提交 5be454bf 编写于 作者: Y yi.wu

polish docs

上级 8deff48d
......@@ -42,7 +42,7 @@ void FuseVarsOpHandle::RunImpl() {
out_t->ShareDataWith(out_tensor->Slice(s, s + numel));
s += numel;
}
this->RunAndRecordEvent([this] {});
this->RunAndRecordEvent([] {});
}
std::string FuseVarsOpHandle::Name() const { return "fuse vars"; }
......
......@@ -54,20 +54,20 @@ The output of this operator changes according to whether Input(Label) is given:
1. Input(Label) is given:
This happens in training. This operator is used to co-work with the chunk_eval
operator.
This happens in training. This operator is used to co-work with the chunk_eval
operator.
When Input(Label) is given, the crf_decoding operator returns a row vector
with shape [N x 1] whose values are fixed to be 0, indicating an incorrect
prediction, or 1 indicating a tag is correctly predicted. Such an output is the
input to chunk_eval operator.
When Input(Label) is given, the crf_decoding operator returns a row vector
with shape [N x 1] whose values are fixed to be 0, indicating an incorrect
prediction, or 1 indicating a tag is correctly predicted. Such an output is the
input to chunk_eval operator.
2. Input(Label) is not given:
This is the standard decoding process.
This is the standard decoding process.
The crf_decoding operator returns a row vector with shape [N x 1] whose values
range from 0 to maximum tag number - 1. Each element indicates an index of a
range from 0 to maximum tag number - 1, Each element indicates an index of a
predicted tag.
)DOC");
}
......
......@@ -141,6 +141,16 @@ class ROIPoolOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
ROIPool operator
Region of interest pooling (also known as RoI pooling) is to perform
is to perform max pooling on inputs of nonuniform sizes to obtain
fixed-size feature maps (e.g. 7*7).
The operator has three steps:
1. Dividing each region proposal into equal-sized sections with
the pooled_width and pooled_height
2. Finding the largest value in each section
3. Copying these max values to the output buffer
ROI Pooling for Faster-RCNN. The link below is a further introduction:
https://stackoverflow.com/questions/43430056/what-is-roi-layer-in-fast-rcnn
)DOC");
......
......@@ -42,6 +42,7 @@ class ScaleOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("Out", "(Tensor) Output tensor of scale operator.");
AddComment(R"DOC(
Scale operator
Multiply the input tensor with a float scalar to scale the input tensor.
$$Out = scale*X$$
)DOC");
......
......@@ -108,10 +108,35 @@ class BlockGuardServ(BlockGuard):
class ListenAndServ(object):
"""
ListenAndServ class.
ListenAndServ layer.
ListenAndServ class is used to wrap listen_and_serv op to create a server
which can receive variables from clients and run a block.
ListenAndServ is used to create a rpc server bind and listen
on specific TCP port, this server will run the sub-block when
received variables from clients.
Args:
endpoint(string): IP:port string which the server will listen on.
inputs(list): a list of variables that the server will get from clients.
fan_in(int): how many client are expected to report to this server, default: 1.
optimizer_mode(bool): whether to run the server as a parameter server, default: True.
Examples:
.. code-block:: python
with fluid.program_guard(main):
serv = layers.ListenAndServ(
"127.0.0.1:6170", ["X"], optimizer_mode=False)
with serv.do():
x = layers.data(
shape=[32, 32],
dtype='float32',
name="X",
append_batch_size=False)
fluid.initializer.Constant(value=1.0)(x, main.global_block())
layers.scale(x=x, scale=10.0, out=out_var)
self.server_exe = fluid.Executor(place)
self.server_exe.run(main)
"""
def __init__(self, endpoint, inputs, fan_in=1, optimizer_mode=True):
......
......@@ -869,10 +869,17 @@ def crf_decoding(input, param_attr, label=None):
return viterbi_path
@templatedoc()
def cos_sim(X, Y):
"""
This function performs the cosine similarity between two tensors
X and Y and returns that as the output.
${comment}
Args:
X(${X_type}): ${X_comment}
Y(${Y_type}): ${Y_comment}
Returns:
A Variable contains the output of this layer.
"""
helper = LayerHelper('cos_sim', **locals())
out = helper.create_tmp_variable(dtype=X.dtype)
......@@ -1059,14 +1066,25 @@ def square_error_cost(input, label):
return square_out
@templatedoc()
def chunk_eval(input,
label,
chunk_scheme,
num_chunk_types,
excluded_chunk_types=None):
"""
This function computes and outputs the precision, recall and
F1-score of chunk detection.
${comment}
Args:
input(Variable): ${Inference_comment}
label(Variable): ${Label_comment}
chunk_scheme(${chunk_scheme_type}): ${chunk_scheme_comment}
num_chunk_types(${num_chunk_types_type}): ${num_chunk_types_comment}
excluded_chunk_types(${excluded_chunk_types_type}): ${excluded_chunk_types_comment}
Returns(typle): a tuple of variables:
(precision, recall, f1_score, num_infer_chunks, num_label_chunks, num_correct_chunks)
"""
helper = LayerHelper("chunk_eval", **locals())
......@@ -1737,6 +1755,7 @@ def beam_search_decode(ids, scores, name=None):
return sentence_ids, sentence_scores
@templatedoc()
def conv2d_transpose(input,
num_filters,
output_size=None,
......@@ -1760,7 +1779,7 @@ def conv2d_transpose(input,
Parameters(dilations, strides, paddings) are two elements. These two elements
represent height and width, respectively. The details of convolution transpose
layer, please refer to the following explanation and references
`therein <http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf>`_.
`here <http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf>`_.
For each input :math:`X`, the equation is:
......@@ -2781,6 +2800,7 @@ def edit_distance(input, label, normalized=True, ignored_tokens=None,
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
......@@ -3451,8 +3471,9 @@ def one_hot(input, depth):
def autoincreased_step_counter(counter_name=None, begin=1, step=1):
"""
NOTE: The counter will be automatically increased by 1 every mini-batch
Return the run counter of the main program, which is started with 1.
Create an auto-increase variable
which will be automatically increased by 1 every mini-batch
Return the run counter of the main program, default is started from 1.
Args:
counter_name(str): The counter name, default is '@STEP_COUNTER@'.
......@@ -3866,34 +3887,20 @@ def label_smooth(label,
return smooth_label
@templatedoc()
def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
"""
Region of interest pooling (also known as RoI pooling) is to perform
is to perform max pooling on inputs of nonuniform sizes to obtain
fixed-size feature maps (e.g. 7*7).
The operator has three steps:
1. Dividing each region proposal into equal-sized sections with
the pooled_width and pooled_height
2. Finding the largest value in each section
3. Copying these max values to the output buffer
${comment}
Args:
input (Variable): The input for ROI pooling.
rois (Variable): ROIs (Regions of Interest) to pool over. It should
be a 2-D one level LoTensor of shape [num_rois, 4].
The layout is [x1, y1, x2, y2], where (x1, y1)
is the top left coordinates, and (x2, y2) is the
bottom right coordinates. The num_rois is the
total number of ROIs in this batch data.
pooled_height (integer): The pooled output height. Default: 1
pooled_width (integer): The pooled output width. Default: 1
spatial_scale (float): Multiplicative spatial scale factor. To
translate ROI coords from their input scale
to the scale used when pooling. Default: 1.0
input (Variable): ${X_comment}
rois (Variable): ${ROIs_comment}
pooled_height (integer): ${pooled_height_comment} Default: 1
pooled_width (integer): ${pooled_width_comment} Default: 1
spatial_scale (float): ${spatial_scale_comment} Default: 1.0
Returns:
pool_out (Variable): The output is a 4-D tensor of the shape
(num_rois, channels, pooled_h, pooled_w).
pool_out (Variable): ${Out_comment}.
Examples:
.. code-block:: python
......
......@@ -73,6 +73,7 @@ __all__ = [
'sum',
'polygon_box_transform',
'shape',
'iou_similarity',
] + __activations__
for _OP in set(__all__):
......
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