From 4673a4a9aa2c4c3d2cf487cacc841d59e817dfac Mon Sep 17 00:00:00 2001 From: wanghaoshuang Date: Thu, 18 Jan 2018 20:40:47 +0800 Subject: [PATCH] divide this operator into ctc_greedy_decoder and edit_distance_error. --- doc/api/v2/fluid/layers.rst | 9 ++- python/paddle/v2/fluid/layers/nn.py | 99 +++++++++++++++++++++++------ 2 files changed, 85 insertions(+), 23 deletions(-) diff --git a/doc/api/v2/fluid/layers.rst b/doc/api/v2/fluid/layers.rst index aae63a9ad06..f1e4e753c52 100644 --- a/doc/api/v2/fluid/layers.rst +++ b/doc/api/v2/fluid/layers.rst @@ -500,9 +500,14 @@ swish .. autofunction:: paddle.v2.fluid.layers.swish :noindex: -greedy_ctc_error +edit_distance_error --------------- -.. autofunction:: paddle.v2.fluid.layers.greedy_ctc_error +.. autofunction:: paddle.v2.fluid.layers.edit_distance_error + :noindex: + +ctc_greedy_decoder +--------------- +.. autofunction:: paddle.v2.fluid.layers.ctc_greedy_decoder :noindex: l2_normalize diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py index 60f2fd8e9d0..72246304bef 100644 --- a/python/paddle/v2/fluid/layers/nn.py +++ b/python/paddle/v2/fluid/layers/nn.py @@ -50,7 +50,8 @@ __all__ = [ 'sequence_last_step', 'dropout', 'split', - 'greedy_ctc_error', + 'ctc_greedy_decoder', + 'edit_distance_error', 'l2_normalize', 'matmul', ] @@ -1791,17 +1792,21 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None): return out -def greedy_ctc_error(input, label, blank, normalized=False, name=None): +def edit_distance_error(input, label, normalized=False, name=None): """ - This evaluator is to calculate sequence-to-sequence edit distance. + EditDistance operator computes the edit distances between a batch of hypothesis strings and their references.Edit distance, also called Levenshtein distance, measures how dissimilar two strings are by counting the minimum number of operations to transform one string into anthor. Here the operations include insertion, deletion, and substitution. For example, given hypothesis string A = "kitten" and reference B = "sitting", the edit distance is 3 for A will be transformed into B at least after two substitutions and one insertion: - Args: + "kitten" -> "sitten" -> "sittin" -> "sitting" - 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). + Input(Hyps) is a LoDTensor consisting of all the hypothesis strings with the total number denoted by `batch_size`, and the separation is specified by the LoD information. And the `batch_size` reference strings are arranged in order in the same way in the LoDTensor Input(Refs). - 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. + Output(Out) contains the `batch_size` results and each stands for the edit stance for a pair of strings respectively. If Attr(normalized) is true, the edit distance will be divided by the length of reference string. - blank(int): the blank label index of Connectionist Temporal Classification (CTC) loss, which is in thehalf-opened interval [0, num_classes + 1). + Args: + + input(Variable): The indices for hypothesis strings. + + label(Variable): The indices for reference strings. normalized(bool): Indicated whether to normalize the edit distance by the length of reference string. @@ -1812,11 +1817,73 @@ def greedy_ctc_error(input, label, blank, normalized=False, name=None): .. code-block:: python x = fluid.layers.data(name='x', shape=[8], dtype='float32') - y = fluid.layers.data(name='y', shape=[1], dtype='float32') + y = fluid.layers.data(name='y', shape=[7], dtype='float32') + + cost = fluid.layers.edit_distance_error(input=x,label=y) + """ + helper = LayerHelper("edit_distance_error", **locals()) + + # edit distance op + edit_distance_out = helper.create_tmp_variable(dtype="int64") + helper.append_op( + type="edit_distance", + inputs={"Hyps": [input], + "Refs": [label]}, + outputs={"Out": [edit_distance_out]}, + attrs={"normalized": normalized}) + + return edit_distance_out + + +def ctc_greedy_decoder(input, blank, name=None): + """ + This op is used to decode sequences by greedy policy by below steps: + 1. Get the indexes of max value for each row in input. a.k.a. numpy.argmax(input, axis=0). + 2. For each sequence in result of step1, merge repeated tokens between two blanks and delete all blanks. + + A simple example as below: + + .. code-block:: text + + Given: + + input.data = [[0.6, 0.1, 0.3, 0.1], + [0.3, 0.2, 0.4, 0.1], + [0.1, 0.5, 0.1, 0.3], + [0.5, 0.1, 0.3, 0.1], + + [0.5, 0.1, 0.3, 0.1], + [0.2, 0.2, 0.2, 0.4], + [0.2, 0.2, 0.1, 0.5], + [0.5, 0.1, 0.3, 0.1]] + + input.lod = [[0, 4, 8]] + + Then: + + output.data = [[2], + [1], + [3]] + + output.lod = [[0, 2, 3]] + + Args: + + input(Variable): (LoDTensor), the 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). + + blank(int): the blank label index of Connectionist Temporal Classification (CTC) loss, which is in thehalf-opened interval [0, num_classes + 1). + + Returns: + Variable: CTC greedy decode result. + + Examples: + .. code-block:: python + + x = fluid.layers.data(name='x', shape=[8], dtype='float32') - cost = fluid.layers.greedy_ctc_error(input=x,label=y, blank=0) + cost = fluid.layers.ctc_greedy_decoder(input=x, blank=0) """ - helper = LayerHelper("greedy_ctc_error", **locals()) + helper = LayerHelper("ctc_greedy_decoder", **locals()) # top 1 op topk_out = helper.create_tmp_variable(dtype=input.dtype) topk_indices = helper.create_tmp_variable(dtype="int64") @@ -1835,14 +1902,4 @@ def greedy_ctc_error(input, label, blank, normalized=False, name=None): 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 + return ctc_out -- GitLab