未验证 提交 acbda44c 编写于 作者: W whs 提交者: GitHub

Merge pull request #8365 from wanghaoshuang/seq_error

Add sequence error output to edit distance evaluator
...@@ -22,6 +22,7 @@ from layer_helper import LayerHelper ...@@ -22,6 +22,7 @@ from layer_helper import LayerHelper
__all__ = [ __all__ = [
'Accuracy', 'Accuracy',
'ChunkEvaluator', 'ChunkEvaluator',
'EditDistance',
] ]
...@@ -211,7 +212,7 @@ class ChunkEvaluator(Evaluator): ...@@ -211,7 +212,7 @@ class ChunkEvaluator(Evaluator):
class EditDistance(Evaluator): class EditDistance(Evaluator):
""" """
Accumulate edit distance sum and sequence number from mini-batches and Accumulate edit distance sum and sequence number from mini-batches and
compute the average edit_distance of all batches. compute the average edit_distance and instance error of all batches.
Args: Args:
input: the sequences predicted by network. input: the sequences predicted by network.
...@@ -227,14 +228,12 @@ class EditDistance(Evaluator): ...@@ -227,14 +228,12 @@ class EditDistance(Evaluator):
for epoch in PASS_NUM: for epoch in PASS_NUM:
distance_evaluator.reset(exe) distance_evaluator.reset(exe)
for data in batches: for data in batches:
loss, sum_distance = exe.run(fetch_list=[cost] + distance_evaluator.metrics) loss = exe.run(fetch_list=[cost])
avg_distance = distance_evaluator.eval(exe) distance, instance_error = distance_evaluator.eval(exe)
pass_distance = distance_evaluator.eval(exe)
In the above example: In the above example:
'sum_distance' is the sum of the batch's edit distance. 'distance' is the average of the edit distance in a pass.
'avg_distance' is the average of edit distance from the firt batch to the current batch. 'instance_error' is the instance error rate in a pass.
'pass_distance' is the average of edit distance from all the pass.
""" """
...@@ -244,25 +243,45 @@ class EditDistance(Evaluator): ...@@ -244,25 +243,45 @@ class EditDistance(Evaluator):
if main_program.current_block().idx != 0: if main_program.current_block().idx != 0:
raise ValueError("You can only invoke Evaluator in root block") raise ValueError("You can only invoke Evaluator in root block")
self.total_error = self.create_state( self.total_distance = self.create_state(
dtype='float32', shape=[1], suffix='total_error') dtype='float32', shape=[1], suffix='total_distance')
self.seq_num = self.create_state( self.seq_num = self.create_state(
dtype='int64', shape=[1], suffix='seq_num') dtype='int64', shape=[1], suffix='seq_num')
error, seq_num = layers.edit_distance( self.instance_error = self.create_state(
dtype='int64', shape=[1], suffix='instance_error')
distances, seq_num = layers.edit_distance(
input=input, label=label, ignored_tokens=ignored_tokens) input=input, label=label, ignored_tokens=ignored_tokens)
#error = layers.cast(x=error, dtype='float32')
sum_error = layers.reduce_sum(error) zero = layers.fill_constant(shape=[1], value=0.0, dtype='float32')
layers.sums(input=[self.total_error, sum_error], out=self.total_error) compare_result = layers.equal(distances, zero)
compare_result_int = layers.cast(x=compare_result, dtype='int')
seq_right_count = layers.reduce_sum(compare_result_int)
instance_error_count = layers.elementwise_sub(
x=seq_num, y=seq_right_count)
total_distance = layers.reduce_sum(distances)
layers.sums(
input=[self.total_distance, total_distance],
out=self.total_distance)
layers.sums(input=[self.seq_num, seq_num], out=self.seq_num) layers.sums(input=[self.seq_num, seq_num], out=self.seq_num)
self.metrics.append(sum_error) layers.sums(
input=[self.instance_error, instance_error_count],
out=self.instance_error)
self.metrics.append(total_distance)
self.metrics.append(instance_error_count)
def eval(self, executor, eval_program=None): def eval(self, executor, eval_program=None):
if eval_program is None: if eval_program is None:
eval_program = Program() eval_program = Program()
block = eval_program.current_block() block = eval_program.current_block()
with program_guard(main_program=eval_program): with program_guard(main_program=eval_program):
total_error = _clone_var_(block, self.total_error) total_distance = _clone_var_(block, self.total_distance)
seq_num = _clone_var_(block, self.seq_num) seq_num = _clone_var_(block, self.seq_num)
instance_error = _clone_var_(block, self.instance_error)
seq_num = layers.cast(x=seq_num, dtype='float32') seq_num = layers.cast(x=seq_num, dtype='float32')
out = layers.elementwise_div(x=total_error, y=seq_num) instance_error = layers.cast(x=instance_error, dtype='float32')
return np.array(executor.run(eval_program, fetch_list=[out])[0]) avg_distance = layers.elementwise_div(x=total_distance, y=seq_num)
avg_instance_error = layers.elementwise_div(
x=instance_error, y=seq_num)
result = executor.run(
eval_program, fetch_list=[avg_distance, avg_instance_error])
return np.array(result[0]), np.array(result[1])
...@@ -2479,10 +2479,7 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None): ...@@ -2479,10 +2479,7 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
return out return out
def edit_distance(input, def edit_distance(input, label, normalized=True, ignored_tokens=None,
label,
normalized=False,
ignored_tokens=None,
name=None): name=None):
""" """
EditDistance operator computes the edit distances between a batch of EditDistance operator computes the edit distances between a batch of
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