@ -21,6 +21,8 @@ from ..framework import Variable
from . . param_attr import ParamAttr
from tensor import concat
import pdb
__all__ = [
' fc ' ,
' embedding ' ,
@ -1871,9 +1873,10 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None):
def matmul ( x , y , transpose_x = False , transpose_y = False , name = None ) :
"""
Applies matrix multiplication to two tensors . Currently , the input
tensors ' rank can be any, but when the rank of anyone inputs is
bigger than 3 , this two inputs ' rank should be equal.
Applies matrix multiplication to two tensors .
Currently , the input tensors ' rank can be any, but when the rank of any
inputs is bigger than 3 , this two inputs ' rank should be equal.
The actual behavior depends on the shapes of : math : ` x ` , : math : ` y ` and the
flag values of : attr : ` transpose_x ` , : attr : ` transpose_y ` . Specifically :
@ -1914,24 +1917,55 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
# Examples to clarify shapes of the inputs and output
# x: [B, ..., M, K], y: [B, ..., K, N]
fluid . layers . matmul ( x , y ) # out: [B, ..., M, N]
# x: [B, M, K], y: [B, K, N]
fluid . layers . matmul ( x , y ) # out: [B, M, N]
# x: [B, M, K], y: [K, N]
fluid . layers . matmul ( x , y ) # out: [B, M, N]
# x: [B, M, K], y: [K]
fluid . layers . matmul ( x , y ) # out: [B, M]
# x: [M, K], y: [K, N]
fluid . layers . matmul ( x , y ) # out: [M, N]
# x: [B, M, K], y: [K]
fluid . layers . matmul ( x , y ) # out: [B, M]
# x: [K], y: [K]
fluid . layers . matmul ( x , y ) # out: [1]
# x: [M], y: [N]
# x: [M], y: [N]
fluid . layers . matmul ( x , y , True , True ) # out: [M, N]
"""
def __check_input ( x , y ) :
if len ( y . shape ) > len ( x . shape ) :
raise ValueError (
" Invalid inputs for matmul. "
" x ' s rank should be always greater than or equal to y ' rank. " )
x_shape = list ( x . shape )
y_shape = list ( y . shape )
if len ( x_shape ) == 1 :
x_shape = [ 1 ] + x_shape
if len ( y_shape ) == 1 :
y_shape = [ 1 ] + y_shape
# check the inner 2 dimensions
if transpose_x :
x_shape [ - 2 ] , x_shape [ - 1 ] = x_shape [ - 1 ] , x_shape [ - 2 ]
if transpose_y :
y_shape [ - 2 ] , y_shape [ - 1 ] = y_shape [ - 1 ] , y_shape [ - 2 ]
if x_shape [ - 1 ] != y_shape [ - 2 ] :
raise ValueError ( " Invalid inputs for matmul. " )
if len ( y_shape ) > 2 :
for i , dim_x in enumerate ( x_shape [ : - 2 ] ) :
if dim_x != y_shape [ i ] :
raise ValueError ( " Invalid inputs for matmul. " )
__check_input ( x , y )
helper = LayerHelper ( ' matmul ' , * * locals ( ) )
assert max ( len ( x . shape ) , len ( y . shape ) ) < = 3 or len ( x . shape ) == len (
y .
shape ) , ' Inputs \' rank should be equal or their rank should be less 4. '
out = helper . create_tmp_variable ( dtype = helper . input_dtype ( ) )
helper . append_op (
type = ' matmul ' ,
@ -1949,13 +1983,26 @@ def edit_distance(input,
ignored_tokens = None ,
name = None ) :
"""
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 :
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 :
" kitten " - > " sitten " - > " sittin " - > " sitting "
" kitten " - > " sitten " - > " sittin " - > " sitting "
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 ) .
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 ) .
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 .
Output ( Out ) contains the ` batch_size ` results and each stands for the edit
distance for a pair of strings respectively . If Attr ( normalized ) is true ,
the edit distance will be divided by the length of reference string .
Args :
@ -1963,9 +2010,11 @@ def edit_distance(input,
label ( Variable ) : The indices for reference strings .
normalized ( bool ) : Indicated whether to normalize the edit distance by the length of reference string .
normalized ( bool ) : Indicated whether to normalize the edit distance by
the length of reference string .
ignored_tokens ( list of int ) : Tokens that should be removed before calculating edit distance .
ignored_tokens ( list of int ) : Tokens that should be removed before
calculating edit distance .
Returns :
Variable : sequence - to - sequence edit distance in shape [ batch_size , 1 ] .
@ -2016,8 +2065,10 @@ def edit_distance(input,
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 .
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 :
@ -2047,9 +2098,16 @@ def ctc_greedy_decoder(input, blank, name=None):
Args :
input ( Variable ) : ( LoDTensor < float > ) , 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 ) .
input ( Variable ) : ( LoDTensor < float > ) , 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 ) .
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 .
@ -2217,6 +2275,12 @@ def transpose(x, perm, name=None):
raise ValueError (
" Input(perm) is the permutation of dimensions of Input(input). "
" It ' s length shoud be equal to Input(input) ' s rank. " )
for idx , dim in enumerate ( perm ) :
if dim > = len ( x . shape ) :
raise ValueError (
" Each element in perm should be less than x ' s rank. "
" %d -th element in perm is %d which accesses x ' s rank %d . " %
( idx , perm [ idx ] , len ( x . shape ) ) )
helper = LayerHelper ( ' transpose ' , * * locals ( ) )
out = helper . create_tmp_variable ( x . dtype )
ying
7 years ago