# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve. # #Licensed under the Apache License, Version 2.0 (the "License"); #you may not use this file except in compliance with the License. #You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #Unless required by applicable law or agreed to in writing, software #distributed under the License is distributed on an "AS IS" BASIS, #WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #See the License for the specific language governing permissions and #limitations under the License. """ All layers just related to the neural network. """ from ..layer_helper import LayerHelper from ..initializer import Normal, Constant from ..framework import Variable from ..param_attr import ParamAttr from tensor import concat __all__ = [ 'fc', 'embedding', 'dynamic_lstm', 'gru_unit', 'linear_chain_crf', 'crf_decoding', 'cos_sim', 'cross_entropy', 'square_error_cost', 'accuracy', 'chunk_eval', 'sequence_conv', 'conv2d', 'sequence_pool', 'pool2d', 'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'sequence_expand', 'lstm_unit', 'reduce_sum', 'reduce_mean', 'reduce_max', 'reduce_min', 'sequence_first_step', 'sequence_last_step', 'dropout', 'split', 'greedy_ctc_error', ] def fc(input, size, num_flatten_dims=1, param_attr=None, bias_attr=None, act=None, name=None): """ **Fully Connected Layer** The fully connected layer can take multiple tensors as its inputs. It creates a variable (one for each input tensor) called weights for each input tensor, which represents a fully connected weight matrix from each input unit to each output unit. The fully connected layer multiplies each input tensor with its coresponding weight to produce an output Tensor. If multiple input tensors are given, the results of multiple multiplications will be sumed up. If bias_attr is not None, a biases variable will be created and added to the output. Finally, if activation is not None, it will be applied to the output as well. This process can be formulated as follows: .. math:: Out = Act({\sum_{i=0}^{N-1}W_iX_i + b}) In the above equation: * :math:`N`: Number of the input. * :math:`X_i`: The input tensor. * :math:`W`: The weights created by this layer. * :math:`b`: The bias parameter created by this layer (if needed). * :math:`Act`: The activation funtion. * :math:`Out`: The output tensor. Args: input(Variable|list): The input tensor(s) to the fully connected layer. size(int): The number of output units in the fully connected layer. num_flatten_dims(int): The fc layer can accept an input tensor with more than two dimensions. If this happens, the multidimensional tensor will first be flattened into a 2-dimensional matrix. The parameter `num_flatten_dims` determines how the input tensor is flattened: the first `num_flatten_dims` dimensions will be flatten to form the first dimension of the final matrix (height of the matrix), and the rest `rank(X) - num_flatten_dims` dimensions are flattened to form the second dimension of the final matrix (width of the matrix). For example, suppose `X` is a 6-dimensional tensor with a shape [2, 3, 4, 5, 6], and `num_flatten_dims` = 3. Then, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30]. By default, `num_flatten_dims` is set to 1. param_attr(ParamAttr|list): The parameter attribute for learnable parameters/weights of the fully connected layer. param_initializer(ParamAttr|list): The initializer used for the weight/parameter. If set None, XavierInitializer() will be used. bias_attr(ParamAttr|list): The parameter attribute for the bias parameter for this layer. If set None, no bias will be added to the output units. bias_initializer(ParamAttr|list): The initializer used for the bias. If set None, then ConstantInitializer() will be used. act(str): Activation to be applied to the output of the fully connected layer. name(str): Name/alias of the fully connected layer. Returns: Variable: The output tensor variable. Raises: ValueError: If rank of the input tensor is less than 2. Examples: .. code-block:: python data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32") fc = fluid.layers.fc(input=data, size=1000, act="tanh") """ helper = LayerHelper("fc", **locals()) dtype = helper.input_dtype() mul_results = [] for input_var, param_attr in helper.iter_inputs_and_params(): input_shape = input_var.shape param_shape = [ reduce(lambda a, b: a * b, input_shape[num_flatten_dims:], 1) ] + [size] w = helper.create_parameter( attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False) tmp = helper.create_tmp_variable(dtype) helper.append_op( type="mul", inputs={ "X": input_var, "Y": w, }, outputs={"Out": tmp}, attrs={"x_num_col_dims": num_flatten_dims, "y_num_col_dims": 1}) mul_results.append(tmp) # sum if len(mul_results) == 1: pre_bias = mul_results[0] else: pre_bias = helper.create_tmp_variable(dtype) helper.append_op( type="sum", inputs={"X": mul_results}, outputs={"Out": pre_bias}) # add bias pre_activation = helper.append_bias_op(pre_bias) # add activation return helper.append_activation(pre_activation) def embedding(input, size, is_sparse=False, param_attr=None, dtype='float32'): """ **Embedding Layer** This layer is used to lookup a vector of IDs, provided by *input*, in a lookup table. The result of this lookup is the embedding of each ID in the *input*. All the input variables are passed in as local variables to the LayerHelper constructor. Args: input(Variable): Input to the function size(tuple|list|None): Shape of the look up table parameter is_sparse(bool): Boolean flag that specifying whether the input is sparse param_attr(ParamAttr): Parameters for this layer dtype(np.dtype|core.DataType|str): The type of data : float32, float_16, int etc Returns: Variable: The tensor variable storing the embeddings of the \ supplied inputs. Examples: .. code-block:: python dict_size = len(dataset.ids) data = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32') fc = fluid.layers.embedding(input=data, size=[dict_size, 16]) """ helper = LayerHelper('embedding', **locals()) w = helper.create_parameter( attr=helper.param_attr, shape=size, dtype=dtype, is_bias=False) tmp = helper.create_tmp_variable(dtype) helper.append_op( type='lookup_table', inputs={'Ids': input, 'W': w}, outputs={'Out': tmp}, attrs={'is_sparse': is_sparse}) return tmp # TODO(qijun): expose H0 and C0 def dynamic_lstm(input, size, param_attr=None, bias_attr=None, use_peepholes=True, is_reverse=False, gate_activation='sigmoid', cell_activation='tanh', candidate_activation='tanh', dtype='float32'): helper = LayerHelper('lstm', **locals()) size = size / 4 weight = helper.create_parameter( attr=helper.param_attr, shape=[size, 4 * size], dtype=dtype) bias_size = [1, 7 * size] if not use_peepholes: bias_size[1] = 4 * size bias = helper.create_parameter( attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True) hidden = helper.create_tmp_variable(dtype) cell = helper.create_tmp_variable(dtype) batch_gate = helper.create_tmp_variable(dtype) batch_cell_pre_act = helper.create_tmp_variable(dtype) helper.append_op( type='lstm', inputs={'Input': input, 'Weight': weight, 'Bias': bias}, outputs={ 'Hidden': hidden, 'Cell': cell, 'BatchGate': batch_gate, 'BatchCellPreAct': batch_cell_pre_act }, attrs={ 'use_peepholes': use_peepholes, 'is_reverse': is_reverse, 'gate_activation': gate_activation, 'cell_activation': cell_activation, 'candidate_activation': candidate_activation }) return hidden, cell def gru_unit(input, hidden, size, weight=None, bias=None, activation='tanh', gate_activation='sigmoid'): """ GRU unit layer. The equation of a gru step is: .. math:: u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u) r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r) m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m) h_t & = dot((1-u_t), m_t) + dot(u_t, h_{t-1}) The inputs of gru unit includes :math:`z_t`, :math:`h_{t-1}`. In terms of the equation above, the :math:`z_t` is split into 3 parts - :math:`xu_t`, :math:`xr_t` and :math:`xm_t`. This means that in order to implement a full GRU unit operator for an input, a fully connected layer has to be applied, such that :math:`z_t = W_{fc}x_t`. The terms :math:`u_t` and :math:`r_t` represent the update and reset gates of the GRU cell. Unlike LSTM, GRU has one lesser gate. However, there is an intermediate candidate hidden output, which is denoted by :math:`m_t`. This layer has three outputs :math:`h_t`, :math:`dot(r_t, h_{t-1})` and concatenation of :math:`u_t`, :math:`r_t` and :math:`m_t`. Args: input (Variable): The fc transformed input value of current step. hidden (Variable): The hidden value of lstm unit from previous step. size (integer): The input dimension value. weight (ParamAttr): The weight parameters for gru unit. Default: None bias (ParamAttr): The bias parameters for gru unit. Default: None activation (string): The activation type for cell (actNode). Default: 'tanh' gate_activation (string): The activation type for gates (actGate). Default: 'sigmoid' Returns: tuple: The hidden value, reset-hidden value and gate values. Examples: .. code-block:: python # assuming we have x_t_data and prev_hidden of size=10 x_t = fluid.layers.fc(input=x_t_data, size=30) hidden_val, r_h_val, gate_val = fluid.layers.gru_unit(input=x_t, hidden = prev_hidden) """ activation_dict = dict( identity=0, sigmoid=1, tanh=2, relu=3, ) activation = activation_dict[activation] gate_activation = activation_dict[gate_activation] helper = LayerHelper('gru_unit', **locals()) dtype = helper.input_dtype() size = size / 3 # create weight if weight is None: weight = helper.create_parameter( attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype) # create bias if bias is None: bias_size = [1, 3 * size] bias = helper.create_parameter( attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True) gate = helper.create_tmp_variable(dtype) reset_hidden_pre = helper.create_tmp_variable(dtype) updated_hidden = helper.create_tmp_variable(dtype) helper.append_op( type='gru_unit', inputs={'Input': input, 'HiddenPrev': hidden, 'Weight': weight}, outputs={ 'Gate': gate, 'ResetHiddenPrev': reset_hidden_pre, 'Hidden': updated_hidden, }, attrs={ 'activation': 0, 'gate_activation': 1, }) return updated_hidden, reset_hidden_pre, gate def linear_chain_crf(input, label, param_attr=None): helper = LayerHelper('linear_chain_crf', **locals()) size = input.shape[1] transition = helper.create_parameter( attr=helper.param_attr, shape=[size + 2, size], dtype=helper.input_dtype()) alpha = helper.create_tmp_variable(dtype=helper.input_dtype()) emission_exps = helper.create_tmp_variable(dtype=helper.input_dtype()) transition_exps = helper.create_tmp_variable(dtype=helper.input_dtype()) log_likelihood = helper.create_tmp_variable(dtype=helper.input_dtype()) helper.append_op( type='linear_chain_crf', inputs={"Emission": [input], "Transition": transition, "Label": label}, outputs={ "Alpha": [alpha], "EmissionExps": [emission_exps], "TransitionExps": transition_exps, "LogLikelihood": log_likelihood }) return log_likelihood def crf_decoding(input, param_attr, label=None): helper = LayerHelper('crf_decoding', **locals()) transition = helper.get_parameter(param_attr.name) viterbi_path = helper.create_tmp_variable(dtype=helper.input_dtype()) helper.append_op( type='crf_decoding', inputs={"Emission": [input], "Transition": transition, "Label": label}, outputs={"ViterbiPath": [viterbi_path]}) return viterbi_path def cos_sim(X, Y, **kwargs): """ This function performs the cosine similarity between two tensors X and Y and returns that as the output. """ helper = LayerHelper('cos_sim', **kwargs) out = helper.create_tmp_variable(dtype=X.dtype) xnorm = helper.create_tmp_variable(dtype=X.dtype) ynorm = helper.create_tmp_variable(dtype=X.dtype) helper.append_op( type='cos_sim', inputs={'X': [X], 'Y': [Y]}, outputs={'Out': [out], 'XNorm': [xnorm], 'YNorm': [ynorm]}) return out def dropout(x, dropout_prob, is_test=False, seed=0, **kwargs): helper = LayerHelper('dropout', **kwargs) out = helper.create_tmp_variable(dtype=x.dtype) mask = helper.create_tmp_variable(dtype=x.dtype, stop_gradient=True) helper.append_op( type='dropout', inputs={'X': [x]}, outputs={'Out': [out], 'Mask': [mask]}, attrs={'dropout_prob': dropout_prob, 'is_test': is_test, 'seed': seed}) return out def cross_entropy(input, label, **kwargs): """ **Cross Entropy Layer** This layer computes the cross entropy between `input` and `label`. It supports both standard cross-entropy and soft-label cross-entropy loss computation. 1) One-hot cross-entropy: `soft_label = False`, `Label[i, 0]` indicates the class index for sample i: .. math:: Y[i] = -\log(X[i, Label[i]]) 2) Soft-label cross-entropy: `soft_label = True`, `Label[i, j]` indicates the soft label of class j for sample i: .. math:: Y[i] = \sum_j{-Label[i, j] * log(X[i, j])} Please make sure that in this case the summation of each row of `label` equals one. 3) One-hot cross-entropy with vecterized `label`: As a special case of 2), when each row of 'label' has only one non-zero element which is equal to 1, soft-label cross-entropy degenerates to a one-hot cross-entropy with one-hot label representation. Args: input (Variable|list): a 2-D tensor with shape [N x D], where N is the batch size and D is the number of classes. This input is a probability computed by the previous operator, which is almost always the result of a softmax operator. label (Variable|list): the ground truth which is a 2-D tensor. When `soft_label` is set to `False`, `label` is a tensor with shape [N x 1]. When `soft_label` is set to `True`, `label` is a tensor with shape [N x D]. soft_label (bool, via `**kwargs`): a flag indicating whether to interpretate the given labels as soft labels, default `False`. Returns: A 2-D tensor with shape [N x 1], the cross entropy loss. Raises: `ValueError`: 1) the 1st dimension of `input` and `label` are not equal; 2) when \ `soft_label == True`, and the 2nd dimension of `input` and `label` are not \ equal; 3) when `soft_label == False`, and the 2nd dimension of `label` is not 1. Examples: .. code-block:: python predict = fluid.layers.fc(input=net, size=classdim, act='softmax') cost = fluid.layers.cross_entropy(input=predict, label=label) """ helper = LayerHelper('cross_entropy', **kwargs) out = helper.create_tmp_variable(dtype=input.dtype) helper.append_op( type='cross_entropy', inputs={'X': [input], 'Label': [label]}, outputs={'Y': [out]}, attrs=kwargs) return out def square_error_cost(input, label, **kwargs): """ **Square error cost layer** This layer accepts input predictions and target label and returns the squared error cost. For predictions, :math:`X`, and target labels, :math:`Y`, the equation is: .. math:: Out = (X - Y)^2 In the above equation: * :math:`X`: Input predictions, a tensor. * :math:`Y`: Input labels, a tensor. * :math:`Out`: Output value, same shape with :math:`X`. Args: input(Variable): Input tensor, has predictions. label(Variable): Label tensor, has target labels. Returns: Variable: The tensor variable storing the element-wise squared error difference \ of input and label. Examples: .. code-block:: python y = layers.data(name='y', shape=[1], dtype='float32') y_predict = layers.data(name='y_predict', shape=[1], dtype='float32') cost = layers.square_error_cost(input=y_predict, label=y) """ helper = LayerHelper('square_error_cost', **kwargs) minus_out = helper.create_tmp_variable(dtype=input.dtype) helper.append_op( type='elementwise_sub', inputs={'X': [input], 'Y': [label]}, outputs={'Out': [minus_out]}) square_out = helper.create_tmp_variable(dtype=input.dtype) helper.append_op( type='square', inputs={'X': [minus_out]}, outputs={'Out': [square_out]}) return square_out def accuracy(input, label, k=1, correct=None, total=None, **kwargs): """ This function computes the accuracy using the input and label. The output is the top_k inputs and their indices. """ helper = LayerHelper("accuracy", **kwargs) topk_out = helper.create_tmp_variable(dtype=input.dtype) topk_indices = helper.create_tmp_variable(dtype="int64") helper.append_op( type="top_k", inputs={"X": [input]}, outputs={"Out": [topk_out], "Indices": [topk_indices]}, attrs={"k": k}) acc_out = helper.create_tmp_variable(dtype="float32") if correct is None: correct = helper.create_tmp_variable(dtype="int64") if total is None: total = helper.create_tmp_variable(dtype="int64") helper.append_op( type="accuracy", inputs={ "Out": [topk_out], "Indices": [topk_indices], "Label": [label] }, outputs={ "Accuracy": [acc_out], "Correct": [correct], "Total": [total], }) return acc_out def chunk_eval(input, label, chunk_scheme, num_chunk_types, excluded_chunk_types=None, **kwargs): """ This function computes and outputs the precision, recall and F1-score of chunk detection. """ helper = LayerHelper("chunk_eval", **kwargs) # prepare output precision = helper.create_tmp_variable(dtype="float32") recall = helper.create_tmp_variable(dtype="float32") f1_score = helper.create_tmp_variable(dtype="float32") num_infer_chunks = helper.create_tmp_variable(dtype="int64") num_label_chunks = helper.create_tmp_variable(dtype="int64") num_correct_chunks = helper.create_tmp_variable(dtype="int64") helper.append_op( type="chunk_eval", inputs={"Inference": [input], "Label": [label]}, outputs={ "Precision": [precision], "Recall": [recall], "F1-Score": [f1_score], "NumInferChunks": [num_infer_chunks], "NumLabelChunks": [num_label_chunks], "NumCorrectChunks": [num_correct_chunks] }, attrs={ "num_chunk_types": num_chunk_types, "chunk_scheme": chunk_scheme, "excluded_chunk_types": excluded_chunk_types or [] }) return precision, recall, f1_score, num_infer_chunks, num_label_chunks, num_correct_chunks def sequence_conv(input, num_filters, filter_size=3, filter_stride=1, padding=None, bias_attr=None, param_attr=None, act=None): """ This function creates the op for sequence_conv, using the inputs and other convolutional configurations for the filters and stride as given in the input parameters to the function. """ # FIXME(dzh) : want to unify the argument of python layer # function. So we ignore some unecessary attributes. # such as, padding_trainable, context_start. helper = LayerHelper('sequence_conv', **locals()) dtype = helper.input_dtype() filter_shape = [filter_size * input.shape[1], num_filters] filter_param = helper.create_parameter( attr=helper.param_attr, shape=filter_shape, dtype=dtype) pre_bias = helper.create_tmp_variable(dtype) helper.append_op( type='sequence_conv', inputs={ 'X': [input], 'Filter': [filter_param], }, outputs={"Out": pre_bias}, attrs={ 'contextStride': filter_stride, 'contextStart': -int(filter_size / 2), 'contextLength': filter_size }) pre_act = helper.append_bias_op(pre_bias) return helper.append_activation(pre_act) def conv2d(input, num_filters, filter_size, stride=None, padding=None, groups=None, param_attr=None, bias_attr=None, act=None): """ **Convlution2D Layer** The convolution2D layer calculates the output based on the input, filter and strides, paddings, dilations, groups parameters. Input(Input) and Output(Output) are in NCHW format. Where N is batch size, C is the number of channels, H is the height of the feature, and W is the width of the feature. The details of convolution layer, please refer UFLDL's `convolution, `_ . If bias attribution and activation type are provided, bias is added to the output of the convolution, and the corresponding activation function is applied to the final result. For each input :math:`X`, the equation is: .. math:: Out = \sigma (W \\ast X + b) In the above equation: * :math:`X`: Input value, a tensor with NCHW format. * :math:`W`: Filter value, a tensor with MCHW format. * :math:`\\ast`: Convolution operation. * :math:`b`: Bias value, a 2-D tensor with shape [M, 1]. * :math:`\\sigma`: Activation function. * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different. Example: Input: Input shape: $(N, C_{in}, H_{in}, W_{in})$ Filter shape: $(C_{out}, C_{in}, H_f, W_f)$ Output: Output shape: $(N, C_{out}, H_{out}, W_{out})$ Where .. math:: H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\ W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1 Args: input(Variable): The input image with [N, C, H, W] format. num_filters(int): The number of filter. It is as same as the output image channel. filter_size(int|tuple|None): The filter size. If filter_size is a tuple, it must contain two integers, (filter_size_H, filter_size_W). Otherwise, the filter will be a square. stride(int|tuple): The stride size. If stride is a tuple, it must contain two integers, (stride_H, stride_W). Otherwise, the stride_H = stride_W = stride. Default: stride = 1. padding(int|tuple): The padding size. If padding is a tuple, it must contain two integers, (padding_H, padding_W). Otherwise, the padding_H = padding_W = padding. Default: padding = 0. groups(int): The groups number of the Conv2d Layer. According to grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only connected to the second half of the input channels. Default: groups=1 param_attr(ParamAttr): The parameters to the Conv2d Layer. Default: None bias_attr(ParamAttr): Bias parameter for the Conv2d layer. Default: None act(str): Activation type. Default: None Returns: Variable: The tensor variable storing the convolution and \ non-linearity activation result. Raises: ValueError: If the shapes of input, filter_size, stride, padding and groups mismatch. Examples: .. code-block:: python data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32') conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu") """ if stride is None: stride = [1, 1] helper = LayerHelper('conv2d', **locals()) dtype = helper.input_dtype() num_channels = input.shape[1] if groups is None: num_filter_channels = num_channels else: if num_channels % groups != 0: raise ValueError("num_channels must be divisible by groups.") num_filter_channels = num_channels / groups if isinstance(filter_size, int): filter_size = [filter_size, filter_size] if isinstance(stride, int): stride = [stride, stride] if isinstance(padding, int): padding = [padding, padding] input_shape = input.shape filter_shape = [num_filters, num_filter_channels] + filter_size def _get_default_param_initializer(): std = (2.0 / (filter_size[0]**2 * num_channels))**0.5 return Normal(0.0, std, 0) filter_param = helper.create_parameter( attr=helper.param_attr, shape=filter_shape, dtype=dtype, default_initializer=_get_default_param_initializer()) pre_bias = helper.create_tmp_variable(dtype) helper.append_op( type='conv2d', inputs={ 'Input': input, 'Filter': filter_param, }, outputs={"Output": pre_bias}, attrs={'strides': stride, 'paddings': padding, 'groups': groups}) pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2) return helper.append_activation(pre_act) def sequence_pool(input, pool_type, **kwargs): """ This function add the operator for sequence pooling. It pools features of all time-steps of each instance, and is applied on top of the input using pool_type mentioned in the parameters. It supports four pool_type: - average: :math:`Out[i] = \\frac{\sum_i X_i}{N}` - sum: :math:`Out[i] = \sum_jX_{ij}` - sqrt: :math:`Out[i] = \\frac{\sum_jX_{ij}}{\sqrt{len(X_i)}}` - max: :math:`Out[i] = max(X_i)` .. code-block:: text x is a 1-level LoDTensor: x.lod = [[0, 2, 5, 7]] x.data = [1, 3, 2, 4, 6, 5, 1] x.dims = [7, 1] then output is a Tensor: out.dim = [3, 1] with condition len(x.lod[-1]) - 1 == out.dims[0] for different pool_type: average: out.data = [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2 sum : out.data = [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1 sqrt : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2), 6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2) max : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1) Args: input(variable): The input variable which is a LoDTensor. pool_type (string): The pooling type of sequence_pool. It supports average, sum, sqrt and max. Returns: The sequence pooling variable which is a Tensor. Examples: .. code-block:: python x = fluid.layers.data(name='x', shape=[7, 1], dtype='float32', lod_level=1) avg_x = fluid.layers.sequence_pool(input=x, pool_type='average') sum_x = fluid.layers.sequence_pool(input=x, pool_type='sum') sqrt_x = fluid.layers.sequence_pool(input=x, pool_type='sqrt') max_x = fluid.layers.sequence_pool(input=x, pool_type='max') """ helper = LayerHelper('sequence_pool', input=input, **kwargs) dtype = helper.input_dtype() pool_out = helper.create_tmp_variable(dtype) max_index = helper.create_tmp_variable(dtype) helper.append_op( type="sequence_pool", inputs={"X": input}, outputs={"Out": pool_out, "MaxIndex": max_index}, attrs={"pooltype": pool_type.upper()}) # when pool_type is max, variable max_index is initialized, # so we stop the gradient explicitly here if pool_type == 'max': max_index.stop_gradient = True return pool_out def sequence_first_step(input, **kwargs): """ This funciton get the first step of sequence. .. code-block:: text x is a 1-level LoDTensor: x.lod = [[0, 2, 5, 7]] x.data = [1, 3, 2, 4, 6, 5, 1] x.dims = [7, 1] then output is a Tensor: out.dim = [3, 1] with condition len(x.lod[-1]) - 1 == out.dims[0] out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1) Args: input(variable): The input variable which is a LoDTensor. Returns: The sequence's first step variable which is a Tensor. Examples: .. code-block:: python x = fluid.layers.data(name='x', shape=[7, 1], dtype='float32', lod_level=1) x_first_step = fluid.layers.sequence_first_step(input=x) """ return sequence_pool(input=input, pool_type="first") def sequence_last_step(input, **kwargs): """ This funciton get the last step of sequence. .. code-block:: text x is a 1-level LoDTensor: x.lod = [[0, 2, 5, 7]] x.data = [1, 3, 2, 4, 6, 5, 1] x.dims = [7, 1] then output is a Tensor: out.dim = [3, 1] with condition len(x.lod[-1]) - 1 == out.dims[0] out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1) Args: input(variable): The input variable which is a LoDTensor. Returns: The sequence's last step variable which is a Tensor. Examples: .. code-block:: python x = fluid.layers.data(name='x', shape=[7, 1], dtype='float32', lod_level=1) x_last_step = fluid.layers.sequence_last_step(input=x) """ return sequence_pool(input=input, pool_type="last") def pool2d(input, pool_size, pool_type, pool_stride=None, pool_padding=None, global_pooling=False): """ This function adds the operator for pooling in 2 dimensions, using the pooling configurations mentioned in input parameters. """ if pool_padding is None: pool_padding = [0, 0] if pool_stride is None: pool_stride = [1, 1] if pool_type not in ["max", "avg"]: raise ValueError( "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.", str(pool_type)) if isinstance(pool_size, int): pool_size = [pool_size, pool_size] if isinstance(pool_stride, int): pool_stride = [pool_stride, pool_stride] if isinstance(pool_padding, int): pool_padding = [pool_padding, pool_padding] helper = LayerHelper('pool2d', **locals()) dtype = helper.input_dtype() pool_out = helper.create_tmp_variable(dtype) helper.append_op( type="pool2d", inputs={"X": input}, outputs={"Out": pool_out}, attrs={ "pooling_type": pool_type, "ksize": pool_size, "global_pooling": global_pooling, "strides": pool_stride, "paddings": pool_padding }) return pool_out def batch_norm(input, act=None, is_test=False, momentum=0.9, epsilon=1e-05, param_attr=None, bias_attr=None, data_layout='NCHW'): """ This function helps create an operator to implement the BatchNorm layer using the configurations from the input parameters. """ helper = LayerHelper('batch_norm', **locals()) dtype = helper.input_dtype() input_shape = input.shape if data_layout == 'NCHW': channel_num = input_shape[1] else: if data_layout == 'NHWC': channel_num = input_shape[-1] else: raise ValueError("unsupported data layout:" + data_layout) param_shape = [channel_num] # create parameter scale = helper.create_parameter( attr=helper.param_attr, shape=param_shape, dtype=dtype, default_initializer=Constant(1.0)) bias = helper.create_parameter( attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True) mean = helper.create_global_variable( dtype=input.dtype, shape=param_shape, persistable=True, stop_gradient=True) helper.set_variable_initializer(var=mean, initializer=Constant(0.0)) variance = helper.create_global_variable( dtype=input.dtype, shape=param_shape, persistable=True, stop_gradient=True) helper.set_variable_initializer(var=variance, initializer=Constant(1.0)) # create output # mean and mean_out share the same memory mean_out = mean # variance and variance out share the same memory variance_out = variance saved_mean = helper.create_tmp_variable(dtype=dtype, stop_gradient=True) saved_variance = helper.create_tmp_variable(dtype=dtype, stop_gradient=True) batch_norm_out = helper.create_tmp_variable(dtype) helper.append_op( type="batch_norm", inputs={ "X": input, "Scale": scale, "Bias": bias, "Mean": mean, "Variance": variance }, outputs={ "Y": batch_norm_out, "MeanOut": mean_out, "VarianceOut": variance_out, "SavedMean": saved_mean, "SavedVariance": saved_variance }, attrs={"momentum": momentum, "epsilon": epsilon, "is_test": is_test}) return helper.append_activation(batch_norm_out) def beam_search_decode(ids, scores): helper = LayerHelper('beam_search_decode', **locals()) sentence_ids = helper.create_tmp_variable(dtype=ids.dtype) sentence_scores = helper.create_tmp_variable(dtype=ids.dtype) helper.append_op( type="beam_search_decode", inputs={"Ids": ids, "Scores": scores}, outputs={ "SentenceIds": sentence_ids, "SentenceScores": sentence_scores }) return sentence_ids, sentence_scores def conv2d_transpose(input, num_filters, output_size=None, filter_size=None, padding=None, stride=None, dilation=None, param_attr=None): """ The transpose of conv2d layer. This layer is also known as deconvolution layer. Args: input(Variable): The input image with [N, C, H, W] format. num_filters(int): The number of filter. It is as same as the output image channel. output_size(int|tuple|None): The output image size. If output size is a tuple, it must contain two integers, (image_H, image_W). This parameter only works when filter_size is None. filter_size(int|tuple|None): The filter size. If filter_size is a tuple, it must contain two integers, (filter_size_H, filter_size_W). Otherwise, the filter will be a square. None if use output size to calculate filter_size padding(int|tuple): The padding size. If padding is a tuple, it must contain two integers, (padding_H, padding_W). Otherwise, the padding_H = padding_W = padding. stride(int|tuple): The stride size. If stride is a tuple, it must contain two integers, (stride_H, stride_W). Otherwise, the stride_H = stride_W = stride. dilation(int|tuple): The dilation size. If dilation is a tuple, it must contain two integers, (dilation_H, dilation_W). Otherwise, the dilation_H = dilation_W = dilation. param_attr: Parameter Attribute. main_program(Program): the main program startup_program(Program): the startup program Returns: Variable: Output image. """ helper = LayerHelper("conv2d_transpose", **locals()) if not isinstance(input, Variable): raise TypeError("Input of conv2d_transpose must be Variable") input_channel = input.shape[1] op_attr = dict() if isinstance(padding, int): op_attr['paddings'] = [padding, padding] elif padding is not None: op_attr['paddings'] = padding if isinstance(stride, int): op_attr['strides'] = [stride, stride] elif stride is not None: op_attr['strides'] = stride if isinstance(dilation, int): op_attr['dilations'] = [dilation, dilation] elif dilation is not None: op_attr['dilations'] = dilation if filter_size is None: if output_size is None: raise ValueError("output_size must be set when filter_size is None") if isinstance(output_size, int): output_size = [output_size, output_size] padding = op_attr.get('paddings', [0, 0]) stride = op_attr.get('strides', [1, 1]) dilation = op_attr.get('dilations', [1, 1]) h_in = input.shape[2] w_in = input.shape[3] filter_size_h = (output_size[0] - (h_in - 1) * stride[0] + 2 * padding[0] - 1) / dilation[0] + 1 filter_size_w = (output_size[1] - (w_in - 1) * stride[1] + 2 * padding[1] - 1) / dilation[1] + 1 filter_size = [filter_size_h, filter_size_w] elif isinstance(filter_size, int): filter_size = [filter_size, filter_size] filter_shape = [input_channel, num_filters] + filter_size img_filter = helper.create_parameter( dtype=input.dtype, shape=filter_shape, attr=helper.param_attr) out = helper.create_tmp_variable(dtype=input.dtype) helper.append_op( type='conv2d_transpose', inputs={'Input': [input], 'Filter': [img_filter]}, outputs={'Output': out}, attrs=op_attr) return out def sequence_expand(x, y): """Sequence Expand Layer. This layer will expand the input variable **x** according to LoD information of **y**. And the following examples will explain how sequence_expand works: .. code-block:: text * Case 1 x is a LoDTensor: x.lod = [[0, 2, 3], [0, 1, 3, 4]] x.data = [a, b, c, d] x.dims = [4, 1] y is a LoDTensor: y.lod = [[0, 2, 4], [0, 3, 6, 7, 8]] with condition len(y.lod[-1]) - 1 == x.dims[0] then output is a 2-level LoDTensor: out.lod = [[0, 2, 4], [0, 3, 6, 7, 8]] out.data = [a, a, a, b, b, b, c, d] out.dims = [8, 1] * Case 2 x is a Tensor: x.data = [a, b, c] x.dims = [3, 1] y is a LoDTensor: y.lod = [[0, 2, 3, 6]] with condition len(y.lod[-1]) - 1 == x.dims[0] then output is a 1-level LoDTensor: out.lod = [[0, 2, 3, 6]] out.data = [a, a, b, c, c, c] out.dims = [6, 1] Args: x (Variable): The input variable which is a Tensor or LoDTensor. y (Variable): The input variable which is a LoDTensor. Returns: Variable: The expanded variable which is a LoDTensor. Examples: .. code-block:: python x = fluid.layers.data(name='x', shape=[10], dtype='float32') y = fluid.layers.data(name='y', shape=[10, 20], dtype='float32', lod_level=1) out = layers.sequence_expand(x=x, y=y) """ helper = LayerHelper('sequence_expand', input=x, **locals()) dtype = helper.input_dtype() tmp = helper.create_tmp_variable(dtype) helper.append_op( type='sequence_expand', inputs={'X': x, 'Y': y}, outputs={'Out': tmp}) return tmp def lstm_unit(x_t, hidden_t_prev, cell_t_prev, forget_bias=0.0, param_attr=None, bias_attr=None): """Lstm unit layer. The equation of a lstm step is: .. math:: i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i) f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + b_f) c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t + W_{h_c}h_{t-1} + b_c) o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + b_o) h_t & = o_t tanh(c_t) The inputs of lstm unit include :math:`x_t`, :math:`h_{t-1}` and :math:`c_{t-1}`. The 2nd dimensions of :math:`h_{t-1}` and :math:`c_{t-1}` should be same. The implementation separates the linear transformation and non-linear transformation apart. Here, we take :math:`i_t` as an example. The linear transformation is applied by calling a `fc` layer and the equation is: .. math:: L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i The non-linear transformation is applied by calling `lstm_unit_op` and the equation is: .. math:: i_t = \sigma(L_{i_t}) This layer has two outputs including :math:`h_t` and :math:`o_t`. Args: x_t (Variable): The input value of current step, a 2-D tensor with shape M x N, M for batch size and N for input size. hidden_t_prev (Variable): The hidden value of lstm unit, a 2-D tensor with shape M x S, M for batch size and S for size of lstm unit. cell_t_prev (Variable): The cell value of lstm unit, a 2-D tensor with shape M x S, M for batch size and S for size of lstm unit. forget_bias (float): The forget bias of lstm unit. param_attr (ParamAttr): The attributes of parameter weights, used to set initializer, name etc. bias_attr (ParamAttr): The attributes of bias weights, if not False, bias weights will be created and be set to default value. Returns: tuple: The hidden value and cell value of lstm unit. Raises: ValueError: The ranks of **x_t**, **hidden_t_prev** and **cell_t_prev**\ not be 2 or the 1st dimensions of **x_t**, **hidden_t_prev** \ and **cell_t_prev** not be the same or the 2nd dimensions of \ **hidden_t_prev** and **cell_t_prev** not be the same. Examples: .. code-block:: python x_t = fluid.layers.fc(input=x_t_data, size=10) prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=30) prev_cell = fluid.layers.fc(input=prev_cell_data, size=30) hidden_value, cell_value = fluid.layers.lstm_unit(x_t=x_t, hidden_t_prev=prev_hidden, cell_t_prev=prev_cell) """ helper = LayerHelper('lstm_unit', **locals()) if len(x_t.shape) != 2: raise ValueError("Rank of x_t must be 2.") if len(hidden_t_prev.shape) != 2: raise ValueError("Rank of hidden_t_prev must be 2.") if len(cell_t_prev.shape) != 2: raise ValueError("Rank of cell_t_prev must be 2.") if x_t.shape[0] != hidden_t_prev.shape[0] or x_t.shape[ 0] != cell_t_prev.shape[0]: raise ValueError("The 1st dimensions of x_t, hidden_t_prev and " "cell_t_prev must be the same.") if hidden_t_prev.shape[1] != cell_t_prev.shape[1]: raise ValueError("The 2nd dimensions of hidden_t_prev and " "cell_t_prev must be the same.") if bias_attr is None: bias_attr = ParamAttr() size = cell_t_prev.shape[1] concat_out = concat(input=[x_t, hidden_t_prev], axis=1) fc_out = fc(input=concat_out, size=4 * size, param_attr=param_attr, bias_attr=bias_attr) dtype = x_t.dtype c = helper.create_tmp_variable(dtype) h = helper.create_tmp_variable(dtype) helper.append_op( type='lstm_unit', inputs={"X": fc_out, "C_prev": cell_t_prev}, outputs={"C": c, "H": h}, attrs={"forget_bias": forget_bias}) return h, c def reduce_sum(input, dim=None, keep_dim=False): """ Computes the sum of tensor elements over the given dimension. Args: input (Variable): The input variable which is a Tensor or LoDTensor. dim (int|None): The dimension along which the sum is performed. If :attr:`None`, sum all elements of :attr:`input` and return a Tensor variable with a single element, otherwise must be in the range :math:`[-rank(input), rank(input))`. If :math:`dim < 0`, the dimension to reduce is :math:`rank + dim`. keep_dim (bool): Whether to reserve the reduced dimension in the output Tensor. The result tensor will have one fewer dimension than the :attr:`input` unless :attr:`keep_dim` is true. Returns: Variable: The reduced Tensor variable. Examples: .. code-block:: python # x is a Tensor variable with following elements: # [[0.2, 0.3, 0.5, 0.9] # [0.1, 0.2, 0.6, 0.7]] # Each example is followed by the correspending output tensor. fluid.layers.reduce_sum(x) # [3.5] fluid.layers.reduce_sum(x, dim=0) # [0.3, 0.5, 1.1, 1.6] fluid.layers.reduce_sum(x, dim=-1) # [1.9, 1.6] fluid.layers.reduce_sum(x, dim=1, keep_dim=True) # [[1.9], [1.6]] """ helper = LayerHelper('reduce_sum', **locals()) out = helper.create_tmp_variable(dtype=helper.input_dtype()) helper.append_op( type='reduce_sum', inputs={'X': input}, outputs={'Out': out}, attrs={ 'dim': dim if dim != None else 0, 'keep_dim': keep_dim, 'reduce_all': True if dim == None else False }) return out def reduce_mean(input, dim=None, keep_dim=False): """ Computes the mean of tensor elements over the given dimension. Args: input (Variable): The input variable which is a Tensor or LoDTensor. dim (int|None): The dimension along which the mean is computed. If :attr:`None`, compute the mean over all elements of :attr:`input` and return a Tensor variable with a single element, otherwise must be in the range :math:`[-rank(input), rank(input))`. If :math:`dim < 0`, the dimension to reduce is :math:`rank + dim`. keep_dim (bool): Whether to reserve the reduced dimension in the output Tensor. The result tensor will have one fewer dimension than the :attr:`input` unless :attr:`keep_dim` is true. Returns: Variable: The reduced Tensor variable. Examples: .. code-block:: python # x is a Tensor variable with following elements: # [[0.2, 0.3, 0.5, 0.9] # [0.1, 0.2, 0.6, 0.7]] # Each example is followed by the correspending output tensor. fluid.layers.reduce_mean(x) # [0.4375] fluid.layers.reduce_mean(x, dim=0) # [0.15, 0.25, 0.55, 0.8] fluid.layers.reduce_mean(x, dim=-1) # [0.475, 0.4] fluid.layers.reduce_mean(x, dim=1, keep_dim=True) # [[0.475], [0.4]] """ helper = LayerHelper('reduce_mean', **locals()) out = helper.create_tmp_variable(dtype=helper.input_dtype()) helper.append_op( type='reduce_mean', inputs={'X': input}, outputs={'Out': out}, attrs={ 'dim': dim if dim != None else 0, 'keep_dim': keep_dim, 'reduce_all': True if dim == None else False }) return out def reduce_max(input, dim=None, keep_dim=False): """ Computes the maximum of tensor elements over the given dimension. Args: input (Variable): The input variable which is a Tensor or LoDTensor. dim (int|None): The dimension along which the maximum is computed. If :attr:`None`, compute the maximum over all elements of :attr:`input` and return a Tensor variable with a single element, otherwise must be in the range :math:`[-rank(input), rank(input))`. If :math:`dim < 0`, the dimension to reduce is :math:`rank + dim`. keep_dim (bool): Whether to reserve the reduced dimension in the output Tensor. The result tensor will have one fewer dimension than the :attr:`input` unless :attr:`keep_dim` is true. Returns: Variable: The reduced Tensor variable. Examples: .. code-block:: python # x is a Tensor variable with following elements: # [[0.2, 0.3, 0.5, 0.9] # [0.1, 0.2, 0.6, 0.7]] # Each example is followed by the correspending output tensor. fluid.layers.reduce_max(x) # [0.9] fluid.layers.reduce_max(x, dim=0) # [0.2, 0.3, 0.6, 0.9] fluid.layers.reduce_max(x, dim=-1) # [0.9, 0.7] fluid.layers.reduce_max(x, dim=1, keep_dim=True) # [[0.9], [0.7]] """ helper = LayerHelper('reduce_max', **locals()) out = helper.create_tmp_variable(dtype=helper.input_dtype()) helper.append_op( type='reduce_max', inputs={'X': input}, outputs={'Out': out}, attrs={ 'dim': dim if dim != None else 0, 'keep_dim': keep_dim, 'reduce_all': True if dim == None else False }) return out def reduce_min(input, dim=None, keep_dim=False): """ Computes the minimum of tensor elements over the given dimension. Args: input (Variable): The input variable which is a Tensor or LoDTensor. dim (int|None): The dimension along which the minimum is computed. If :attr:`None`, compute the minimum over all elements of :attr:`input` and return a Tensor variable with a single element, otherwise must be in the range :math:`[-rank(input), rank(input))`. If :math:`dim < 0`, the dimension to reduce is :math:`rank + dim`. keep_dim (bool): Whether to reserve the reduced dimension in the output Tensor. The result tensor will have one fewer dimension than the :attr:`input` unless :attr:`keep_dim` is true. Returns: Variable: The reduced Tensor variable. Examples: .. code-block:: python # x is a Tensor variable with following elements: # [[0.2, 0.3, 0.5, 0.9] # [0.1, 0.2, 0.6, 0.7]] # Each example is followed by the correspending output tensor. fluid.layers.reduce_min(x) # [0.1] fluid.layers.reduce_min(x, dim=0) # [0.1, 0.2, 0.5, 0.7] fluid.layers.reduce_min(x, dim=-1) # [0.2, 0.1] fluid.layers.reduce_min(x, dim=1, keep_dim=True) # [[0.2], [0.1]] """ helper = LayerHelper('reduce_min', **locals()) out = helper.create_tmp_variable(dtype=helper.input_dtype()) helper.append_op( type='reduce_min', inputs={'X': input}, outputs={'Out': out}, attrs={ 'dim': dim if dim != None else 0, 'keep_dim': keep_dim, 'reduce_all': True if dim == None else False }) return out def split(input, num_or_sections, dim=-1): """ Splits the tensor into multiple sub-tensors. Args: input (Variable): The input variable which is a Tensor or LoDTensor. num_or_sections (int|list): If :attr:`num_or_sections` is an integer, then the integer indicates the number of equal sized sub-tensors that the tensor will be divided into. If :attr:`num_or_sections` is a list of integers, the length of list indicates the number of sub-tensors and the integers indicate the sizes of sub-tensors' :attr:`dim` dimension orderly. dim (int): The dimension along which to split. If :math:`dim < 0`, the dimension to split along is :math:`rank(input) + dim`. Returns: List: The list of segmented tensor variables. Examples: .. code-block:: python # x is a Tensor variable with shape [3, 9, 5]: x0, x1, x2 = fluid.layers.split(x, num_or_sections=3, dim=1) x0.shape # [3, 3, 5] x1.shape # [3, 3, 5] x2.shape # [3, 3, 5] x0, x1, x2 = fluid.layers.split(x, num_or_sections=[2, 3, 4], dim=1) x0.shape # [3, 2, 5] x1.shape # [3, 3, 5] x2.shape # [3, 4, 5] """ helper = LayerHelper('split', **locals()) input_shape = input.shape dim = (len(input_shape) + dim) if dim < 0 else dim if isinstance(num_or_sections, int): assert num_or_sections > 1, 'num_or_sections must be more than 1.' num = num_or_sections else: assert len(num_or_sections) < input_shape[ dim], 'len(num_or_sections) must not be more than input.shape[dim].' num = len(num_or_sections) outs = [ helper.create_tmp_variable(dtype=helper.input_dtype()) for i in range(num) ] helper.append_op( type='split', inputs={'X': input}, outputs={'Out': outs}, attrs={ 'num': num_or_sections if isinstance(num_or_sections, int) else 0, 'sections': num_or_sections if isinstance(num_or_sections, list) else [], 'axis': dim }) return outs def greedy_ctc_error(input, label, blank, normalized=False, name=None): """ This evaluator is to calculate sequence-to-sequence edit distance. Args: input(Variable): (LodTensor, default: LoDTensor), the unscaled probabilities of variable-length sequences, which is a 2-D Tensor with LoD information. It's shape is [Lp, num_classes + 1], where Lp is the sum of all input sequences' length and num_classes is the true number of classes. (not including the blank label). label(Variable): (LodTensor, default: LoDTensor), the ground truth of variable-length sequence, which is a 2-D Tensor with LoD information. It is of the shape [Lg, 1], where Lg is th sum of all labels' length. blank(int): the blank label index of Connectionist Temporal Classification (CTC) loss, which is in thehalf-opened interval [0, num_classes + 1). normalized(bool): Indicated whether to normalize the edit distance by the length of reference string. Returns: Variable: sequence-to-sequence edit distance loss in shape [batch_size, 1]. Examples: .. code-block:: python x = fluid.layers.data(name='x', shape=[8], dtype='float32') y = fluid.layers.data(name='y', shape=[1], dtype='float32') cost = fluid.layers.greedy_ctc_error(input=x,label=y, blank=0) """ helper = LayerHelper("greedy_ctc_error", **locals()) # top 1 op topk_out = helper.create_tmp_variable(dtype=input.dtype) topk_indices = helper.create_tmp_variable(dtype="int64") helper.append_op( type="top_k", inputs={"X": [input]}, outputs={"Out": [topk_out], "Indices": [topk_indices]}, attrs={"k": 1}) # ctc align op ctc_out = helper.create_tmp_variable(dtype="int64") helper.append_op( type="ctc_align", inputs={"Input": [topk_indices]}, outputs={"Output": [ctc_out]}, attrs={"merge_repeated": True, "blank": blank}) # edit distance op edit_distance_out = helper.create_tmp_variable(dtype="int64") helper.append_op( type="edit_distance", inputs={"Hyps": [ctc_out], "Refs": [label]}, outputs={"Out": [edit_distance_out]}, attrs={"normalized": normalized}) return edit_distance_out