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未验证 提交 5100c4c3 编写于 作者: W Wu Yi 提交者： GitHub
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Enable docstring checker and fix comments (#11351) 

* enable docstring checker and fix comments

* update

* update by comments

* fix build
上级 19fd0717
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python/paddle/fluid/layers/nn.py
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...@@ -12,7 +12,7 @@ ...@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
""" """
All layers just related to the neural network. All layers just related to the neural network.
""" """
from ..layer_helper import LayerHelper from ..layer_helper import LayerHelper
...@@ -95,7 +95,6 @@ def fc(input, ...@@ -95,7 +95,6 @@ def fc(input,
num_flatten_dims=1, num_flatten_dims=1,
param_attr=None, param_attr=None,
bias_attr=None, bias_attr=None,
use_cudnn=False,
use_mkldnn=False, use_mkldnn=False,
act=None, act=None,
is_test=False, is_test=False,
...@@ -222,6 +221,7 @@ def embedding(input, ...@@ -222,6 +221,7 @@ def embedding(input,
have two elements which indicate the size of the dictionary of have two elements which indicate the size of the dictionary of
embeddings and the size of each embedding vector respectively. embeddings and the size of each embedding vector respectively.
is_sparse(bool): The flag indicating whether to use sparse update. is_sparse(bool): The flag indicating whether to use sparse update.
is_distributed (bool): Whether to run lookup table from remote parameter server.
padding_idx(int|long|None): If :attr:`None`, it makes no effect to lookup. padding_idx(int|long|None): If :attr:`None`, it makes no effect to lookup.
Otherwise the given :attr:`padding_idx` indicates padding the output Otherwise the given :attr:`padding_idx` indicates padding the output
with zeros whenever lookup encounters it in :attr:`input`. If with zeros whenever lookup encounters it in :attr:`input`. If
...@@ -654,8 +654,9 @@ def dynamic_gru(input, ...@@ -654,8 +654,9 @@ def dynamic_gru(input,
:attr:`False`. :attr:`False`.
gate_activation(str): The activation for update gate and reset gate. gate_activation(str): The activation for update gate and reset gate.
Choices = ["sigmoid", "tanh", "relu", "identity"], default "sigmoid". Choices = ["sigmoid", "tanh", "relu", "identity"], default "sigmoid".
activation(str): The activation for candidate hidden state. candidate_activation(str): The activation for candidate hidden state.
Choices = ["sigmoid", "tanh", "relu", "identity"], default "tanh". Choices = ["sigmoid", "tanh", "relu", "identity"], default "tanh".
h_0 (Variable): The hidden output of the first time step.
Returns: Returns:
Variable: The hidden state of GRU. The shape is :math:`(T \\times D)`, \ Variable: The hidden state of GRU. The shape is :math:`(T \\times D)`, \
...@@ -873,6 +874,13 @@ def cos_sim(X, Y): ...@@ -873,6 +874,13 @@ 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.
Args:
X (Variable): The input X.
Y (Variable): The input Y.
Returns:
Variable: the output of cosine(X, Y).
""" """
helper = LayerHelper('cos_sim', **locals()) helper = LayerHelper('cos_sim', **locals())
out = helper.create_tmp_variable(dtype=X.dtype) out = helper.create_tmp_variable(dtype=X.dtype)
...@@ -899,15 +907,15 @@ def dropout(x, dropout_prob, is_test=False, seed=None, name=None): ...@@ -899,15 +907,15 @@ def dropout(x, dropout_prob, is_test=False, seed=None, name=None):
unchanged. unchanged.
Args: Args:
x(variable): The input tensor. x (Variable): The input tensor.
dropout_prob(float): Probability of setting units to zero. dropout_prob (float): Probability of setting units to zero.
is_test(bool): A flag indicating whether it is in test phrase or not. is_test (bool): A flag indicating whether it is in test phrase or not.
seed(int): A Python integer used to create random seeds. If this seed (int): A Python integer used to create random seeds. If this
parameter is set to None, a random seed is used. parameter is set to None, a random seed is used.
NOTE: If an integer seed is given, always the same output NOTE: If an integer seed is given, always the same output
units will be dropped. DO NOT use a fixed seed in training. units will be dropped. DO NOT use a fixed seed in training.
name(str|None): A name for this layer(optional). If set None, the layer name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically. will be named automatically.
Returns: Returns:
Variable: A tensor variable. Variable: A tensor variable.
...@@ -1029,8 +1037,8 @@ def square_error_cost(input, label): ...@@ -1029,8 +1037,8 @@ def square_error_cost(input, label):
* :math:`Out`: Output value, same shape with :math:`X`. * :math:`Out`: Output value, same shape with :math:`X`.
Args: Args:
input(Variable): Input tensor, has predictions. input (Variable): Input tensor, has predictions.
label(Variable): Label tensor, has target labels. label (Variable): Label tensor, has target labels.
Returns: Returns:
Variable: The tensor variable storing the element-wise squared error \ Variable: The tensor variable storing the element-wise squared error \
...@@ -1059,6 +1067,7 @@ def square_error_cost(input, label): ...@@ -1059,6 +1067,7 @@ def square_error_cost(input, label):
return square_out return square_out
@templatedoc()
def chunk_eval(input, def chunk_eval(input,
label, label,
chunk_scheme, chunk_scheme,
...@@ -1067,6 +1076,18 @@ def chunk_eval(input, ...@@ -1067,6 +1076,18 @@ def chunk_eval(input,
""" """
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.
Args:
input (Variable): prediction output of the network.
label (Variable): label of the test data set.
chunk_scheme (str): ${chunk_scheme_comment}
num_chunk_types (int): ${num_chunk_types_comment}
excluded_chunk_types (list): ${excluded_chunk_types_comment}
Returns:
tuple: tuple containing: (precision, recall, f1_score,
num_infer_chunks, num_label_chunks,
num_correct_chunks)
""" """
helper = LayerHelper("chunk_eval", **locals()) helper = LayerHelper("chunk_eval", **locals())
...@@ -1099,6 +1120,7 @@ def chunk_eval(input, ...@@ -1099,6 +1120,7 @@ def chunk_eval(input,
num_correct_chunks) num_correct_chunks)
@templatedoc()
def sequence_conv(input, def sequence_conv(input,
num_filters, num_filters,
filter_size=3, filter_size=3,
...@@ -1111,6 +1133,19 @@ def sequence_conv(input, ...@@ -1111,6 +1133,19 @@ def sequence_conv(input,
This function creates the op for sequence_conv, using the inputs and This function creates the op for sequence_conv, using the inputs and
other convolutional configurations for the filters and stride as given other convolutional configurations for the filters and stride as given
in the input parameters to the function. in the input parameters to the function.
Args:
input (Variable): ${x_comment}
num_filters (int): number of filters.
filter_size (int): the filter size (H and W).
filter_stride (int): stride of the filter.
padding (bool): if True, add paddings.
bias_attr (ParamAttr|None): attributes for bias
param_attr (ParamAttr|None): attributes for parameter
act (str): the activation type
Returns:
Variable: output of sequence_conv
""" """
# FIXME(dzh) : want to unify the argument of python layer # FIXME(dzh) : want to unify the argument of python layer
...@@ -1225,33 +1260,34 @@ def conv2d(input, ...@@ -1225,33 +1260,34 @@ def conv2d(input,
W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1 W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1
Args: Args:
input(Variable): The input image with [N, C, H, W] format. 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 num_filters(int): The number of filter. It is as same as the output
image channel. image channel.
filter_size(int|tuple|None): The filter size. If filter_size is a tuple, 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). it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square. Otherwise, the filter will be a square.
stride(int|tuple): The stride size. If stride is a tuple, it must stride (int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: stride = 1. stride_H = stride_W = stride. Default: stride = 1.
padding(int|tuple): The padding size. If padding is a tuple, it must padding (int|tuple): The padding size. If padding is a tuple, it must
contain two integers, (padding_H, padding_W). Otherwise, the contain two integers, (padding_H, padding_W). Otherwise, the
padding_H = padding_W = padding. Default: padding = 0. padding_H = padding_W = padding. Default: padding = 0.
dilation(int|tuple): The dilation size. If dilation is a tuple, it must dilation (int|tuple): The dilation size. If dilation is a tuple, it must
contain two integers, (dilation_H, dilation_W). Otherwise, the contain two integers, (dilation_H, dilation_W). Otherwise, the
dilation_H = dilation_W = dilation. Default: dilation = 1. dilation_H = dilation_W = dilation. Default: dilation = 1.
groups(int): The groups number of the Conv2d Layer. According to grouped groups (int): The groups number of the Conv2d Layer. According to grouped
convolution in Alex Krizhevsky's Deep CNN paper: when group=2, convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
the first half of the filters is only connected to the first half 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 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 connected to the second half of the input channels. Default: groups=1
param_attr(ParamAttr): The parameters to the Conv2d Layer. Default: None param_attr (ParamAttr): The parameters to the Conv2d Layer. Default: None
bias_attr(ParamAttr): Bias parameter for the Conv2d layer. Default: None bias_attr (ParamAttr): Bias parameter for the Conv2d layer. Default: None
use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True library is installed. Default: True
act(str): Activation type. Default: None use_mkldnn (bool): Use mkldnn kernels or not.
name(str|None): A name for this layer(optional). If set None, the layer act (str): Activation type. Default: None
will be named automatically. name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns: Returns:
Variable: The tensor variable storing the convolution and \ Variable: The tensor variable storing the convolution and \
...@@ -1486,6 +1522,22 @@ def pool2d(input, ...@@ -1486,6 +1522,22 @@ def pool2d(input,
""" """
This function adds the operator for pooling in 2 dimensions, using the This function adds the operator for pooling in 2 dimensions, using the
pooling configurations mentioned in input parameters. pooling configurations mentioned in input parameters.
Args:
input (Variable): ${input_comment}
pool_size (int): ${ksize_comment}
pool_type (str): ${pooling_type_comment}
pool_stride (int): stride of the pooling layer.
pool_padding (int): padding size.
global_pooling (bool): ${global_pooling_comment}
use_cudnn (bool): ${use_cudnn_comment}
ceil_mode (bool): ${ceil_mode_comment}
use_mkldnn (bool): ${use_mkldnn_comment}
name (str): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: output of pool2d layer.
""" """
if pool_type not in ["max", "avg"]: if pool_type not in ["max", "avg"]:
raise ValueError( raise ValueError(
...@@ -1543,6 +1595,25 @@ def batch_norm(input, ...@@ -1543,6 +1595,25 @@ def batch_norm(input,
""" """
This function helps create an operator to implement This function helps create an operator to implement
the BatchNorm layer using the configurations from the input parameters. the BatchNorm layer using the configurations from the input parameters.
Args:
input (Variable): the input variable.
act (str): activation type
is_test (bool): whether to run batch_norm as test mode.
momentum (float): momentum
epsilon (float): epsilon, default 1e-05
param_attr (ParamAttr|None): attributes for parameter
bias_attr (ParamAttr|None): attributes for bias
data_layout (str): data layout, default NCHW
in_place (bool): if True, do not create tmp variable
use_mkldnn (bool): ${use_mkldnn_comment}
name (str): The name of this layer. It is optional.
moving_mean_name (str): The name of moving mean variable name, optional.
moving_variance_name (str): The name of moving variance name, optional.
do_model_average_for_mean_and_var (bool):
Returns:
Variable: output of batch_norm layer.
""" """
helper = LayerHelper('batch_norm', **locals()) helper = LayerHelper('batch_norm', **locals())
dtype = helper.input_dtype() dtype = helper.input_dtype()
...@@ -1670,6 +1741,7 @@ def layer_norm(input, ...@@ -1670,6 +1741,7 @@ def layer_norm(input,
bias_attr(ParamAttr|None): The parameter attribute for the learnable bias_attr(ParamAttr|None): The parameter attribute for the learnable
bias :math:`b`. bias :math:`b`.
act(str): Activation to be applied to the output of layer normalizaiton. act(str): Activation to be applied to the output of layer normalizaiton.
name (str): The name of this layer. It is optional.
Returns: Returns:
Variable: A tensor variable with the same shape as the input. Variable: A tensor variable with the same shape as the input.
...@@ -1721,6 +1793,17 @@ def layer_norm(input, ...@@ -1721,6 +1793,17 @@ def layer_norm(input,
def beam_search_decode(ids, scores, name=None): def beam_search_decode(ids, scores, name=None):
"""
${beam_search_decode}
Args:
ids (Variable): ${ids_comment}
scores (Variable): ${scores_comment}
name (str): The name of this layer. It is optional.
Returns:
tuple: a tuple of two output variable: sentence_ids, sentence_scores
"""
helper = LayerHelper('beam_search_decode', **locals()) helper = LayerHelper('beam_search_decode', **locals())
sentence_ids = helper.create_tmp_variable(dtype=ids.dtype) sentence_ids = helper.create_tmp_variable(dtype=ids.dtype)
sentence_scores = helper.create_tmp_variable(dtype=ids.dtype) sentence_scores = helper.create_tmp_variable(dtype=ids.dtype)
...@@ -1796,46 +1879,46 @@ def conv2d_transpose(input, ...@@ -1796,46 +1879,46 @@ def conv2d_transpose(input,
W_{out} &= (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1 W_{out} &= (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1
Args: Args:
input(Variable): The input image with [N, C, H, W] format. input(Variable): The input image with [N, C, H, W] format.
num_filters(int): The number of the filter. It is as same as the output num_filters(int): The number of the filter. It is as same as the output
image channel. image channel.
output_size(int|tuple|None): The output image size. If output size is a 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 tuple, it must contain two integers, (image_H, image_W). This
parameter only works when filter_size is None. parameter only works when filter_size is None.
filter_size(int|tuple|None): The filter size. If filter_size is a tuple, 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). it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square. None if use output size to Otherwise, the filter will be a square. None if use output size to
calculate filter_size. calculate filter_size.
padding(int|tuple): The padding size. If padding is a tuple, it must padding(int|tuple): The padding size. If padding is a tuple, it must
contain two integers, (padding_H, padding_W). Otherwise, the contain two integers, (padding_H, padding_W). Otherwise, the
padding_H = padding_W = padding. Default: padding = 0. padding_H = padding_W = padding. Default: padding = 0.
stride(int|tuple): The stride size. If stride is a tuple, it must stride(int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: stride = 1. stride_H = stride_W = stride. Default: stride = 1.
dilation(int|tuple): The dilation size. If dilation is a tuple, it must dilation(int|tuple): The dilation size. If dilation is a tuple, it must
contain two integers, (dilation_H, dilation_W). Otherwise, the contain two integers, (dilation_H, dilation_W). Otherwise, the
dilation_H = dilation_W = dilation. Default: dilation = 1. dilation_H = dilation_W = dilation. Default: dilation = 1.
groups(int): The groups number of the Conv2d transpose layer. Inspired by groups(int): The groups number of the Conv2d transpose layer. Inspired by
grouped convolution in Alex Krizhevsky's Deep CNN paper, in which grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
when group=2, the first half of the filters is only connected to the 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 first half of the input channels, while the second half of the
filters is only connected to the second half of the input channels. filters is only connected to the second half of the input channels.
Default: groups=1 Default: groups=1
param_attr(ParamAttr): The parameters to the Conv2d_transpose Layer. param_attr(ParamAttr): The parameters to the Conv2d_transpose Layer.
Default: None Default: None
bias_attr(ParamAttr): Bias parameter for the Conv2d layer. Default: None bias_attr(ParamAttr): Bias parameter for the Conv2d layer. Default: None
use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True library is installed. Default: True
act(str): Activation type. Default: None act(str): Activation type. Default: None
name(str|None): A name for this layer(optional). If set None, the layer name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically. will be named automatically.
Returns: Returns:
Variable: The tensor variable storing the convolution transpose result. Variable: The tensor variable storing the convolution transpose result.
Raises: Raises:
ValueError: If the shapes of input, filter_size, stride, padding and ValueError: If the shapes of input, filter_size, stride, padding and
groups mismatch. groups mismatch.
Examples: Examples:
.. code-block:: python .. code-block:: python
...@@ -1972,6 +2055,17 @@ def sequence_expand(x, y, ref_level=-1, name=None): ...@@ -1972,6 +2055,17 @@ def sequence_expand(x, y, ref_level=-1, name=None):
def beam_search(pre_ids, ids, scores, beam_size, end_id, level=0): def beam_search(pre_ids, ids, scores, beam_size, end_id, level=0):
''' '''
This function implements the beam search algorithm. This function implements the beam search algorithm.
Args:
pre_ids (Variable): ${pre_ids_comment}
ids (Variable): ${ids_comment}
scores (Variable): ${scores_comment}
beam_size (int): ${beam_size_comment}
end_id (int): ${end_id_comment}
level (int): ${level_comment}
Returns:
tuple: a tuple of beam_search output variables: selected_ids, selected_scores
''' '''
helper = LayerHelper('beam_search', **locals()) helper = LayerHelper('beam_search', **locals())
score_type = scores.dtype score_type = scores.dtype
...@@ -2474,14 +2568,14 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None): ...@@ -2474,14 +2568,14 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None):
slice along dimension `axis`. slice along dimension `axis`.
Args: Args:
x(Variable|list): The input tensor to l2_normalize layer. x(Variable|list): The input tensor to l2_normalize layer.
axis(int): The axis on which to apply normalization. If `axis < 0`, axis(int): The axis on which to apply normalization. If `axis < 0`,
the dimension to normalization is rank(X) + axis. -1 is the the dimension to normalization is rank(X) + axis. -1 is the
last dimension. last dimension.
epsilon(float): The epsilon value is used to avoid division by zero, epsilon(float): The epsilon value is used to avoid division by zero,
the defalut value is 1e-10. the defalut value is 1e-10.
name(str|None): A name for this layer(optional). If set None, the layer name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically. will be named automatically.
Returns: Returns:
...@@ -2694,16 +2788,13 @@ def edit_distance(input, label, normalized=True, ignored_tokens=None, ...@@ -2694,16 +2788,13 @@ def edit_distance(input, label, normalized=True, ignored_tokens=None,
the edit distance will be divided by the length of reference string. the edit distance will be divided by the length of reference string.
Args: Args:
input(Variable): The indices for hypothesis strings. input(Variable): The indices for hypothesis strings.
label(Variable): The indices for reference strings. label(Variable): The indices for reference strings.
normalized(bool): Indicated whether to normalize the edit distance by normalized(bool): Indicated whether to normalize the edit distance by
the length of reference string. the length of reference string.
ignored_tokens(list of int): Tokens that should be removed before ignored_tokens(list of int): Tokens that should be removed before
calculating edit distance. calculating edit distance.
name (str): The name of this layer. It is optional.
Returns: Returns:
Variable: sequence-to-sequence edit distance in shape [batch_size, 1]. Variable: sequence-to-sequence edit distance in shape [batch_size, 1].
...@@ -2793,10 +2884,10 @@ def ctc_greedy_decoder(input, blank, name=None): ...@@ -2793,10 +2884,10 @@ def ctc_greedy_decoder(input, blank, name=None):
where Lp is the sum of all input sequences' length and where Lp is the sum of all input sequences' length and
num_classes is the true number of classes. (not num_classes is the true number of classes. (not
including the blank label). including the blank label).
blank(int): the blank label index of Connectionist Temporal blank(int): the blank label index of Connectionist Temporal
Classification (CTC) loss, which is in thehalf-opened Classification (CTC) loss, which is in thehalf-opened
interval [0, num_classes + 1). interval [0, num_classes + 1).
name (str): The name of this layer. It is optional.
Returns: Returns:
Variable: CTC greedy decode result. If all the sequences in result were Variable: CTC greedy decode result. If all the sequences in result were
...@@ -2833,23 +2924,23 @@ def warpctc(input, label, blank=0, norm_by_times=False): ...@@ -2833,23 +2924,23 @@ def warpctc(input, label, blank=0, norm_by_times=False):
input tensor. input tensor.
Args: Args:
input(Variable): (LodTensor, default: LoDTensor<float>), input(Variable): (LodTensor, default: LoDTensor<float>),
the unscaled probabilities of variable-length sequences, the unscaled probabilities of variable-length sequences,
which is a 2-D Tensor with LoD information. 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 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. sequences' length and num_classes is the true number of classes.
(not including the blank label). (not including the blank label).
label(Variable): (LodTensor, default: LoDTensor<int>), the ground truth label(Variable): (LodTensor, default: LoDTensor<int>), the ground truth
of variable-length sequence, which is a 2-D Tensor with LoD 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 information. It is of the shape [Lg, 1], where Lg is th sum of
all labels' length. all labels' length.
blank: (int, default: 0), the blank label index of Connectionist blank (int): default 0, the blank label index of Connectionist
Temporal Classification (CTC) loss, which is in the Temporal Classification (CTC) loss, which is in the
half-opened interval [0, num_classes + 1). half-opened interval [0, num_classes + 1).
norm_by_times: (bool, default: false), whether to normalize norm_by_times (bool): default false, whether to normalize
the gradients by the number of time-step, which is also the the gradients by the number of time-step, which is also the
sequence's length. There is no need to normalize the gradients sequence's length. There is no need to normalize the gradients
if warpctc layer was follewed by a mean_op. if warpctc layer was follewed by a mean_op.
Returns: Returns:
Variable: The Connectionist Temporal Classification (CTC) loss, Variable: The Connectionist Temporal Classification (CTC) loss,
...@@ -2908,9 +2999,9 @@ def sequence_reshape(input, new_dim): ...@@ -2908,9 +2999,9 @@ def sequence_reshape(input, new_dim):
no remainder for each sequence. no remainder for each sequence.
Args: Args:
input (Variable): (LodTensor, default: LoDTensor<float>), a 2-D LoDTensor input (Variable): (LodTensor, default: LoDTensor<float>), a 2-D LoDTensor
with shape being [N, M] where M for dimension. with shape being [N, M] where M for dimension.
new_dim (int): New dimension which the input LoDTensor is reshaped to. new_dim (int): New dimension which the input LoDTensor is reshaped to.
Returns: Returns:
Variable: Reshaped LoDTensor according to new dimension. Variable: Reshaped LoDTensor according to new dimension.
...@@ -2932,7 +3023,10 @@ def sequence_reshape(input, new_dim): ...@@ -2932,7 +3023,10 @@ def sequence_reshape(input, new_dim):
return out return out
@autodoc() # FIXME(wuyi): let docstring_checker.py understand @autodoc.
# For now, the comments in c++ use types like Tensor, but in python side
# the type is often "Variable", and arguments may vary.
@templatedoc(op_type="nce")
def nce(input, def nce(input,
label, label,
num_total_classes, num_total_classes,
...@@ -2940,6 +3034,21 @@ def nce(input, ...@@ -2940,6 +3034,21 @@ def nce(input,
param_attr=None, param_attr=None,
bias_attr=None, bias_attr=None,
num_neg_samples=None): num_neg_samples=None):
"""
${comment}
Args:
input (Variable): input variable.
label (Variable): label.
num_total_classes (int):${num_total_classes_comment}
sample_weight (int): ${sample_weight_comment}
param_attr (ParamAttr|None): attributes for parameter
bias_attr (ParamAttr|None): attributes for bias
num_neg_samples (int): ${num_neg_samples_comment}
Returns:
Variable: output of nce layer.
"""
helper = LayerHelper('nce', **locals()) helper = LayerHelper('nce', **locals())
assert isinstance(input, Variable) assert isinstance(input, Variable)
dim = input.shape[1] dim = input.shape[1]
...@@ -2997,8 +3106,9 @@ def transpose(x, perm, name=None): ...@@ -2997,8 +3106,9 @@ def transpose(x, perm, name=None):
perm[i]-th dimension of `input`. perm[i]-th dimension of `input`.
Args: Args:
input (Variable): (Tensor), A Tensor. x (Variable): The input Tensor.
perm (list): A permutation of the dimensions of `input`. perm (list): A permutation of the dimensions of `input`.
name (str): The name of this layer. It is optional.
Returns: Returns:
Variable: A transposed Tensor. Variable: A transposed Tensor.
...@@ -3231,9 +3341,9 @@ def multiplex(inputs, index): ...@@ -3231,9 +3341,9 @@ def multiplex(inputs, index):
row of the matrix, then `O[i]` is equal to :math:`I_{ID[i]}[i]`. row of the matrix, then `O[i]` is equal to :math:`I_{ID[i]}[i]`.
Args: Args:
inputs (list): A list of variables to gather from. All variables have the inputs (list): A list of variables to gather from. All variables have the
same shape and the rank is at least 2. same shape and the rank is at least 2.
index (Variable): Tensor<int32>, index variable which is a 2-D tensor index (Variable): Tensor<int32>, index variable which is a 2-D tensor
with shape [M, 1] where M is the batch size. with shape [M, 1] where M is the batch size.
Returns: Returns:
...@@ -3432,7 +3542,8 @@ def autoincreased_step_counter(counter_name=None, begin=1, step=1): ...@@ -3432,7 +3542,8 @@ def autoincreased_step_counter(counter_name=None, begin=1, step=1):
begin(int): The first value of this counter. begin(int): The first value of this counter.
step(int): The increment step between each execution. step(int): The increment step between each execution.
Returns(Variable): The global run counter. Returns:
Variable: The global run counter.
""" """
helper = LayerHelper('global_step_counter') helper = LayerHelper('global_step_counter')
if counter_name is None: if counter_name is None:
...@@ -3493,7 +3604,7 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None): ...@@ -3493,7 +3604,7 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
the corresponding dimension of x. the corresponding dimension of x.
Args: Args:
input(variable): The input tensor. x(variable): The input tensor.
shape(list): The new shape. At most one dimension of the new shape can shape(list): The new shape. At most one dimension of the new shape can
be -1. be -1.
actual_shape(variable): An optional input. If provided, reshape actual_shape(variable): An optional input. If provided, reshape
...@@ -3505,8 +3616,10 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None): ...@@ -3505,8 +3616,10 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
inplace(bool): If this flag is set true, a new output tensor is created inplace(bool): If this flag is set true, a new output tensor is created
whose data is copied from input x, otherwise the output whose data is copied from input x, otherwise the output
shares data with input without copying. shares data with input without copying.
name (str): The name of this layer. It is optional.
Returns(variable): The output tensor. Returns:
Variable: The output tensor.
Examples: Examples:
.. code-block:: python .. code-block:: python
...@@ -4027,7 +4140,6 @@ def resize_bilinear(input, out_shape=None, scale=None, name=None): ...@@ -4027,7 +4140,6 @@ def resize_bilinear(input, out_shape=None, scale=None, name=None):
name(str|None): The output variable name. name(str|None): The output variable name.
Returns: Returns:
${out_comment}. ${out_comment}.
""" """
...@@ -4046,6 +4158,7 @@ def image_resize_short(input, out_short_len, resample='BILINEAR'): ...@@ -4046,6 +4158,7 @@ def image_resize_short(input, out_short_len, resample='BILINEAR'):
This is a 4-D tensor of the shape This is a 4-D tensor of the shape
(num_batches, channels, in_h, in_w). (num_batches, channels, in_h, in_w).
out_short_len(int): The length of output images' short edge. out_short_len(int): The length of output images' short edge.
resample (str): resample method, default: BILINEAR.
Returns: Returns:
out (Variable): The output is a 4-D tensor of the shape out (Variable): The output is a 4-D tensor of the shape
......
tools/codestyle/docstring_checker.py
浏览文件 @ 5100c4c3
...@@ -126,9 +126,10 @@ class DocstringChecker(BaseChecker): ...@@ -126,9 +126,10 @@ class DocstringChecker(BaseChecker):
'W9002': 'W9002':
('Doc string does not end with "." period', symbol + "-end-with", ('Doc string does not end with "." period', symbol + "-end-with",
'Used when a doc string does not end with a period'), 'Used when a doc string does not end with a period'),
'W9003': ('All args with their types must be mentioned in doc string', 'W9003':
symbol + "-with-all-args", ('All args with their types must be mentioned in doc string %s',
'Used when not all arguments are in the doc string '), symbol + "-with-all-args",
'Used when not all arguments are in the doc string '),
'W9005': ('Missing docstring or docstring is too short', 'W9005': ('Missing docstring or docstring is too short',
symbol + "-missing", 'Add docstring longer >=10'), symbol + "-missing", 'Add docstring longer >=10'),
'W9006': ('Docstring indent error, use 4 space for indent', 'W9006': ('Docstring indent error, use 4 space for indent',
...@@ -178,6 +179,8 @@ class DocstringChecker(BaseChecker): ...@@ -178,6 +179,8 @@ class DocstringChecker(BaseChecker):
self.indent_style(node) self.indent_style(node)
def missing_doc_string(self, node): def missing_doc_string(self, node):
if node.name.startswith("__") or node.name.startswith("_"):
return True
if node.tolineno - node.fromlineno <= 10: if node.tolineno - node.fromlineno <= 10:
return True return True
...@@ -199,12 +202,16 @@ class DocstringChecker(BaseChecker): ...@@ -199,12 +202,16 @@ class DocstringChecker(BaseChecker):
doc = node.doc doc = node.doc
lines = doc.splitlines() lines = doc.splitlines()
line_num = 0
for l in lines: for l in lines:
if line_num == 0:
continue
cur_indent = len(l) - len(l.lstrip()) cur_indent = len(l) - len(l.lstrip())
if cur_indent % indent != 0: if cur_indent % indent != 0:
self.add_message('W9006', node=node, line=node.fromlineno) self.add_message('W9006', node=node, line=node.fromlineno)
return False return False
line_num += 1
return True return True
...@@ -320,15 +327,19 @@ class DocstringChecker(BaseChecker): ...@@ -320,15 +327,19 @@ class DocstringChecker(BaseChecker):
return True return True
parsed_args = doc.args parsed_args = doc.args
args_not_documented = set(args) - set(parsed_args)
if len(args) > 0 and len(parsed_args) <= 0: if len(args) > 0 and len(parsed_args) <= 0:
print "debug:parsed args: ", parsed_args self.add_message(
self.add_message('W9003', node=node, line=node.fromlineno) 'W9003',
node=node,
line=node.fromlineno,
args=list(args_not_documented))
return False return False
for t in args: for t in args:
if t not in parsed_args: if t not in parsed_args:
print t, " with (type) not in ", parsed_args self.add_message(
self.add_message('W9003', node=node, line=node.fromlineno) 'W9003', node=node, line=node.fromlineno, args=[t, ])
return False return False
return True return True
tools/codestyle/pylint_pre_commit.hook
浏览文件 @ 5100c4c3
...@@ -7,13 +7,13 @@ DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" ...@@ -7,13 +7,13 @@ DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
export PYTHONPATH=$DIR:$PYTHONPATH export PYTHONPATH=$DIR:$PYTHONPATH
# The trick to remove deleted files: https://stackoverflow.com/a/2413151 # The trick to remove deleted files: https://stackoverflow.com/a/2413151
for file in $(git diff --cached --name-status | awk '$1 != "D" {print $2}'); do for file in $(git diff --name-status | awk '$1 != "D" {print $2}'); do
pylint --disable=all --load-plugins=docstring_checker \ pylint --disable=all --load-plugins=docstring_checker \
--enable=doc-string-one-line,doc-string-end-with,doc-string-with-all-args,doc-string-triple-quotes,doc-string-missing,doc-string-indent-error,doc-string-with-returns,doc-string-with-raises $file; --enable=doc-string-one-line,doc-string-end-with,doc-string-with-all-args,doc-string-triple-quotes,doc-string-missing,doc-string-indent-error,doc-string-with-returns,doc-string-with-raises $file;
TOTAL_ERRORS=$(expr $TOTAL_ERRORS + $?); TOTAL_ERRORS=$(expr $TOTAL_ERRORS + $?);
done done
#exit $TOTAL_ERRORS exit $TOTAL_ERRORS
#For now, just warning: #For now, just warning:
exit 0 #exit 0
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