to save the runtime of the operator and improve the inference accuracy. .. Note:: This operator only supports forward propogation. DO NOT use it in training. Defined in src/operator/quantization/requantize.cc:L60 Parameters ---------- data : 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. out_type : {'auto', 'int8', 'uint8'},optional, default='int8' Output data type. `auto` can be specified to automatically determine output type according to min_calib_range. 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 : NDArray or list of NDArrays The output of this function. i(i(( RRRRRRRRR((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pyt requantizeo s#RRRR#R.R9R>RERORPRQRTRXRdRfRgRhRiRlRoRsR{R|RRRRRRRRRRRRRRRRRRRRRRRRRRRRRN(:t _internalRtbaseRtNoneRRRR#R.R9R>RERORPRQRTRXRdRfRgRhRiRlRoRsR{R|RRRRRRRRRRRRRRRRRRRRRRRRRRRRRt__all__(((s:/tmp/pip-install-Qvdv_2/mxnet/mxnet/ndarray/gen_contrib.pytsl $)$M9Y0'-$!*"3(!3(!0!O3Z/%)0a0a1$M)$ J C 6 0,"$1'U#C3,03 KB!%3163!%