提交 8c302d48 编写于 作者: F fengjiayi

remove kwargs in layer apis

上级 e9d30991
...@@ -829,12 +829,12 @@ def crf_decoding(input, param_attr, label=None): ...@@ -829,12 +829,12 @@ def crf_decoding(input, param_attr, label=None):
return viterbi_path return viterbi_path
def cos_sim(X, Y, **kwargs): def cos_sim(X, Y):
""" """
This function performs the cosine similarity between two tensors This function performs the cosine similarity between two tensors
X and Y and returns that as the output. X and Y and returns that as the output.
""" """
helper = LayerHelper('cos_sim', **kwargs) helper = LayerHelper('cos_sim', **locals())
out = helper.create_tmp_variable(dtype=X.dtype) out = helper.create_tmp_variable(dtype=X.dtype)
xnorm = helper.create_tmp_variable(dtype=X.dtype) xnorm = helper.create_tmp_variable(dtype=X.dtype)
ynorm = helper.create_tmp_variable(dtype=X.dtype) ynorm = helper.create_tmp_variable(dtype=X.dtype)
...@@ -848,7 +848,7 @@ def cos_sim(X, Y, **kwargs): ...@@ -848,7 +848,7 @@ def cos_sim(X, Y, **kwargs):
return out return out
def dropout(x, dropout_prob, is_test=False, seed=None, **kwargs): def dropout(x, dropout_prob, is_test=False, seed=None):
""" """
Computes dropout. Computes dropout.
...@@ -877,7 +877,7 @@ def dropout(x, dropout_prob, is_test=False, seed=None, **kwargs): ...@@ -877,7 +877,7 @@ def dropout(x, dropout_prob, is_test=False, seed=None, **kwargs):
droped = fluid.layers.dropout(input=x, dropout_rate=0.5) droped = fluid.layers.dropout(input=x, dropout_rate=0.5)
""" """
helper = LayerHelper('dropout', **kwargs) helper = LayerHelper('dropout', **locals())
out = helper.create_tmp_variable(dtype=x.dtype) out = helper.create_tmp_variable(dtype=x.dtype)
mask = helper.create_tmp_variable(dtype=x.dtype, stop_gradient=True) mask = helper.create_tmp_variable(dtype=x.dtype, stop_gradient=True)
helper.append_op( helper.append_op(
...@@ -894,7 +894,7 @@ def dropout(x, dropout_prob, is_test=False, seed=None, **kwargs): ...@@ -894,7 +894,7 @@ def dropout(x, dropout_prob, is_test=False, seed=None, **kwargs):
return out return out
def cross_entropy(input, label, **kwargs): def cross_entropy(input, label, soft_label=False):
""" """
**Cross Entropy Layer** **Cross Entropy Layer**
...@@ -936,7 +936,7 @@ def cross_entropy(input, label, **kwargs): ...@@ -936,7 +936,7 @@ def cross_entropy(input, label, **kwargs):
tensor<int64> with shape [N x 1]. When tensor<int64> with shape [N x 1]. When
`soft_label` is set to `True`, `label` is a `soft_label` is set to `True`, `label` is a
tensor<float/double> with shape [N x D]. tensor<float/double> with shape [N x D].
soft_label (bool, via `**kwargs`): a flag indicating whether to soft_label (bool): a flag indicating whether to
interpretate the given labels as soft interpretate the given labels as soft
labels, default `False`. labels, default `False`.
...@@ -956,18 +956,18 @@ def cross_entropy(input, label, **kwargs): ...@@ -956,18 +956,18 @@ def cross_entropy(input, label, **kwargs):
predict = fluid.layers.fc(input=net, size=classdim, act='softmax') predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
cost = fluid.layers.cross_entropy(input=predict, label=label) cost = fluid.layers.cross_entropy(input=predict, label=label)
""" """
helper = LayerHelper('cross_entropy', **kwargs) helper = LayerHelper('cross_entropy', **locals())
out = helper.create_tmp_variable(dtype=input.dtype) out = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op( helper.append_op(
type='cross_entropy', type='cross_entropy',
inputs={'X': [input], inputs={'X': [input],
'Label': [label]}, 'Label': [label]},
outputs={'Y': [out]}, outputs={'Y': [out]},
attrs=kwargs) attrs={"soft_label": soft_label})
return out return out
def square_error_cost(input, label, **kwargs): def square_error_cost(input, label):
""" """
**Square error cost layer** **Square error cost layer**
...@@ -1002,7 +1002,7 @@ def square_error_cost(input, label, **kwargs): ...@@ -1002,7 +1002,7 @@ def square_error_cost(input, label, **kwargs):
cost = layers.square_error_cost(input=y_predict, label=y) cost = layers.square_error_cost(input=y_predict, label=y)
""" """
helper = LayerHelper('square_error_cost', **kwargs) helper = LayerHelper('square_error_cost', **locals())
minus_out = helper.create_tmp_variable(dtype=input.dtype) minus_out = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op( helper.append_op(
type='elementwise_sub', type='elementwise_sub',
...@@ -1017,12 +1017,12 @@ def square_error_cost(input, label, **kwargs): ...@@ -1017,12 +1017,12 @@ def square_error_cost(input, label, **kwargs):
return square_out return square_out
def accuracy(input, label, k=1, correct=None, total=None, **kwargs): def accuracy(input, label, k=1, correct=None, total=None):
""" """
This function computes the accuracy using the input and label. This function computes the accuracy using the input and label.
The output is the top_k inputs and their indices. The output is the top_k inputs and their indices.
""" """
helper = LayerHelper("accuracy", **kwargs) helper = LayerHelper("accuracy", **locals())
topk_out = helper.create_tmp_variable(dtype=input.dtype) topk_out = helper.create_tmp_variable(dtype=input.dtype)
topk_indices = helper.create_tmp_variable(dtype="int64") topk_indices = helper.create_tmp_variable(dtype="int64")
helper.append_op( helper.append_op(
...@@ -1055,13 +1055,12 @@ def chunk_eval(input, ...@@ -1055,13 +1055,12 @@ def chunk_eval(input,
label, label,
chunk_scheme, chunk_scheme,
num_chunk_types, num_chunk_types,
excluded_chunk_types=None, excluded_chunk_types=None):
**kwargs):
""" """
This function computes and outputs the precision, recall and This function computes and outputs the precision, recall and
F1-score of chunk detection. F1-score of chunk detection.
""" """
helper = LayerHelper("chunk_eval", **kwargs) helper = LayerHelper("chunk_eval", **locals())
# prepare output # prepare output
precision = helper.create_tmp_variable(dtype="float32") precision = helper.create_tmp_variable(dtype="float32")
...@@ -1293,7 +1292,7 @@ def conv2d(input, ...@@ -1293,7 +1292,7 @@ def conv2d(input,
return helper.append_activation(pre_act) return helper.append_activation(pre_act)
def sequence_pool(input, pool_type, **kwargs): def sequence_pool(input, pool_type):
""" """
This function add the operator for sequence pooling. This function add the operator for sequence pooling.
It pools features of all time-steps of each instance, and is applied It pools features of all time-steps of each instance, and is applied
...@@ -1343,7 +1342,7 @@ def sequence_pool(input, pool_type, **kwargs): ...@@ -1343,7 +1342,7 @@ def sequence_pool(input, pool_type, **kwargs):
sqrt_x = fluid.layers.sequence_pool(input=x, pool_type='sqrt') sqrt_x = fluid.layers.sequence_pool(input=x, pool_type='sqrt')
max_x = fluid.layers.sequence_pool(input=x, pool_type='max') max_x = fluid.layers.sequence_pool(input=x, pool_type='max')
""" """
helper = LayerHelper('sequence_pool', input=input, **kwargs) helper = LayerHelper('sequence_pool', **locals())
dtype = helper.input_dtype() dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype) pool_out = helper.create_tmp_variable(dtype)
max_index = helper.create_tmp_variable(dtype) max_index = helper.create_tmp_variable(dtype)
...@@ -1363,7 +1362,7 @@ def sequence_pool(input, pool_type, **kwargs): ...@@ -1363,7 +1362,7 @@ def sequence_pool(input, pool_type, **kwargs):
return pool_out return pool_out
def sequence_first_step(input, **kwargs): def sequence_first_step(input):
""" """
This funciton get the first step of sequence. This funciton get the first step of sequence.
...@@ -1396,7 +1395,7 @@ def sequence_first_step(input, **kwargs): ...@@ -1396,7 +1395,7 @@ def sequence_first_step(input, **kwargs):
return sequence_pool(input=input, pool_type="first") return sequence_pool(input=input, pool_type="first")
def sequence_last_step(input, **kwargs): def sequence_last_step(input):
""" """
This funciton get the last step of sequence. This funciton get the last step of sequence.
...@@ -2336,7 +2335,8 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None): ...@@ -2336,7 +2335,8 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None):
normed = fluid.layers.l2_normalize(x=data, axis=1) normed = fluid.layers.l2_normalize(x=data, axis=1)
""" """
if len(x.shape) == 1: axis = 0 if len(x.shape) == 1:
axis = 0
helper = LayerHelper("l2_normalize", **locals()) helper = LayerHelper("l2_normalize", **locals())
...@@ -2654,7 +2654,7 @@ def ctc_greedy_decoder(input, blank, name=None): ...@@ -2654,7 +2654,7 @@ def ctc_greedy_decoder(input, blank, name=None):
return ctc_out return ctc_out
def warpctc(input, label, blank=0, norm_by_times=False, **kwargs): def warpctc(input, label, blank=0, norm_by_times=False):
""" """
An operator integrating the open source Warp-CTC library An operator integrating the open source Warp-CTC library
(https://github.com/baidu-research/warp-ctc) (https://github.com/baidu-research/warp-ctc)
...@@ -2695,7 +2695,7 @@ def warpctc(input, label, blank=0, norm_by_times=False, **kwargs): ...@@ -2695,7 +2695,7 @@ def warpctc(input, label, blank=0, norm_by_times=False, **kwargs):
cost = layers.warpctc(input=y_predict, label=y) cost = layers.warpctc(input=y_predict, label=y)
""" """
helper = LayerHelper('warpctc', **kwargs) helper = LayerHelper('warpctc', **locals())
loss_out = helper.create_tmp_variable(dtype=input.dtype) loss_out = helper.create_tmp_variable(dtype=input.dtype)
grad_out = helper.create_tmp_variable(dtype=input.dtype) grad_out = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op( helper.append_op(
......
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