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未验证 提交 8dde7aea 编写于 作者: N Nyakku Shigure 提交者： GitHub
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[CodeStyle] trim trailing whitespace in .h, .cc, .cu, etc. (#46006)

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paddle/fluid/framework/custom_operator.cc
浏览文件 @ 8dde7aea
......@@ -511,9 +511,9 @@ class CustomOpMaker : public OpProtoAndCheckerMaker {
AddComment(R"DOC(
Custom Operator.
According to the Tensor operation function implemented by the user
independently of the framework, it is encapsulated into a framework
operator to adapt to various execution scenarios such as dynamic graph,
According to the Tensor operation function implemented by the user
independently of the framework, it is encapsulated into a framework
operator to adapt to various execution scenarios such as dynamic graph,
mode static graph mode, and inference mode.
)DOC");
......
paddle/fluid/framework/new_executor/executor_statistics.cc
浏览文件 @ 8dde7aea
......@@ -600,9 +600,9 @@ void StatisticsEngine::Log(const std::string& filepath) {
for (size_t idx = 0; idx < statistics_.size(); ++idx) {
const auto& evt_stat = statistics_[idx];
ofs << platform::string_format(std::string(R"JSON(
{
"statistical item" : "%s",
"total time(ns)" : %llu,
{
"statistical item" : "%s",
"total time(ns)" : %llu,
"total number of times" : %llu,
"normalization time(ns)" : %llu
},)JSON"),
......
paddle/fluid/operators/activation_op.cc
浏览文件 @ 8dde7aea
......@@ -607,7 +607,7 @@ class LogitOpMaker : public framework::OpProtoAndCheckerMaker {
"(float, default 1e-6f) the epsilon for input clamp bound")
.SetDefault(1e-6f);
AddComment(R"DOC(
Logit Operator.
Logit Operator.
this function is defined as follow:
$ logit=ln\left ( {\frac {x} {1-x}} \right ) $
......
paddle/fluid/operators/add_position_encoding_op.cc
浏览文件 @ 8dde7aea
......@@ -87,7 +87,7 @@ class AddPositionEncodingOpMaker : public framework::OpProtoAndCheckerMaker {
});
AddComment(R"DOC(
Add Position Encoding Operator.
The add position encoding calculates the output based on the input, alpha, beta.
The size of each dimension of the parameters checked in the infer-shape.
)DOC");
......
paddle/fluid/operators/addmm_op.cc
浏览文件 @ 8dde7aea
......@@ -77,7 +77,7 @@ class AddMMOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
AddMM Operator.
This operator is used to perform matrix multiplication for input $x$ and $y$ with coefficient $alpha$.
$input$ with coefficient $beta$ is added to the final result.
$input$ with coefficient $beta$ is added to the final result.
The equation is:
$$Out = alpha * x * y + beta * input$$
......
paddle/fluid/operators/affine_grid_op.cc
浏览文件 @ 8dde7aea
......@@ -177,7 +177,7 @@ class AffineGridOpMaker : public framework::OpProtoAndCheckerMaker {
[x_14, x_15, x_16]]
[[x_21, x_22, x_23]
[x_24, x_25, x_26]]]
OutputShape = [2, 3, 5, 5]
Step 1:
......@@ -185,12 +185,12 @@ class AffineGridOpMaker : public framework::OpProtoAndCheckerMaker {
Generate relative coordinates according to OutputShape.
The values of relative coordinates are in the interval between -1 and 1.
The shape of the relative coordinates is [2, H, W] as below:
C = [[[-1. -1. -1. -1. -1. ]
[-0.5 -0.5 -0.5 -0.5 -0.5]
[ 0. 0. 0. 0. 0. ]
[ 0.5 0.5 0.5 0.5 0.5]
[ 1. 1. 1. 1. 1. ]]
[ 1. 1. 1. 1. 1. ]]
[[-1. -0.5 0. 0.5 1. ]
[-1. -0.5 0. 0.5 1. ]
[-1. -0.5 0. 0.5 1. ]
......@@ -198,7 +198,7 @@ class AffineGridOpMaker : public framework::OpProtoAndCheckerMaker {
[-1. -0.5 0. 0.5 1. ]]]
C[0] is the coordinates in height axis and C[1] is the coordinates in
width axis.
Step2:
Tanspose and reshape C to shape [H * W, 2] and append ones to last
dimension. The we get:
......
paddle/fluid/operators/allclose_op.cc
浏览文件 @ 8dde7aea
......@@ -47,7 +47,7 @@ class AllcloseOpMaker : public framework::OpProtoAndCheckerMaker {
"compared as equal. Default: :math:`False` .")
.SetDefault(false);
AddComment(R"DOC(
AddComment(R"DOC(
This operator checks if all :math:`x` and :math:`y` satisfy the condition:
.. math::
......@@ -110,7 +110,7 @@ REGISTER_OP_VERSION(allclose)
"The added input 'Atol' is not"
"dispensable."))
.AddCheckpoint(
R"ROC(Delete two float attributes [rtol] and [atol],
R"ROC(Delete two float attributes [rtol] and [atol],
then add 2 string attributes [atol, rtol]. Don't be surprised.
This is because float cannot represent hight-precision
floating-point values, and our framework doesn't support
......
paddle/fluid/operators/amp/check_finite_and_unscale_op.cc
浏览文件 @ 8dde7aea
......@@ -69,8 +69,8 @@ Check if input X contains all finite data, if yes, scale it by input Scale.
$$Out = X / scale$$
If any tensor in X contains Inf or Nan, the Out will generate a indicator.
FoundInfinite will be 1 (True), and Out will not be scaled. In this case, the data of
Out should not be used, and its data may not be deterministic.
FoundInfinite will be 1 (True), and Out will not be scaled. In this case, the data of
Out should not be used, and its data may not be deterministic.
Otherwise, FoundInfinite will be 0 (False).
)DOC");
......
paddle/fluid/operators/amp/update_loss_scaling_op.cc
浏览文件 @ 8dde7aea
......@@ -111,8 +111,8 @@ class UpdateLossScalingOpMaker : public framework::OpProtoAndCheckerMaker {
"Stop updating loss scaling, and just zero inputs.")
.SetDefault(false);
AddComment(R"DOC(
Update loss scaling according to overall gradients. If all gradients is
finite after incr_every_n_steps, loss scaling will increase by incr_ratio.
Update loss scaling according to overall gradients. If all gradients is
finite after incr_every_n_steps, loss scaling will increase by incr_ratio.
Otherwise, loss scaling will decrease by decr_ratio after
decr_every_n_nan_or_inf steps and each step some gradients are infinite.
......
paddle/fluid/operators/argsort_op.cc
浏览文件 @ 8dde7aea
......@@ -58,9 +58,9 @@ class ArgsortOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Argsort operator
Performs sorting on the input tensor along the given axis and outputs two
tensors, Output(Out) and Output(Indices). They reserve the same shape
with Input(X), and Output(Out) represents the sorted tensor while
Performs sorting on the input tensor along the given axis and outputs two
tensors, Output(Out) and Output(Indices). They reserve the same shape
with Input(X), and Output(Out) represents the sorted tensor while
Output(Indices) gives the sorted order along the given axis Attr(axis).
)DOC");
......
paddle/fluid/operators/array_to_lod_tensor_op.cc
浏览文件 @ 8dde7aea
......@@ -223,10 +223,10 @@ class ArrayToLoDTensorOpProtoMaker : public framework::OpProtoAndCheckerMaker {
"'paddle/framework/lod_rank_table.h' for more details.");
AddOutput("Out", "(LoDTensor) The LoDTensor formed by input tensor array.");
AddComment(
R"DOC(This Op build a big LoDTensor from a std::vector<LoDTensor>
R"DOC(This Op build a big LoDTensor from a std::vector<LoDTensor>
and a LoDRankTable. It is supposed to be used in getting dynamic RNN's
outputs back to a normal LoDTensor. The std::vector<LoDTensor>
would be the output of RNN Op and the LoDRankTable would be build
outputs back to a normal LoDTensor. The std::vector<LoDTensor>
would be the output of RNN Op and the LoDRankTable would be build
with RNN's input.)DOC");
}
};
......
paddle/fluid/operators/assign_pos_op.cc
浏览文件 @ 8dde7aea
......@@ -62,7 +62,7 @@ class AssignPosOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
assign_pos_op Operator.
Assign pos decides which tokens should be fetched belong to
Assign pos decides which tokens should be fetched belong to
specially counter orderingly.
)DOC");
......
paddle/fluid/operators/attention_lstm_op.cc
浏览文件 @ 8dde7aea
......@@ -297,7 +297,7 @@ tmp(seqlen*(M+D)) * fc((M+D)*1) => fcout(seqlen*1) with bias, relu
fcout(seqlen*1) * scalar => fcout(seqlen*1) with bias, relu
dotmul and sum pool ( fcout(seqlen*1), x(seqlen * M) ) => lstm_x_t(1, M)
dotmul and sum pool ( fcout(seqlen*1), x(seqlen * M) ) => lstm_x_t(1, M)
LSTM part:
use lstm_x_t as input and compute as standard LSTM.
......
paddle/fluid/operators/bmm_op.cc
浏览文件 @ 8dde7aea
......@@ -44,8 +44,8 @@ class BmmOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("Out", "(Tensor), The output tensor of Bmm op.");
AddComment(R"DOC(
The Bmm operator is used to perform batched matrix multiplication
over the last two dimensions of the input tensors `X` and `Y`
which are both 3-dimentionsal.
over the last two dimensions of the input tensors `X` and `Y`
which are both 3-dimentionsal.
Examples:
- X: [B, M, K], Y: [B, K, N] => Out: [B, M, N]
......
paddle/fluid/operators/broadcast_tensors_op.cc
浏览文件 @ 8dde7aea
......@@ -54,7 +54,7 @@ class BroadcastTensorsOpMaker : public framework::OpProtoAndCheckerMaker {
"consistent with :code:`x`.")
.AsDuplicable();
AddComment(
R"DOC(This OP is used to broadcast a vector of inputs
R"DOC(This OP is used to broadcast a vector of inputs
with Tensor or LoDTensor type, following broadcast semantics.)DOC");
}
};
......
paddle/fluid/operators/center_loss_op.cc
浏览文件 @ 8dde7aea
......@@ -80,10 +80,10 @@ class CenterLossOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<bool>("need_update", "whether need to update center info.");
AddComment(R"DOC(
**CenterLoss operator**
implemention of the center loss function in the papper<<A Discriminative
implemention of the center loss function in the papper<<A Discriminative
Feature Learning Approach for Deep Face Recognition>>, equations in this implement
is:loss = 1/2 * (x-y)^2 ,where x(X) means the deep feature(output of last hidden layer )
and y(Label) the target label
and y(Label) the target label
)DOC");
}
};
......
paddle/fluid/operators/channel_shuffle_op.cc
浏览文件 @ 8dde7aea
......@@ -52,9 +52,9 @@ class ChannelShuffleOpMaker : public framework::OpProtoAndCheckerMaker {
while keeping the original tensor shape.
Please refer to the paper:
`ShuffleNet: An Extremely Efficient Convolutional Neural Network for
`ShuffleNet: An Extremely Efficient Convolutional Neural Network for
Mobile Devices <https://arxiv.org/abs/1707.01083>`_
by Zhang et. al (2017) for more details.
by Zhang et. al (2017) for more details.
)DOC");
}
......
paddle/fluid/operators/chunk_eval_op.cc
浏览文件 @ 8dde7aea
......@@ -145,7 +145,7 @@ For some basics of chunking, please refer to
ChunkEvalOp computes the precision, recall, and F1-score of chunk detection,
and supports IOB, IOE, IOBES and IO (also known as plain) tagging schemes.
Here is a NER example of labeling for these tagging schemes:
Li Ming works at Agricultural Bank of China in Beijing.
IO I-PER I-PER O O I-ORG I-ORG I-ORG I-ORG O I-LOC
IOB B-PER I-PER O O B-ORG I-ORG I-ORG I-ORG O B-LOC
......@@ -158,13 +158,13 @@ and LOC(LOCATION), and we can see that the labels have the form <tag type>-<chun
Since the calculations actually use label ids rather than labels, extra attention
should be paid when mapping labels to ids to make CheckEvalOp work. The key point
is that the listed equations are satisfied by ids.
tag_type = label % num_tag_type
chunk_type = label / num_tag_type
where `num_tag_type` is the num of tag types in the tagging scheme, `num_chunk_type`
is the num of chunk types, and `tag_type` get its value from the following table.
Scheme Begin Inside End Single
plain 0 - - -
IOB 0 1 - -
......
paddle/fluid/operators/cinn/cinn_instruction_run_op.cc
浏览文件 @ 8dde7aea
......@@ -94,7 +94,7 @@ CINN(https://github.com/PaddlePaddle/CINN/blob/develop/README.md) instruction ex
Both the input and output of this operator are a set of variables
which are the input and output arguments of the bound cinn instruction respectively.
In addition, there is an attribute named 'cached_index' should be
set necessarily to get the CinnCompiledObject where the instruction is included
set necessarily to get the CinnCompiledObject where the instruction is included
and 'instruction_index' is fetch the instruction object from complied runtime prograrm.
It accomplishes the execution of the instruction according to the following steps:
......
paddle/fluid/operators/class_center_sample_op.cc
浏览文件 @ 8dde7aea
......@@ -75,8 +75,8 @@ class ClassCenterSampleOpMaker : public framework::OpProtoAndCheckerMaker {
The process of sampling subset class centers is straightforward: 1) First select the positive class centers;
2) Randomly sample negative class centers. Specifically, given a Label tensor, shape [batch_size], select all
the positive class centers and randomly sample negative class centers, then remap the input label tensor using
the sampled class centers. Note that if the number of the positive class centers is greater than the input
num_samples, it keeps all the positive class centers and the shape of SampledLocalClassCenter will be
the sampled class centers. Note that if the number of the positive class centers is greater than the input
num_samples, it keeps all the positive class centers and the shape of SampledLocalClassCenter will be
[num_positive_class_centers]. The op supports CPU, single GPU and multi GPU.
For more information, Partial FC: Training 10 Million Identities on a Single Machine
......
paddle/fluid/operators/collective/global_scatter_op.cc
浏览文件 @ 8dde7aea
......@@ -80,8 +80,8 @@ class GlobalScatterOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("Out", "(Tensor) the result of global_scatter.");
AddComment(R"DOC(
Global Scatter Operator
Scatter data in X which has been put together belong to one expert
to n_expert * world_size exeperts according to local_count
Scatter data in X which has been put together belong to one expert
to n_expert * world_size exeperts according to local_count
and receive tensors from n_expert * world_size experts according
to global_count.
)DOC");
......
paddle/fluid/operators/complex_view_op.cc
浏览文件 @ 8dde7aea
......@@ -41,8 +41,8 @@ class AsComplexOpMaker : public framework::OpProtoAndCheckerMaker {
As_complex Operator.
This operator is used to return a complex tensor represented
by an old-fashioned real tensor. The size of the last dimension of
the input tensor should be 2, which corresponds to 'real' and
by an old-fashioned real tensor. The size of the last dimension of
the input tensor should be 2, which corresponds to 'real' and
'complex', respectively.
)DOC");
......@@ -75,7 +75,7 @@ class AsRealOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
AsReal Operator.
This operator is used to return an old-fashioned real tensor from a
This operator is used to return an old-fashioned real tensor from a
complex tensor. The size of the last dimension of the output tensor is 2,
which corresponds to 'real' and 'complex', respectively.
......
paddle/fluid/operators/controlflow/compare_all_op.cc
浏览文件 @ 8dde7aea
......@@ -38,7 +38,7 @@ class CompareReduceOpProtoMaker : public framework::OpProtoAndCheckerMaker {
comment.equation));
AddComment(string::Sprintf(R"DOC(
It operates element-wise on X and Y, and returns the Out. X, Y is a
N-dim tensor, which could be any type. If all element $%s$, the Out tensor
N-dim tensor, which could be any type. If all element $%s$, the Out tensor
is [True], else [False]
)DOC",
comment.equation));
......
paddle/fluid/operators/controlflow/depend_op.cc
浏览文件 @ 8dde7aea
......@@ -73,7 +73,7 @@ b = opA(a)
y = opB(x)
if tensor b and tensor x has some inner dependency, for example, x share data with b,
we need to add explicit dependency for x <- b, otherwise the these two operators may
we need to add explicit dependency for x <- b, otherwise the these two operators may
be executed parellel in static graph. We can use depend op as below,
b = opA(a)
......
paddle/fluid/operators/copy_cross_scope_op.cc
浏览文件 @ 8dde7aea
......@@ -140,9 +140,9 @@ class CopyCrossScopeOpMaker : public framework::OpProtoAndCheckerMaker {
.SetDefault(false);
AddAttr<int>("num_micro_batches", "Number of micro batches for pipeline.");
AddComment(R"DOC(
This op is used by pipeline to copy tensors across micro batch scopes.
Copy the variable value of the giving Id's micro scope to the micro scope of Id + 1 position.
If need to copy back to the main scope, using to_main_scope option to copy the variable value of
This op is used by pipeline to copy tensors across micro batch scopes.
Copy the variable value of the giving Id's micro scope to the micro scope of Id + 1 position.
If need to copy back to the main scope, using to_main_scope option to copy the variable value of
the current micro scope to the main scope.
)DOC");
}
......
paddle/fluid/operators/crf_decoding_op.cc
浏览文件 @ 8dde7aea
......@@ -58,9 +58,9 @@ class CRFDecodingOpMaker : public framework::OpProtoAndCheckerMaker {
.AsDispensable();
AddComment(R"DOC(
The crf_decoding operator reads the emission feature weights and the transition
feature weights learned by the linear_chain_crf operator and performs decoding.
It implements the Viterbi algorithm which is a dynamic programming algorithm
for finding the most likely sequence of hidden states, called the Viterbi path,
feature weights learned by the linear_chain_crf operator and performs decoding.
It implements the Viterbi algorithm which is a dynamic programming algorithm
for finding the most likely sequence of hidden states, called the Viterbi path,
that results in a sequence of observed tags.
The output of this operator changes according to whether Input(Label) is given:
......@@ -68,15 +68,15 @@ 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.
When Input(Label) is given, the crf_decoding operator returns tensor with the
sampe shape as Input(Label) whose values are fixed to be 0, indicating an
incorrect prediction, or 1 indicating a tag is correctly predicted. Such an
When Input(Label) is given, the crf_decoding operator returns tensor with the
sampe shape as Input(Label) 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.
The crf_decoding operator returns a row vector with shape [N x 1]/[B x S], here
The crf_decoding operator returns a row vector with shape [N x 1]/[B x S], here
the shape depends on the inputs are LoDTensors or common tensors, whose values
range from 0 to maximum tag number - 1, Each element indicates an index of a
predicted tag.
......
paddle/fluid/operators/crop_op.cc
浏览文件 @ 8dde7aea
......@@ -102,14 +102,14 @@ Crop Operator.
Crop input into output, as specified by offsets and shape.
There are two ways to set the offsets:
1. In runtime: Using the input 'Offsets', which is a Variable and can be
output of other operators. This way is suitable for
1. In runtime: Using the input 'Offsets', which is a Variable and can be
output of other operators. This way is suitable for
dynamic offsets.
2. In network configuration: Using the attribute 'offsets', which will be
set in Python configure script. This way is
2. In network configuration: Using the attribute 'offsets', which will be
set in Python configure script. This way is
suitable for fixed offsets.
You CANNOT use these two ways at the same time. An exception will be raised
if input 'Offset' is configured and meanwhile the attribute 'offsets' is
You CANNOT use these two ways at the same time. An exception will be raised
if input 'Offset' is configured and meanwhile the attribute 'offsets' is
not empty.
There are two ways to set shape:
......
paddle/fluid/operators/crop_tensor_op.cc
浏览文件 @ 8dde7aea
......@@ -180,26 +180,26 @@ CropTensor Operator.
Crop input into output, as specified by offsets and shape.
There are three ways to set the offsets:
1. Input 'OffsetsTensor: It is a tensor list. It should be set as a list that
contains tensor variable in python configure script.
1. Input 'OffsetsTensor: It is a tensor list. It should be set as a list that
contains tensor variable in python configure script.
This way is suitable for dynamic offsets.
2. Input 'Offsets': It is a variable and can be output of other operators.
2. Input 'Offsets': It is a variable and can be output of other operators.
This way is suitable for dynamic offsets.
3. Attribute 'offsets': It will be set in python configure script. This way
3. Attribute 'offsets': It will be set in python configure script. This way
is suitable for fixed offsets.
You CANNOT use these three ways at the same time. An exception will be raised
if input 'OffsetsTensor' or 'Offset' is configured and meanwhile the attribute 'offsets' is
You CANNOT use these three ways at the same time. An exception will be raised
if input 'OffsetsTensor' or 'Offset' is configured and meanwhile the attribute 'offsets' is
not empty.
There are three ways to set shape:
1. Input 'ShapeTensor': It is a tensor list. It should be set as a list that contains
tensor variable in python configure script. This way is suitable
tensor variable in python configure script. This way is suitable
for dynamic shape.
2. Input 'Shape': It is a Variable and can be output of other operators. This way is suitable
2. Input 'Shape': It is a Variable and can be output of other operators. This way is suitable
for dynamic shape.
2. Attribute 'shape': crop input X into the shape described by a list<int>. The size of shape
list should be the same as the dimension size of input X. This way is
2. Attribute 'shape': crop input X into the shape described by a list<int>. The size of shape
list should be the same as the dimension size of input X. This way is
suitable for fixed shape.
The input should be a k-D tensor(k > 0 and k < 7). As an example:
......
paddle/fluid/operators/cross_entropy_op.cc
浏览文件 @ 8dde7aea
......@@ -250,10 +250,10 @@ class CrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
CrossEntropy Operator.
The input 'X' and 'Label' will first be logically flattened to 2-D matrixs.
The matrix's second dimension(row length) is as same as the original last
dimension, and the first dimension(column length) is the product of all other
original dimensions. Then the softmax computation will take palce on each raw
The input 'X' and 'Label' will first be logically flattened to 2-D matrixs.
The matrix's second dimension(row length) is as same as the original last
dimension, and the first dimension(column length) is the product of all other
original dimensions. Then the softmax computation will take palce on each raw
of flattened matrixs.
It supports both standard cross-entropy and soft-label cross-entropy loss
......@@ -385,10 +385,10 @@ class CrossEntropyOpMaker2 : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Hard-label CrossEntropy Operator.
The input 'X' and 'Label' will first be logically flattened to 2-D matrixs.
The matrix's second dimension(row length) is as same as the original last
dimension, and the first dimension(column length) is the product of all other
original dimensions. Then the softmax computation will take palce on each raw
The input 'X' and 'Label' will first be logically flattened to 2-D matrixs.
The matrix's second dimension(row length) is as same as the original last
dimension, and the first dimension(column length) is the product of all other
original dimensions. Then the softmax computation will take palce on each raw
of flattened matrixs.
Only support hard label.
......
paddle/fluid/operators/ctc_align_op.cc
浏览文件 @ 8dde7aea
......@@ -93,12 +93,12 @@ Then:
Output.dims = {8, 1}
Output.LoD = [[0, 6, 8]]
or Given:
Input.data = [[0, 1, 2, 2, 0, 4],
[0, 4, 5, 0, 6, 0],
Input.data = [[0, 1, 2, 2, 0, 4],
[0, 4, 5, 0, 6, 0],
[0, 7, 7, 7, 0, 0]]
InputLength.data = [[6],
[5],
[4]],
[4]],
Input.dims = {3, 6},
Input.Lod = []
And:
......
paddle/fluid/operators/cudnn_lstm_op.cc
浏览文件 @ 8dde7aea
......@@ -190,7 +190,7 @@ class CudnnLSTMOpMaker : public framework::OpProtoAndCheckerMaker {
CUDNN LSTM implementation
A four-gate Long Short-Term Memory network with no peephole connections.
In the forward pass the output ht and cell output ct for a given iteration can be computed from the recurrent input ht-1,
In the forward pass the output ht and cell output ct for a given iteration can be computed from the recurrent input ht-1,
the cell input ct-1 and the previous layer input xt given matrices W, R and biases bW, bR from the following equations:
$$ i_t = sigmoid(W_{ix}x_{t} + W_{ih}h_{t-1} + bx_i + bh_i) $$
......@@ -217,7 +217,7 @@ $$ h_t = o_t \\odot tanh(c_t) $$
- $\tilde{c_t}$ is also called candidate hidden state,
which is computed based on the current input and the previous hidden state.
Where sigmoid is the sigmoid operator: sigmoid(x) = 1 / (1 + e^-x), * represents a point-wise multiplication,
Where sigmoid is the sigmoid operator: sigmoid(x) = 1 / (1 + e^-x), * represents a point-wise multiplication,
X represensts a matrix multiplication
......
paddle/fluid/operators/cumprod_op.cc
浏览文件 @ 8dde7aea
......@@ -35,7 +35,7 @@ class CumprodOpMaker : public framework::OpProtoAndCheckerMaker {
"（int), The dim along which the input tensors will be cumproded");
AddComment(
R"DOC(Cumprod operator. Return the cumprod results of the input elements along the dim.
For example, if input X is a tensor with rank 1 and N elements, the output will also be a tensor
For example, if input X is a tensor with rank 1 and N elements, the output will also be a tensor
with rank 1 and N elements, and elements y[i] = x[0] * x[1] * x[2] *...* x[i] (0<=i<N))DOC");
}
};
......
paddle/fluid/operators/decode_jpeg_op.cc
浏览文件 @ 8dde7aea
......@@ -61,9 +61,9 @@ class DecodeJpegOpMaker : public framework::OpProtoAndCheckerMaker {
"of the JPEG image. It is a tensor with rank 1.");
AddOutput("Out", "The output tensor of DecodeJpeg op");
AddComment(R"DOC(
This operator decodes a JPEG image into a 3 dimensional RGB Tensor
or 1 dimensional Gray Tensor. Optionally converts the image to the
desired format. The values of the output tensor are uint8 between 0
This operator decodes a JPEG image into a 3 dimensional RGB Tensor
or 1 dimensional Gray Tensor. Optionally converts the image to the
desired format. The values of the output tensor are uint8 between 0
and 255.
)DOC");
AddAttr<std::string>(
......
paddle/fluid/operators/deformable_conv_v1_op.cc
浏览文件 @ 8dde7aea
......@@ -73,13 +73,13 @@ class DeformableConvV1OpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
**Deformable Convolution v1 Operator**
Deformable Convolution is a new method based Convolution which feature has offset
Deformable Convolution is a new method based Convolution which feature has offset
in spatial location.
1. Get offset of each pixel in feature map with convolution layers which number
1. Get offset of each pixel in feature map with convolution layers which number
of channels should be double of weight size.
2. Add offset to pixel to get new location and the new value which are computed
2. Add offset to pixel to get new location and the new value which are computed
directly through bilinear interpolation with four nearest pixel.
3. Get the product of pixel and weight as result
......
paddle/fluid/operators/deformable_psroi_pooling_op.cc
浏览文件 @ 8dde7aea
......@@ -104,7 +104,7 @@ class DeformablePSROIPoolOpMaker : public framework::OpProtoAndCheckerMaker {
"W is thewidth of output. ");
AddComment(R"DOC(
**DeformablePSROIPooling Operator**
DeformablePSROIPooling is a new method based Region of interest pooling
DeformablePSROIPooling is a new method based Region of interest pooling
(also known as RoI pooling).
The operator has four steps:
......
paddle/fluid/operators/detection/box_clip_op.cc
浏览文件 @ 8dde7aea
......@@ -82,14 +82,14 @@ This operator clips input boxes to original input images.
For each input box, The formula is given as follows:
$$xmin = \max(\min(xmin, im_w - 1), 0)$$
$$ymin = \max(\min(ymin, im_h - 1), 0)$$
$$ymin = \max(\min(ymin, im_h - 1), 0)$$
$$xmax = \max(\min(xmax, im_w - 1), 0)$$
$$ymax = \max(\min(ymax, im_h - 1), 0)$$
where im_w and im_h are computed from ImInfo, the formula is given as follows:
$$im_w = \round(width / im_scale)$$
$$im_h = \round(height / im_scale)$$
$$im_h = \round(height / im_scale)$$
)DOC");
}
};
......
paddle/fluid/operators/detection/box_coder_op.cc
浏览文件 @ 8dde7aea
......@@ -98,9 +98,9 @@ The Encoding schema described below:
oy = (ty - py) / ph / pyv
ow = log(abs(tw / pw)) / pwv
ow = log(abs(tw / pw)) / pwv
oh = log(abs(th / ph)) / phv
oh = log(abs(th / ph)) / phv
The Decoding schema described below:
......@@ -116,11 +116,11 @@ where `tx`, `ty`, `tw`, `th` denote the target box's center coordinates, width
and height respectively. Similarly, `px`, `py`, `pw`, `ph` denote the
priorbox's (anchor) center coordinates, width and height. `pxv`, `pyv`, `pwv`,
`phv` denote the variance of the priorbox and `ox`, `oy`, `ow`, `oh` denote the
encoded/decoded coordinates, width and height.
encoded/decoded coordinates, width and height.
During Box Decoding, two modes for broadcast are supported. Say target box has
During Box Decoding, two modes for broadcast are supported. Say target box has
shape [N, M, 4], and the shape of prior box can be [N, 4] or [M, 4]. Then prior
box will broadcast to target box along the assigned axis.
box will broadcast to target box along the assigned axis.
)DOC");
}
};
......
paddle/fluid/operators/detection/box_decoder_and_assign_op.cc
浏览文件 @ 8dde7aea
......@@ -189,7 +189,7 @@ Decode the target bounding box with the prior_box information.
The Decoding schema is described below:
$$
ox = (pw \\times pxv \\times tx + px) - \\frac{tw}{2}
ox = (pw \\times pxv \\times tx + px) - \\frac{tw}{2}
$$
$$
oy = (ph \\times pyv \\times ty + py) - \\frac{th}{2}
......@@ -205,11 +205,11 @@ where `tx`, `ty`, `tw`, `th` denote the target box's center coordinates, width
and height respectively. Similarly, `px`, `py`, `pw`, `ph` denote the
prior_box's (anchor) center coordinates, width and height. `pxv`, `pyv`, `pwv`,
`phv` denote the variance of the prior_box and `ox`, `oy`, `ow`, `oh` denote the
decoded coordinates, width and height in decode_box.
decoded coordinates, width and height in decode_box.
decode_box is obtained after box decode, then assigning schema is described below:
For each prior_box, use the best non-background class's decoded values to
For each prior_box, use the best non-background class's decoded values to
update the prior_box locations and get output_assign_box. So, the shape of
output_assign_box is the same as PriorBox.
)DOC");
......
paddle/fluid/operators/detection/collect_fpn_proposals_op.cc
浏览文件 @ 8dde7aea
......@@ -125,7 +125,7 @@ class CollectFpnProposalsOpMaker : public framework::OpProtoAndCheckerMaker {
This operator concats all proposals from different images
and different FPN levels. Then sort all of those proposals
by objectness confidence. Select the post_nms_topN RoIs in
total. Finally, re-sort the RoIs in the order of batch index.
total. Finally, re-sort the RoIs in the order of batch index.
)DOC");
}
};
......@@ -145,7 +145,7 @@ REGISTER_OP_CPU_KERNEL(collect_fpn_proposals,
REGISTER_OP_VERSION(collect_fpn_proposals)
.AddCheckpoint(
R"ROC(
Upgrade collect_fpn_proposals add a new input
Upgrade collect_fpn_proposals add a new input
[MultiLevelRoIsNum] and add a new output [RoisNum].)ROC",
paddle::framework::compatible::OpVersionDesc()
.NewInput("MultiLevelRoIsNum",
......
paddle/fluid/operators/detection/generate_proposals_v2_op.cc
浏览文件 @ 8dde7aea
......@@ -86,7 +86,7 @@ class GenerateProposalsV2OpMaker : public framework::OpProtoAndCheckerMaker {
"If true, im_shape pixel offset is 1.")
.SetDefault(true);
AddComment(R"DOC(
This operator is the second version of generate_proposals op to generate
This operator is the second version of generate_proposals op to generate
bounding box proposals for Faster RCNN.
The proposals are generated for a list of images based on image
score 'Scores', bounding box regression result 'BboxDeltas' as
......@@ -96,9 +96,9 @@ boxes.
The difference between this version and the first version is that the image
scale is no long needed now, so the input requires im_shape instead of im_info.
The change aims to unify the input for all kinds of objective detection
such as YOLO-v3 and Faster R-CNN. As a result, the min_size represents the
size on input image instead of original image which is slightly different
The change aims to unify the input for all kinds of objective detection
such as YOLO-v3 and Faster R-CNN. As a result, the min_size represents the
size on input image instead of original image which is slightly different
to before and will not effect the result.
)DOC");
......
paddle/fluid/operators/detection/iou_similarity_op.cc
浏览文件 @ 8dde7aea
......@@ -95,7 +95,7 @@ boxes in 'Y' are shared by all instance of the batched inputs of X.
Given two boxes A and B, the calculation of IOU is as follows:
$$
IOU(A, B) =
IOU(A, B) =
\\frac{area(A\\cap B)}{area(A)+area(B)-area(A\\cap B)}
$$
......
paddle/fluid/operators/detection/matrix_nms_op.cc
浏览文件 @ 8dde7aea
......@@ -116,7 +116,7 @@ independently for each class. The outputs is a 2-D LoDTenosr, for each
image, the offsets in first dimension of LoDTensor are called LoD, the number
of offset is N + 1, where N is the batch size. If LoD[i + 1] - LoD[i] == 0,
means there is no detected bbox for this image. Now this operator has one more
output, which is RoisNum. The size of RoisNum is N, RoisNum[i] means the number of
output, which is RoisNum. The size of RoisNum is N, RoisNum[i] means the number of
detected bbox for this image.
For more information on Matrix NMS, please refer to:
......
paddle/fluid/operators/detection/mine_hard_examples_op.cc
浏览文件 @ 8dde7aea
......@@ -383,11 +383,11 @@ class MineHardExamplesOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Mine hard examples Operator.
This operator implements hard example mining to select a subset of negative box indices.
For each image, selects the box with highest losses. subject to the condition that the
box cannot have an Matcht > neg_dist_threshold when mining_type is max_negative.
The selected number is min(sample_size, max_negative_box_number) when mining_type is
hard_example, or min(neg_pos_ratio * positive_box_number, max_negative_box_number)
when mining_type is max_negative, where the max_negative_box_number is the count of
For each image, selects the box with highest losses. subject to the condition that the
box cannot have an Matcht > neg_dist_threshold when mining_type is max_negative.
The selected number is min(sample_size, max_negative_box_number) when mining_type is
hard_example, or min(neg_pos_ratio * positive_box_number, max_negative_box_number)
when mining_type is max_negative, where the max_negative_box_number is the count of
MatchIndices elements with value -1.
)DOC");
}
......
paddle/fluid/operators/detection/retinanet_detection_output_op.cc
浏览文件 @ 8dde7aea
......@@ -640,7 +640,7 @@ where `tx`, `ty`, `tw`, `th` denote the predicted box's center coordinates, widt
and height respectively. Similarly, `px`, `py`, `pw`, `ph` denote the
anchor's center coordinates, width and height. `pxv`, `pyv`, `pwv`,
`phv` denote the variance of the anchor box and `ox`, `oy`, `ow`, `oh` denote the
decoded coordinates, width and height.
decoded coordinates, width and height.
Then the top decoded prediction from all levels are merged followed by NMS.
In the NMS step, this operator prunes away boxes that have high IOU
......
paddle/fluid/operators/detection/rpn_target_assign_op.cc
浏览文件 @ 8dde7aea
......@@ -661,7 +661,7 @@ The rest anchors would not contibute to the RPN training loss
ScoreIndex is composed of foreground anchor indexes(positive labels) and
background anchor indexes(negative labels). LocationIndex is exactly same
as the foreground anchor indexes since we can not assign regression target to
as the foreground anchor indexes since we can not assign regression target to
the background anchors.
The classification targets(TargetLabel) is a binary class label (of being
......@@ -730,16 +730,16 @@ class RetinanetTargetAssignOpMaker : public framework::OpProtoAndCheckerMaker {
This layer can be, for given the Intersection-over-Union (IoU) overlap
between anchors and ground truth boxes, to assign classification and
regression targets to each anchor, these target labels are used for
train retinanet.
train retinanet.
Every anchor is assigned with a length C one-hot vector of
classification targets, and a 4-vector of box regression targets,
where C is the class number. The assignment rules are as followed:
1. Anchors are assigned to ground-truth boxes when: (i) it has the highest
IoU overlap with a ground-truth box, or (ii) it has an IoU overlap higher
than positive_overlap(0.5) with any ground-truth box.
2. Anchors are assigned to background when its IoU ratio is lower than
negative_overlap (0.4) for all ground-truth boxes.
......
paddle/fluid/operators/detection/target_assign_op.cc
浏览文件 @ 8dde7aea
......@@ -131,7 +131,7 @@ If id = MatchIndices[i][j] > 0,
Out[i][j][0 : K] = X[lod[i] + id][j % P][0 : K]
OutWeight[i][j] = 1.
Otherwise,
Otherwise,
Out[j][j][0 : K] = {mismatch_value, mismatch_value, ...}
OutWeight[i][j] = 0.
......
paddle/fluid/operators/detection/yolo_box_op.cc
浏览文件 @ 8dde7aea
......@@ -192,19 +192,19 @@ class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
.SetDefault(0.5);
AddComment(R"DOC(
This operator generates YOLO detection boxes from output of YOLOv3 network.
The output of previous network is in shape [N, C, H, W], while H and W
should be the same, H and W specify the grid size, each grid point predict
should be the same, H and W specify the grid size, each grid point predict
given number boxes, this given number, which following will be represented as S,
is specified by the number of anchors. In the second dimension(the channel
dimension), C should be equal to S * (5 + class_num) if :attr:`iou_aware` is false,
otherwise C should be equal to S * (6 + class_num). class_num is the object
category number of source dataset(such as 80 in coco dataset), so the
second(channel) dimension, apart from 4 box location coordinates x, y, w, h,
also includes confidence score of the box and class one-hot key of each anchor
category number of source dataset(such as 80 in coco dataset), so the
second(channel) dimension, apart from 4 box location coordinates x, y, w, h,
also includes confidence score of the box and class one-hot key of each anchor
box.
Assume the 4 location coordinates are :math:`t_x, t_y, t_w, t_h`, the box
Assume the 4 location coordinates are :math:`t_x, t_y, t_w, t_h`, the box
predictions should be as follows:
$$
......@@ -225,9 +225,9 @@ class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
The logistic regression value of the 5th channel of each anchor prediction boxes
represents the confidence score of each prediction box, and the logistic
regression value of the last :attr:`class_num` channels of each anchor prediction
regression value of the last :attr:`class_num` channels of each anchor prediction
boxes represents the classifcation scores. Boxes with confidence scores less than
:attr:`conf_thresh` should be ignored, and box final scores is the product of
:attr:`conf_thresh` should be ignored, and box final scores is the product of
confidence scores and classification scores.
$$
......
paddle/fluid/operators/detection/yolov3_loss_op.cc
浏览文件 @ 8dde7aea
......@@ -105,14 +105,14 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
This operator generates yolov3 loss based on given predict result and ground
truth boxes.
The output of previous network is in shape [N, C, H, W], while H and W
should be the same, H and W specify the grid size, each grid point predict
should be the same, H and W specify the grid size, each grid point predict
given number bounding boxes, this given number, which following will be represented as S,
is specified by the number of anchor clusters in each scale. In the second dimension(the channel
dimension), C should be equal to S * (class_num + 5), class_num is the object
category number of source dataset(such as 80 in coco dataset), so in the
second(channel) dimension, apart from 4 box location coordinates x, y, w, h,
dimension), C should be equal to S * (class_num + 5), class_num is the object
category number of source dataset(such as 80 in coco dataset), so in the
second(channel) dimension, apart from 4 box location coordinates x, y, w, h,
also includes confidence score of the box and class one-hot key of each anchor box.
Assume the 4 location coordinates are :math:`t_x, t_y, t_w, t_h`, the box predictions
......@@ -135,21 +135,21 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
and :math:`p_w, p_h` is specified by anchors.
As for confidence score, it is the logistic regression value of IoU between
anchor boxes and ground truth boxes, the score of the anchor box which has
the max IoU should be 1, and if the anchor box has IoU bigger than ignore
anchor boxes and ground truth boxes, the score of the anchor box which has
the max IoU should be 1, and if the anchor box has IoU bigger than ignore
thresh, the confidence score loss of this anchor box will be ignored.
Therefore, the yolov3 loss consists of three major parts: box location loss,
objectness loss and classification loss. The L1 loss is used for
box coordinates (w, h), sigmoid cross entropy loss is used for box
objectness loss and classification loss. The L1 loss is used for
box coordinates (w, h), sigmoid cross entropy loss is used for box
coordinates (x, y), objectness loss and classification loss.
Each groud truth box finds a best matching anchor box in all anchors.
Each groud truth box finds a best matching anchor box in all anchors.
Prediction of this anchor box will incur all three parts of losses, and
prediction of anchor boxes with no GT box matched will only incur objectness
loss.
In order to trade off box coordinate losses between big boxes and small
In order to trade off box coordinate losses between big boxes and small
boxes, box coordinate losses will be mutiplied by scale weight, which is
calculated as follows.
......@@ -165,12 +165,12 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
$$
While :attr:`use_label_smooth` is set to be :attr:`True`, the classification
target will be smoothed when calculating classification loss, target of
target will be smoothed when calculating classification loss, target of
positive samples will be smoothed to :math:`1.0 - 1.0 / class\_num` and target of
negetive samples will be smoothed to :math:`1.0 / class\_num`.
While :attr:`GTScore` is given, which means the mixup score of ground truth
boxes, all losses incured by a ground truth box will be multiplied by its
While :attr:`GTScore` is given, which means the mixup score of ground truth
boxes, all losses incured by a ground truth box will be multiplied by its
mixup score.
)DOC");
}
......
paddle/fluid/operators/dgc_op.cc
浏览文件 @ 8dde7aea
......@@ -126,10 +126,10 @@ class DGCOpMaker : public framework::OpProtoAndCheckerMaker {
DGC also uses momentum factor masking and warmup training to overcome the staleness problem caused by reduced communication.
This optimizer will do two things:
1. Compress the gradient by get TopK import value from tensor \
and use it for allreduce to reduce network bandwidth.
2. Call momentum to optimize on the cost.
)DOC");
......
paddle/fluid/operators/diag_embed_op.cc
浏览文件 @ 8dde7aea
......@@ -47,11 +47,11 @@ class DiagEmbedOpMaker : public framework::OpProtoAndCheckerMaker {
)DOC")
.SetDefault(-1);
AddComment(R"DOC(Creates a tensor whose diagonals of certain 2D planes
(specified by dim1 and dim2) are filled by input.
To facilitate creating batched diagonal matrices,
AddComment(R"DOC(Creates a tensor whose diagonals of certain 2D planes
(specified by dim1 and dim2) are filled by input.
To facilitate creating batched diagonal matrices,
the 2D planes formed by the last two dimensions of the returned tensor
are chosen by default.
are chosen by default.
)DOC");
}
};
......
paddle/fluid/operators/diag_op.cc
浏览文件 @ 8dde7aea
......@@ -45,7 +45,7 @@ class DiagOpMaker : public framework::OpProtoAndCheckerMaker {
"Diagonal values of square matrix. It is a tensor with rank 1.");
AddOutput("Out", "A square matrix.");
AddComment(R"DOC(
Return a square matrix with specified diagonal values.
Return a square matrix with specified diagonal values.
)DOC");
}
};
......
paddle/fluid/operators/edit_distance_op.cc
浏览文件 @ 8dde7aea
......@@ -65,7 +65,7 @@ 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 another.
The operations include insertion, deletion, and substitution.
The operations include insertion, deletion, and substitution.
For example, given hypothesis string A = "kitten" and reference B = "sitting",
A will be transformed into B at least after two substitutions and one
......
paddle/fluid/operators/fill_any_op.cc
浏览文件 @ 8dde7aea
......@@ -30,7 +30,7 @@ class FillAnyOpMaker : public framework::OpProtoAndCheckerMaker {
.SetDefault(0);
AddAttr<int>("value_int", "The int var to fill in Tensor").SetDefault(0);
AddComment(R"DOC(Fill operator with backward;
Fill an tensor with `value`.
Fill an tensor with `value`.
)DOC");
};
};
......
paddle/fluid/operators/filter_by_instag_op.cc
浏览文件 @ 8dde7aea
......@@ -80,15 +80,15 @@ class FilterByInstagOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("LossWeight", "(Tensor) loss weight.");
AddOutput("IndexMap", "(LoDTensor) mapping from Out rows to X1 rows");
AddComment(R"DOC(
Filter By Instag Op
Filter By Instag Op
This operator is used to filter embeded ins.
There are 3 inputs. First is embeded ins, Second is tags for ins,
There are 3 inputs. First is embeded ins, Second is tags for ins,
Third is tags to filter.
There are 3 outputs. First is filtered embeded ins, Second is Loss Weight,
Third is the IndexMap from Out line number to X1 line number.
Third is the IndexMap from Out line number to X1 line number.
)DOC");
}
};
......
paddle/fluid/operators/fold_op.cc
浏览文件 @ 8dde7aea
......@@ -70,9 +70,9 @@ class FoldOpMaker : public framework::OpProtoAndCheckerMaker {
**Fold Operator**
This Operator is used to combines an array of sliding local blocks into a large containing
tensor. also known as col2im when operated on batched 2D image tensor. Fold calculates each
combined value in the resulting large tensor by summing all values from all containing blocks.
Unfold extracts the values in the local blocks by copying from the large tensor. So, if the
tensor. also known as col2im when operated on batched 2D image tensor. Fold calculates each
combined value in the resulting large tensor by summing all values from all containing blocks.
Unfold extracts the values in the local blocks by copying from the large tensor. So, if the
blocks overlap, they are not inverses of each other.
)DOC");
}
......
paddle/fluid/operators/fused/fused_attention_op.cc
浏览文件 @ 8dde7aea
......@@ -432,8 +432,8 @@ class FusedAttentionOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
The fused_attention operator is the same as following pseudo codes:
// @input: [batch_size, seq_len, embed_dim]
// @final_out: [batch_size, seq_len, num_heads, head_dim]
// @input: [batch_size, seq_len, embed_dim]
// @final_out: [batch_size, seq_len, num_heads, head_dim]
residual = input
if (pre_layernorm)
query = layer_norm(input);
......@@ -447,7 +447,7 @@ class FusedAttentionOpMaker : public framework::OpProtoAndCheckerMaker {
out = dropout(out);
out = out * v;
out = transpose(out, perm=[0, 2, 1, 3]);
}
// out linear
out = linear(out);
......
paddle/fluid/operators/fused/fused_bias_dropout_residual_layer_norm_op.cc
浏览文件 @ 8dde7aea
......@@ -140,8 +140,8 @@ class FusedBiasDropoutResidualLnOpMaker
AddComment(R"DOC(
Add fused bias_dropout_residual_layer_norm op whose logic is as follows:
// @input: [batch_size, seq_len, embed_dim]
// @final_out: [batch_size, seq_len, embed_dim]
// @input: [batch_size, seq_len, embed_dim]
// @final_out: [batch_size, seq_len, embed_dim]
y = layer_norm(residual + dropout(bias + x));
)DOC");
}
......
paddle/fluid/operators/fused/fused_gate_attention_op.cc
浏览文件 @ 8dde7aea
......@@ -174,7 +174,7 @@ class FusedGateAttentionOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Add fused attention op whose logic is as follows:
{
q = paddle.einsum('nbqa,ahc->nbqhc', q_data, self.query_w)
q = paddle.einsum('nbqa,ahc->nbqhc', q_data, self.query_w)
k = paddle.einsum('nbka,ahc->nbkhc', m_data, self.key_w)
v = paddle.einsum('nbka,ahc->nbkhc', m_data, self.value_w)
......@@ -189,10 +189,10 @@ class FusedGateAttentionOpMaker : public framework::OpProtoAndCheckerMaker {
self.gating_w) + self.gating_b
gate_values_1 = nn.functional.sigmoid(gate_values)
weighted_avg *= gate_values_1
output = paddle.einsum('nbqhc,hco->nbqo', weighted_avg,
self.output_w) + self.output_b
}
)DOC");
}
......
paddle/fluid/operators/fused/fused_gemm_epilogue_op.cc
浏览文件 @ 8dde7aea
......@@ -164,32 +164,32 @@ class FusedGemmEpilogueOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("Out", "The output tensor Out of Out = Act((X * Y) + Bias).");
AddOutput("ReserveSpace",
R"DOC(Reserve GPU space to place
auxiliary data pointer. It is used to pass auxiliary data pointer
for fused_gemm_epilogue op. If not given (empty string), the
R"DOC(Reserve GPU space to place
auxiliary data pointer. It is used to pass auxiliary data pointer
for fused_gemm_epilogue op. If not given (empty string), the
auxiliary mode would not be enable.)DOC")
.AsDispensable()
.AsExtra();
AddAttr<bool>(
"trans_x",
R"DOC((bool, default false), Whether to transpose input tensor X
or not. The input tensor X coulbe be more than two dimension. When
set trans_x=true, it would fully reverse X. For instant: X with shpae
R"DOC((bool, default false), Whether to transpose input tensor X
or not. The input tensor X coulbe be more than two dimension. When
set trans_x=true, it would fully reverse X. For instant: X with shpae
[d0, d1, d2, d3] -> [d3, d2, d1, d0].)DOC")
.SetDefault(false);
AddAttr<bool>(
"trans_y",
R"DOC((bool, default false), Whether to transpose input tensor Y
or not. The input tensor Y should be two dimension. When
set trans_y=true, it would transpose Y. For instant: Y with shpae
R"DOC((bool, default false), Whether to transpose input tensor Y
or not. The input tensor Y should be two dimension. When
set trans_y=true, it would transpose Y. For instant: Y with shpae
[d0, d1] -> [d1, d0].)DOC")
.SetDefault(false);
AddAttr<std::string>(
"activation",
R"DOC((string, default none), The activation function. It could be
one of {none, relu, gelu}. When none is given, Act would be null
R"DOC((string, default none), The activation function. It could be
one of {none, relu, gelu}. When none is given, Act would be null
operations)DOC")
.SetDefault("none");
......@@ -337,9 +337,9 @@ class FusedGemmEpilogueGradOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("X", "The input tensor X of Out = (Act(X) * Y) + bias");
AddInput("Y", "The input tensor Y of Out = (Act(X) * Y) + bias");
AddInput("ReserveSpace",
R"DOC(A GPU space to fetch
auxiliary data pointer. It is used to pass auxiliary data pointer
for fused_gemm_epilogue_grad op. If not given (empty string), the
R"DOC(A GPU space to fetch
auxiliary data pointer. It is used to pass auxiliary data pointer
for fused_gemm_epilogue_grad op. If not given (empty string), the
auxiliary mode would not be enable.)DOC")
.AsDispensable();
......@@ -352,23 +352,23 @@ class FusedGemmEpilogueGradOpMaker : public framework::OpProtoAndCheckerMaker {
.AsDispensable();
AddAttr<bool>(
"trans_x",
R"DOC((bool, default false), Whether to transpose input tensor X
or not. The input tensor X coulbe be more than two dimension. When
set trans_x=true, it would fully reverse X. For instant: X with shpae
R"DOC((bool, default false), Whether to transpose input tensor X
or not. The input tensor X coulbe be more than two dimension. When
set trans_x=true, it would fully reverse X. For instant: X with shpae
[d0, d1, d2, d3] -> [d3, d2, d1, d0].)DOC")
.SetDefault(false);
AddAttr<bool>(
"trans_y",
R"DOC((bool, default false), Whether to transpose input tensor Y
or not. The input tensor Y should be two dimension. When
set trans_y=true, it would transpose Y. For instant: Y with shpae
R"DOC((bool, default false), Whether to transpose input tensor Y
or not. The input tensor Y should be two dimension. When
set trans_y=true, it would transpose Y. For instant: Y with shpae
[d0, d1] -> [d1, d0].)DOC")
.SetDefault(false);
AddAttr<std::string>(
"activation_grad",
R"DOC((string, default none), The backward activation function. It could be
one of {none, relu_grad, gelu_grad}. When none is given, The backward Act would
R"DOC((string, default none), The backward activation function. It could be
one of {none, relu_grad, gelu_grad}. When none is given, The backward Act would
be null operations)DOC")
.SetDefault("none");
......
paddle/fluid/operators/fused/fusion_gru_op.cc
浏览文件 @ 8dde7aea
......@@ -251,7 +251,7 @@ void FusionGRUOpMaker::Make() {
.SetDefault(false);
AddComment(R"DOC(
The Fusion complete GRU Operator.
This operator fuse the fully-connected operator into GRU,
This operator fuse the fully-connected operator into GRU,
more details can refer to GRU op.
)DOC");
}
......
paddle/fluid/operators/fused/fusion_squared_mat_sub_op.cc
浏览文件 @ 8dde7aea
......@@ -79,7 +79,7 @@ void FusionSquaredMatSubOpMaker::Make() {
AddAttr<float>("scalar", "The scalar on output matrix.").SetDefault(1.f);
AddComment(R"DOC(
Fusion Squared Matrix and substrct operator.
( (X * Y).^2 - (X.^2 * Y.^2) ) .* scalar
)DOC");
}
......
paddle/fluid/operators/fused/multi_gru_op.cc
浏览文件 @ 8dde7aea
......@@ -219,7 +219,7 @@ void MultiGRUOpMaker::Make() {
.SetDefault(false);
AddComment(R"DOC(
The Fusion complete GRU Operator.
This operator fuse the fully-connected operator into GRU,
This operator fuse the fully-connected operator into GRU,
more details can refer to GRU op.
)DOC");
}
......
paddle/fluid/operators/fused/resnet_unit_op.cc
浏览文件 @ 8dde7aea
......@@ -274,10 +274,10 @@ class ResNetUnitOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<std::string>("act_type", "The activation type to be fused.")
.SetDefault("relu");
AddComment(R"DOC(
Fusion op of the basic unit of resnet block.
Fusion op of the basic unit of resnet block.
The implementation is based on the latest fusion op interface in cuDNN v8.0.
For more details:
For more details:
https://docs.nvidia.com/deeplearning/cudnn/api/index.html#cudnnFusedOps_t
)DOC");
......
paddle/fluid/operators/fused_token_prune_op.cc
浏览文件 @ 8dde7aea
......@@ -81,7 +81,7 @@ class FusedTokenPruneOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
fused_token_prune op is used to fuse multiple ops to perform token pruning.
In this op:
1. Elements of Attn will be set to zero if their corresponding mask is smaller than 0.
1. Elements of Attn will be set to zero if their corresponding mask is smaller than 0.
2. The second dimension of X will be sorted by Attn.
3. The last (max_seq_len - slimmed_seq_len) lines of X will be pruned.
4. The remainning part of sorted X will output.
......
paddle/fluid/operators/gather_nd_op.cc
浏览文件 @ 8dde7aea
......@@ -59,13 +59,13 @@ class GatherNdOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Gather_Nd Operator.
This function is actually a high-dimensional extension of gather
and supports for simultaneous indexing by multiple axes. Out is
obtained by gathering slices from X into a tensor with shape
This function is actually a high-dimensional extension of gather
and supports for simultaneous indexing by multiple axes. Out is
obtained by gathering slices from X into a tensor with shape
Index.shape[:-1] + X.shape[Index.shape[-1]:].
Example:
Given:
X = [[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
......@@ -73,7 +73,7 @@ class GatherNdOpMaker : public framework::OpProtoAndCheckerMaker {
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]]
X.shape = (2, 3, 4)
*Case 1:
......@@ -81,7 +81,7 @@ class GatherNdOpMaker : public framework::OpProtoAndCheckerMaker {
Index = [[1]]
we get:
Out =
Out =
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]
......@@ -91,7 +91,7 @@ class GatherNdOpMaker : public framework::OpProtoAndCheckerMaker {
Index = [[0,2]]
we get:
Out = [8, 9, 10, 11]
*Case 3:
......
paddle/fluid/operators/gaussian_random_op.cc
浏览文件 @ 8dde7aea
......@@ -161,7 +161,7 @@ REGISTER_OP_CPU_KERNEL(gaussian_random_batch_size_like,
REGISTER_OP_VERSION(gaussian_random)
.AddCheckpoint(
R"ROC(
Upgrade gaussian_random add new inputs [ShapeTensor] and [ShapeTensorList]
Upgrade gaussian_random add new inputs [ShapeTensor] and [ShapeTensorList]
and modify the attribute of [shape])ROC",
paddle::framework::compatible::OpVersionDesc()
.NewInput("ShapeTensor",
......
paddle/fluid/operators/gelu_op.cc
浏览文件 @ 8dde7aea
......@@ -100,7 +100,7 @@ class GeluOpMaker : public framework::OpProtoAndCheckerMaker {
"(bool, default false) use approximation of gelu")
.SetDefault(false);
AddComment(R"DOC(
Gelu Activation Operator.
Gelu Activation Operator.
For more details, please refer to [Gaussian Error Linear Units](https://arxiv.org/pdf/1606.08415.pdf).
......
paddle/fluid/operators/graph_send_recv_op.cc
浏览文件 @ 8dde7aea
......@@ -83,10 +83,10 @@ Graph Learning Send_Recv combine operator.
$Out = Recv(Send(X, Src_index), Dst_index, reduce_op)$
This operator is mainly used in Graph Learning domain, and the main purpose is to reduce
intermediate memory consumption in the process of message passing.
Take `x` as the input tensor, we first use `src_index` to gather corresponding data,
and then use `dst_index` to update the corresponding position of output tensor in different
This operator is mainly used in Graph Learning domain, and the main purpose is to reduce
intermediate memory consumption in the process of message passing.
Take `x` as the input tensor, we first use `src_index` to gather corresponding data,
and then use `dst_index` to update the corresponding position of output tensor in different
pooling types, like sum, mean, max, or min.
)DOC");
......
paddle/fluid/operators/graph_send_ue_recv_op.cc
浏览文件 @ 8dde7aea
......@@ -97,7 +97,7 @@ intermediate memory consumption in the process of message passing.
Take `X` as the input tensor, we first use `src_index` to gather corresponding data.
Then the gather data should compute with `Y` in different message_ops, like add, sub, mul, and div,
and get the computation result. Then, use `dst_index` to update the corresponding position of output
and get the computation result. Then, use `dst_index` to update the corresponding position of output
tensor in different pooling types, like sum, mean, max, or min.
)DOC");
......
paddle/fluid/operators/grid_sampler_op.cc
浏览文件 @ 8dde7aea
......@@ -89,12 +89,12 @@ class GridSampleOpMaker : public framework::OpProtoAndCheckerMaker {
.SetDefault("zeros");
AddComment(R"DOC(
This operation samples input X by using bilinear or nearest interpolation based on
This operation samples input X by using bilinear or nearest interpolation based on
flow field grid, which is usually generated by affine_grid. The grid of
shape [N, H, W, 2] is the concatenation of (grid_x, grid_y) coordinates
with shape [N, H, W] each, where grid_x is indexing the 4th dimension
(in width dimension) of input data x and grid_y is indexing the 3rd
dimension (in height dimension), finally results is the bilinear
shape [N, H, W, 2] is the concatenation of (grid_x, grid_y) coordinates
with shape [N, H, W] each, where grid_x is indexing the 4th dimension
(in width dimension) of input data x and grid_y is indexing the 3rd
dimension (in height dimension), finally results is the bilinear
interpolation value or nearest value of 4 nearest corner points.
For bilinear interpolation mode:
......@@ -105,7 +105,7 @@ class GridSampleOpMaker : public framework::OpProtoAndCheckerMaker {
grid_y = 0.5 * (grid[:, :, :, 1] + 1) * (H - 1)
Step 2:
Indices input data X with grid (x, y) in each [H, W] area, and bilinear
Indices input data X with grid (x, y) in each [H, W] area, and bilinear
interpolate point value by 4 nearest points.
wn ------- y_n ------- en
......
paddle/fluid/operators/hash_op.cc
浏览文件 @ 8dde7aea
......@@ -63,7 +63,7 @@ class HashOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("X", "(Tensor) Input tensor of hash operator.");
AddOutput("Out", "(Tensor) Output tensor of hash operator.");
AddComment(R"DOC(
Execute `num_hash` times xxHash algorithm on all elements on second dimension of input.
Execute `num_hash` times xxHash algorithm on all elements on second dimension of input.
)DOC");
AddAttr<int>("num_hash", "").SetDefault(1);
AddAttr<int64_t>("mod_by", "").SetDefault(100000);
......
paddle/fluid/operators/hinge_loss_op.cc
浏览文件 @ 8dde7aea
......@@ -82,7 +82,7 @@ take any values from (-inf, inf), but the labels should be either -1 or 1.
Then, the hinge loss is computed as follows:
$$
L_(x, y) = max(1 - y.x, 0)
L_(x, y) = max(1 - y.x, 0)
$$
Note that the labels passed as input will have values as either 0 or 1.
......
paddle/fluid/operators/increment_op.cc
浏览文件 @ 8dde7aea
......@@ -61,7 +61,7 @@ class IncrementOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Increment Operator.
The equation is:
The equation is:
$$Out = X + step$$
)DOC");
......
paddle/fluid/operators/index_sample_op.cc
浏览文件 @ 8dde7aea
......@@ -30,10 +30,10 @@ class IndexSampleOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("Out", "Return the element of input at index");
AddComment(R"DOC(
IndexSample OP returns the element of the specified location of X,
and the location is specified by Index.
IndexSample OP returns the element of the specified location of X,
and the location is specified by Index.
X tensor and Index tensor's shape must be 2-D,
X tensor and Index tensor's shape must be 2-D,
dimension at 0 which usually is batch size must be equal.
The returned tensor has the same shape and dimensions as the Index tensor.
......
paddle/fluid/operators/interpolate_op.cc
浏览文件 @ 8dde7aea
......@@ -452,25 +452,25 @@ class InterpolateOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
This operator samples input X to given output shape by using specified
interpolation method, the interpolation methods can be \"nearest\"
for nearest neighbor interpolation and \"bilinear\" for bilinear
for nearest neighbor interpolation and \"bilinear\" for bilinear
interpolation and \"linear\" for linear interpolation..
Nearest neighbor interpolation is to perform nearest neighbor interpolation
in both the 3rd dimension(in height direction) and the 4th dimension(in width
in both the 3rd dimension(in height direction) and the 4th dimension(in width
direction) on input tensor.
Linear interpolation is the method of using a line connecting two known quantities
to determine the value of an unknown quantity between the two known quantities.
Bilinear interpolation is an extension of linear interpolation for
interpolating functions of two variables (e.g. H-direction and
W-direction in this op) on a rectilinear 2D grid. The key idea is
to perform linear interpolation first in one direction, and then
Linear interpolation is the method of using a line connecting two known quantities
to determine the value of an unknown quantity between the two known quantities.
Bilinear interpolation is an extension of linear interpolation for
interpolating functions of two variables (e.g. H-direction and
W-direction in this op) on a rectilinear 2D grid. The key idea is
to perform linear interpolation first in one direction, and then
again in the other direction.
Trilinear interpolation is an extension of linear interpolation for
interpolating functions of three variables (e.g. D-direction,
H-direction and W-direction in this op) on a rectilinear 3D grid.
Trilinear interpolation is an extension of linear interpolation for
interpolating functions of three variables (e.g. D-direction,
H-direction and W-direction in this op) on a rectilinear 3D grid.
The linear interpolation is performed on three directions.
Bicubic interpolation is an extension of cubic interpolation for interpolating
......@@ -478,24 +478,24 @@ class InterpolateOpMaker : public framework::OpProtoAndCheckerMaker {
smoother than corresponding surfaces obtained by bilinear interpolation or
nearest-neighbor interpolation.
Align_corners and align_mode are optional parameters,the calculation method
Align_corners and align_mode are optional parameters,the calculation method
of interpolation can be selected by them.
Example:
For scale:
if align_corners = True and out_{size}>1 :
scale_{factor} = (in_{size}-1.0)/(out_{size}-1.0)
else:
scale_{factor} = float(in_{size}/out_{size})
Nearest neighbor interpolation:
if:
align_corners = False
......@@ -518,16 +518,16 @@ class InterpolateOpMaker : public framework::OpProtoAndCheckerMaker {
if:
align_corners = False , align_mode = 0
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = (H_{in}+0.5) * scale_{factor} - 0.5
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
......@@ -538,17 +538,17 @@ class InterpolateOpMaker : public framework::OpProtoAndCheckerMaker {
if:
align_corners = False , align_mode = 0
input : (N,C,D_in,H_in,W_in)
output: (N,C,D_out,H_out,W_out) where:
D_out = (D_{in}+0.5) * scale_{factor} - 0.5
H_out = (H_{in}+0.5) * scale_{factor} - 0.5
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,D_in,H_in,W_in)
output: (N,C,D_out,H_out,W_out) where:
......@@ -570,13 +570,13 @@ class InterpolateOpMaker : public framework::OpProtoAndCheckerMaker {
H_out = H_{in} * scale_{factor}
W_out = W_{in} * scale_{factor}
For details of nearest neighbor interpolation, please refer to Wikipedia:
For details of nearest neighbor interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation
For details of bilinear interpolation, please refer to Wikipedia:
For details of bilinear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bilinear_interpolation
For details of trilinear interpolation, please refer to Wikipedia:
For details of trilinear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Trilinear_interpolation
For details of bicubic interpolation, please refer to Wikipedia:
......
paddle/fluid/operators/interpolate_v2_op.cc
浏览文件 @ 8dde7aea
......@@ -553,25 +553,25 @@ class InterpolateV2OpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
This operator samples input X to given output shape by using specified
interpolation method, the interpolation methods can be \"nearest\"
for nearest neighbor interpolation and \"bilinear\" for bilinear
for nearest neighbor interpolation and \"bilinear\" for bilinear
interpolation and \"linear\" for linear interpolation..
Nearest neighbor interpolation is to perform nearest neighbor interpolation
in both the 3rd dimension(in height direction) and the 4th dimension(in width
in both the 3rd dimension(in height direction) and the 4th dimension(in width
direction) on input tensor.
Linear interpolation is the method of using a line connecting two known quantities
to determine the value of an unknown quantity between the two known quantities.
Bilinear interpolation is an extension of linear interpolation for
interpolating functions of two variables (e.g. H-direction and
W-direction in this op) on a rectilinear 2D grid. The key idea is
to perform linear interpolation first in one direction, and then
Linear interpolation is the method of using a line connecting two known quantities
to determine the value of an unknown quantity between the two known quantities.
Bilinear interpolation is an extension of linear interpolation for
interpolating functions of two variables (e.g. H-direction and
W-direction in this op) on a rectilinear 2D grid. The key idea is
to perform linear interpolation first in one direction, and then
again in the other direction.
Trilinear interpolation is an extension of linear interpolation for
interpolating functions of three variables (e.g. D-direction,
H-direction and W-direction in this op) on a rectilinear 3D grid.
Trilinear interpolation is an extension of linear interpolation for
interpolating functions of three variables (e.g. D-direction,
H-direction and W-direction in this op) on a rectilinear 3D grid.
The linear interpolation is performed on three directions.
Bicubic interpolation is an extension of cubic interpolation for interpolating
......@@ -579,24 +579,24 @@ class InterpolateV2OpMaker : public framework::OpProtoAndCheckerMaker {
smoother than corresponding surfaces obtained by bilinear interpolation or
nearest-neighbor interpolation.
Align_corners and align_mode are optional parameters,the calculation method
Align_corners and align_mode are optional parameters,the calculation method
of interpolation can be selected by them.
Example:
For scale:
if align_corners = True and out_{size}>1 :
scale_{factor} = (in_{size}-1.0)/(out_{size}-1.0)
else:
scale_{factor} = float(in_{size}/out_{size})
Nearest neighbor interpolation:
if:
align_corners = False
......@@ -619,16 +619,16 @@ class InterpolateV2OpMaker : public framework::OpProtoAndCheckerMaker {
if:
align_corners = False , align_mode = 0
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = (H_{in}+0.5) * scale_{factor} - 0.5
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
......@@ -639,17 +639,17 @@ class InterpolateV2OpMaker : public framework::OpProtoAndCheckerMaker {
if:
align_corners = False , align_mode = 0
input : (N,C,D_in,H_in,W_in)
output: (N,C,D_out,H_out,W_out) where:
D_out = (D_{in}+0.5) * scale_{factor} - 0.5
H_out = (H_{in}+0.5) * scale_{factor} - 0.5
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,D_in,H_in,W_in)
output: (N,C,D_out,H_out,W_out) where:
......@@ -671,13 +671,13 @@ class InterpolateV2OpMaker : public framework::OpProtoAndCheckerMaker {
H_out = H_{in} * scale_{factor}
W_out = W_{in} * scale_{factor}
For details of nearest neighbor interpolation, please refer to Wikipedia:
For details of nearest neighbor interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation
For details of bilinear interpolation, please refer to Wikipedia:
For details of bilinear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bilinear_interp_v2olation
For details of trilinear interpolation, please refer to Wikipedia:
For details of trilinear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Trilinear_interp_v2olation
For details of bicubic interpolation, please refer to Wikipedia:
......
paddle/fluid/operators/isclose_op.cc
浏览文件 @ 8dde7aea
......@@ -46,7 +46,7 @@ class IscloseOpMaker : public framework::OpProtoAndCheckerMaker {
"compared as equal. Default: :math:`False` .")
.SetDefault(false);
AddComment(R"DOC(
AddComment(R"DOC(
This operator checks if all :math:`x` and :math:`y` satisfy the condition:
.. math::
......
paddle/fluid/operators/kldiv_loss_op.cc
浏览文件 @ 8dde7aea
......@@ -72,19 +72,19 @@ class KLDivLossOpMaker : public framework::OpProtoAndCheckerMaker {
While :math:`x` is Input(X) and :math:`y` is Input(Target).
While :attr:`reduction` is :attr:`none`, output loss is in
the same shape as Input(X), loss in each point is calculated
the same shape as Input(X), loss in each point is calculated
seperately and no reduction is applied.
While :attr:`reduction` is :attr:`mean`, output loss is in
shape of [1] and loss value is the mean value of all losses.
While :attr:`reduction` is :attr:`sum`, output loss is in
shape of [1] and loss value is the sum value of all losses.
While :attr:`reduction` is :attr:`batchmean`, output loss is
While :attr:`reduction` is :attr:`batchmean`, output loss is
in shape of [1] and loss value is the sum value of all losses
divided by batch size.
)DOC");
}
};
......
paddle/fluid/operators/kron_op.cc
浏览文件 @ 8dde7aea
......@@ -63,14 +63,14 @@ class KronOpMaker : public framework::OpProtoAndCheckerMaker {
Kron Operator.
This operator computes the Kronecker product of two tensors, a
composite tensor made of blocks of the second tensor scaled by the
composite tensor made of blocks of the second tensor scaled by the
first.
This operator assumes that the rank of the two tensors, $X$ and $Y$
are the same, if necessary prepending the smallest with ones. If the
shape of $X$ is [$r_0$, $r_1$, ..., $r_N$] and the shape of $Y$ is
[$s_0$, $s_1$, ..., $s_N$], then the shape of the output tensor is
[$r_{0}s_{0}$, $r_{1}s_{1}$, ..., $r_{N}s_{N}$]. The elements are
are the same, if necessary prepending the smallest with ones. If the
shape of $X$ is [$r_0$, $r_1$, ..., $r_N$] and the shape of $Y$ is
[$s_0$, $s_1$, ..., $s_N$], then the shape of the output tensor is
[$r_{0}s_{0}$, $r_{1}s_{1}$, ..., $r_{N}s_{N}$]. The elements are
products of elements from $X$ and $Y$.
The equation is:
......
paddle/fluid/operators/label_smooth_op.cc
浏览文件 @ 8dde7aea
......@@ -92,23 +92,23 @@ class LabelSmoothOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
LabelSmooth Operator.
Label smoothing is a mechanism to regularize the classifier layer. In machine
learning, optimizing the log-likelihood of the correct label directly may
cause two problems. First, it may result in overfitting: if the model learns
Label smoothing is a mechanism to regularize the classifier layer. In machine
learning, optimizing the log-likelihood of the correct label directly may
cause two problems. First, it may result in overfitting: if the model learns
to assign full probability to the ground-truth label for each training example,
it is not guaranteed to generalize. Second, it encourages the differences
between the largest logit and all others to become large, reducing the ability
of the model to adapt. Label smoothing is proposed to encourage the model to
be less confident, which replaces the ground-truth label $y$ with the weighted
it is not guaranteed to generalize. Second, it encourages the differences
between the largest logit and all others to become large, reducing the ability
of the model to adapt. Label smoothing is proposed to encourage the model to
be less confident, which replaces the ground-truth label $y$ with the weighted
sum of itself and some fixed distribution $\mu$, i.e.
$$
\tilde{y} = (1 - \epsilon) * y + \epsilon * \mu,
$$
where $(1 - \epsilon)$ and $\epsilon$ are the weights respectively, and
$\tilde{y}$ is the smoothed label. Usually uniform distribution is used for
$\mu$. This change in the ground-truth label is called label-smoothing
where $(1 - \epsilon)$ and $\epsilon$ are the weights respectively, and
$\tilde{y}$ is the smoothed label. Usually uniform distribution is used for
$\mu$. This change in the ground-truth label is called label-smoothing
regularization or LSR.
See more details about label smoothing in https://arxiv.org/abs/1512.00567.
......
paddle/fluid/operators/logspace_op.cc
浏览文件 @ 8dde7aea
......@@ -54,11 +54,11 @@ class LogspaceOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<int>("dtype", "The output data type.");
AddOutput("Out", "A sequence of numbers.");
AddComment(R"DOC(
Return fixed number of logarithmical-evenly spaced values within a given
interval. First entry is exponential of Start with base Base, and last
entry is exponential of Stop with base Base. In the case when Num is 1,
only exponential of Start with base Base is returned. If dtype is int32
or int64, the decimal part of values will be truncated.
Return fixed number of logarithmical-evenly spaced values within a given
interval. First entry is exponential of Start with base Base, and last
entry is exponential of Stop with base Base. In the case when Num is 1,
only exponential of Start with base Base is returned. If dtype is int32
or int64, the decimal part of values will be truncated.
Like logspace function of numpy.
)DOC");
}
......
paddle/fluid/operators/lookup_table_dequant_op.cc
浏览文件 @ 8dde7aea
......@@ -114,7 +114,7 @@ Lookup Table Dequant Operator.
The `W` input is a quantized parameter for the sake of saving memories.
This operator first index embeddings with `Ids`,
then dequantizes them and contact them as output (`Out`).
then dequantizes them and contact them as output (`Out`).
The input Ids can carry the LoD (Level of Details) information,
or not. And the output only shares the LoD information with input Ids.
......
paddle/fluid/operators/lstmp_op.cc
浏览文件 @ 8dde7aea
......@@ -259,11 +259,11 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Long-Short Term Memory with recurrent Projection layer (LSTMP) Operator.
LSTMP has a separate projection layer after the LSTM layer, projecting the
original hidden state to a lower-dimensional one, which is proposed to reduce
the number of total parameters and furthermore computational complexity for
the LSTM, espeacially for the case that the size of output units is relative
large (https://research.google.com/pubs/archive/43905.pdf).
LSTMP has a separate projection layer after the LSTM layer, projecting the
original hidden state to a lower-dimensional one, which is proposed to reduce
the number of total parameters and furthermore computational complexity for
the LSTM, espeacially for the case that the size of output units is relative
large (https://research.google.com/pubs/archive/43905.pdf).
The formula is as follows:
......@@ -291,14 +291,14 @@ denote bias vectors ($b_i$ is the input gate bias vector), $\sigma$
is the activation, such as logistic sigmoid function, and
$i, f, o$ and $c$ are the input gate, forget gate, output gate,
and cell activation vectors, respectively, all of which have the same size as
the cell output activation vector $h$. Here $h$ is usually called the hidden
state and $r$ denotes its recurrent projection. And $\tilde{c_t}$ is also
called the candidate hidden state, whose computation is based on the current
the cell output activation vector $h$. Here $h$ is usually called the hidden
state and $r$ denotes its recurrent projection. And $\tilde{c_t}$ is also
called the candidate hidden state, whose computation is based on the current
input and previous hidden state.
The $\odot$ is the element-wise product of the vectors. $act_g$ and $act_h$
are the cell input and cell output activation functions and `tanh` is usually
used for them. $\overline{act_h}$ is the activation function for the
used for them. $\overline{act_h}$ is the activation function for the
projection output, usually using `identity` or same as $act_h$.
Note that these $W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}$
......
paddle/fluid/operators/lu_op.cc
浏览文件 @ 8dde7aea
......@@ -24,7 +24,7 @@ namespace operators {
class LUOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddComment(R"DOC(LU decomposition,
AddComment(R"DOC(LU decomposition,
Computes the LU factorization of a matrix or batches of matrices A.
)DOC");
AddInput("X", "(Tensor) The input tensor, shape of (*,m,n)");
......
paddle/fluid/operators/lu_unpack_op.cc
浏览文件 @ 8dde7aea
......@@ -24,7 +24,7 @@ namespace operators {
class LU_UnpackOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddComment(R"DOC(Unpack L U and P to single matrix tensor,
AddComment(R"DOC(Unpack L U and P to single matrix tensor,
unpack L and U matrix from LU, unpack permutation matrix Pmat from Pivtos .
)DOC");
AddInput("X", "(Tensor) The input LU tensor, shape of (*,m,n)");
......
paddle/fluid/operators/margin_rank_loss_op.cc
浏览文件 @ 8dde7aea
......@@ -102,19 +102,19 @@ class MarginRankLossOpMaker : public framework::OpProtoAndCheckerMaker {
MarginRankLoss Operator.
This operator measures the loss given a pair of training sample
{`X1`, `X2`} and the `Label` with attribute `margin`, where `Label = +1`
indicating X1 is ranked higher than `X2` and `Label = -1` otherwise. The loss
{`X1`, `X2`} and the `Label` with attribute `margin`, where `Label = +1`
indicating X1 is ranked higher than `X2` and `Label = -1` otherwise. The loss
is calculated as:
$loss(X1, X2, Label) = \max(0, -Label * (X1 - X2) + margin)$
The attribute `margin` here helps make the predictions more robust.
Denote the item ranked higher as the positive sample, otherwise the negative
sample. If the score of the two samples satisfies
Denote the item ranked higher as the positive sample, otherwise the negative
sample. If the score of the two samples satisfies
$positive sample - negative sample < margin$
the pair of samples will contribute to the final loss, which will backpropagate
the pair of samples will contribute to the final loss, which will backpropagate
and train the ranking model to enlarge the difference between the two scores.
For batch input with size `batch_size`, `X1`, `X2` and `Label`
......
paddle/fluid/operators/match_matrix_tensor_op.cc
浏览文件 @ 8dde7aea
......@@ -230,9 +230,9 @@ void MatchMatrixTensorOpMaker::Make() {
Match Matrix Tensor Operator
This operator calculate X * W * Y, only support 2-D for X and Y.
the output is a level-1 LodTensor:
the output is a level-1 LodTensor:
level_0: dim_t
NOTE: only support 'float32' data type now.
)DOC");
......
paddle/fluid/operators/matmul_v2_op.cc
浏览文件 @ 8dde7aea
......@@ -193,8 +193,8 @@ class MatMulV2OpMaker : public framework::OpProtoAndCheckerMaker {
"doing multiplication")
.SetDefault(false);
AddComment(
R"DOC(Matrix multiplication Out = X * Y. A has shape (d0, d1 ... M, K),
B has shape (d0, d1 ... K, N), Out has shape ((d0, d1 ... M, N)).
R"DOC(Matrix multiplication Out = X * Y. A has shape (d0, d1 ... M, K),
B has shape (d0, d1 ... K, N), Out has shape ((d0, d1 ... M, N)).
In addition, it also follows the broadcast rule which is similar as
numpy.matmul.
)DOC");
......
paddle/fluid/operators/mean_iou_op.cc
浏览文件 @ 8dde7aea
......@@ -87,10 +87,10 @@ class MeanIoUOpMaker : public framework::OpProtoAndCheckerMaker {
mean-IOU Operator.
Mean Intersection-Over-Union is a common evaluation metric for
semantic image segmentation, which first computes the IOU for each
semantic class and then computes the average over classes.
IOU is defined as follows:
semantic class and then computes the average over classes.
IOU is defined as follows:
IOU = true_positive / (true_positive + false_positive + false_negative).
It is based on pixel level area while "IOU Similarity Operator"
It is based on pixel level area while "IOU Similarity Operator"
is based on area of rectangle.
)DOC");
......
paddle/fluid/operators/memcpy_op.cc
浏览文件 @ 8dde7aea
......@@ -118,7 +118,7 @@ class MemcpyOpProtoMaker : public framework::OpProtoAndCheckerMaker {
"6: dst is on CustomDevicePlace");
AddComment(R"DOC(
Memcpy Operator.
By now, it ONLY supports the memcopy between CUDAPinnedPlace <-> CUDAPlace or
By now, it ONLY supports the memcopy between CUDAPinnedPlace <-> CUDAPlace or
NPUPlace <-> CPUPlace, and used as an internal op by Recompute-Offload.
You would have to update it if you want other more capacities.
......
paddle/fluid/operators/meshgrid_op.cc
浏览文件 @ 8dde7aea
......@@ -65,7 +65,7 @@ Take: N tensors, each of which can be either scalr or 1-dimensional vector, and
N-dimensional grids.
Args:
tensors (list of tensor): if the input k tensors has (N1,), (N2,),..., (Nk,), then
tensors (list of tensor): if the input k tensors has (N1,), (N2,),..., (Nk,), then
the output tensors are all of size (N1, N2, ...., Nk).
Example::
......
paddle/fluid/operators/metrics/accuracy_op.cc
浏览文件 @ 8dde7aea
......@@ -44,7 +44,7 @@ class AccuracyOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("Total", "The samples count of current batch");
AddComment(R"DOC(
Accuracy Operator.
Accuracy Operator.
It will print accuracy rate for classification.
The accuracy is calculated as follows:
......@@ -52,7 +52,7 @@ The accuracy is calculated as follows:
$$accuracy = \frac{NumOfCorrectPredicts}{NumOfAllSamples}$$
Both the input Out and Label can carry the LoD (Level of Details)
information, or not. But the output only shares the LoD information
information, or not. But the output only shares the LoD information
with the input Out(Inference).
)DOC");
......
paddle/fluid/operators/mode_op.cc
浏览文件 @ 8dde7aea
......@@ -51,7 +51,7 @@ class ModeOpMaker : public framework::OpProtoAndCheckerMaker {
.SetDefault(-1);
AddAttr<bool>("keepdim", "Keep the dim that to reduce.").SetDefault(false);
AddComment(R"DOC(
This operator finds the mode of input Tensor. And outputs their values and indices as vectors.
This operator finds the mode of input Tensor. And outputs their values and indices as vectors.
)DOC");
}
};
......
paddle/fluid/operators/modified_huber_loss_op.cc
浏览文件 @ 8dde7aea
......@@ -86,7 +86,7 @@ Since target Y is not differentiable, calculating gradient for Y is illegal.
The formula of modified huber loss is:
$$
L(y, f(x)) =
L(y, f(x)) =
\begin{cases}
(\max(0, 1 - yf(x)))^2, \text{if} \ yf(x) >= -1 \\
-4yf(x), \quad \text{otherwise}
......
paddle/fluid/operators/nll_loss_op.cc
浏览文件 @ 8dde7aea
......@@ -82,10 +82,10 @@ The loss can be described as:
$Out[i] = -X[Label[i]]*Weight[Label[i]]$
It can also be used for higher dimension inputs, such as 2D images, by
providing an input of shape (batch_size, C, d1, d2, ..., dK), with
K >= 1, where K is the number of dimensions, and a Label of
appropriate shape. In the case of images, it computes NLL loss
It can also be used for higher dimension inputs, such as 2D images, by
providing an input of shape (batch_size, C, d1, d2, ..., dK), with
K >= 1, where K is the number of dimensions, and a Label of
appropriate shape. In the case of images, it computes NLL loss
per-pixel.
)DOC");
......
paddle/fluid/operators/norm_op.cc
浏览文件 @ 8dde7aea
......@@ -54,7 +54,7 @@ y = \frac{x}{ \sqrt{\sum {x^2} + epsion }}
$$
where, $\sum {x^2}$ is calculated along the `axis` dimension.
)DOC");
}
};
......
paddle/fluid/operators/optimizers/dpsgd_op.cc
浏览文件 @ 8dde7aea
......@@ -116,7 +116,7 @@ class DpsgdOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Dpsgd Optimizer.
We implement the Dpsgd optimizer according to CCS16 paper -
We implement the Dpsgd optimizer according to CCS16 paper -
Deep Learning with Differential Privacy.
Dpsgd updates:
......
paddle/fluid/operators/optimizers/lamb_op.cc
浏览文件 @ 8dde7aea
......@@ -101,8 +101,8 @@ class LambOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
LAMB (Layer-wise Adaptive Moments optimizer for Batching training) Optimizer.
LAMB Optimizer is designed to scale up the batch size of training without losing
accuracy, which supports adaptive element-wise updating and accurate layer-wise
LAMB Optimizer is designed to scale up the batch size of training without losing
accuracy, which supports adaptive element-wise updating and accurate layer-wise
correction. For more information, please refer to https://arxiv.org/abs/1904.00962.
The updating of parameters follows:
......@@ -121,7 +121,7 @@ r_t &= \frac{m_t}{\sqrt{v_t}+\epsilon} \\
w_t &= w_{t-1} -\eta_t \frac{\left \| w_{t-1}\right \|}{\left \| r_t + \lambda w_{t-1}\right \|} (r_t + \lambda w_{t-1})
$$
where $m$ is the 1st moment, and $v$ the 2nd moment, $\eta$ the
where $m$ is the 1st moment, and $v$ the 2nd moment, $\eta$ the
learning rate, $\lambda$ the weight decay rate.
)DOC");
}
......
paddle/fluid/operators/optimizers/pow2_decay_with_linear_warmup_op.cc
浏览文件 @ 8dde7aea
......@@ -62,11 +62,11 @@ class Pow2DecayWithLinearWarmupOpMaker
AddComment(R"DOC(
The Pow2DecayWithLinearWarmup learning rate scheduler.
When step_num < warmup_steps, lr = base_lr * step_num / warmup_steps
When step_num < warmup_steps, lr = base_lr * step_num / warmup_steps
When warmup_steps <= step_num <= total_steps,
factor = 1 - (step_num - warmup_steps) / (total_steps - warmup_steps)
lr = (base_lr - end_lr) * factor * factor + end_lr
When warmup_steps <= step_num <= total_steps,
factor = 1 - (step_num - warmup_steps) / (total_steps - warmup_steps)
lr = (base_lr - end_lr) * factor * factor + end_lr
When step_num > total_steps, lr = end_lr
......
paddle/fluid/operators/optimizers/proximal_adagrad_op.cc
浏览文件 @ 8dde7aea
......@@ -119,9 +119,9 @@ param = sign(prox\_param) / (1 + learning\_rate * l2) *
\max(|prox\_param| - learning\_rate * l1 , 0)
$$
The paper that proposed Proximal GD:
The paper that proposed Proximal GD:
(http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting.pdf)
Here, we use the adagrad learning rate as specified here:
Here, we use the adagrad learning rate as specified here:
(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
)DOC");
......
paddle/fluid/operators/optimizers/proximal_gd_op.cc
浏览文件 @ 8dde7aea
......@@ -92,7 +92,7 @@ $$
prox\_param = param - learning\_rate * grad \\
param = sign(prox\_param) / (1 + learning\_rate * l2) *
\max(|prox\_param| - learning\_rate * l1, 0)
$$
$$
The paper that proposed Proximal Gradient Descent:
(http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting.pdf)
......
paddle/fluid/operators/optimizers/rmsprop_op.cc
浏览文件 @ 8dde7aea
......@@ -66,7 +66,7 @@ class RmspropOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<bool>("centered", "(bool, default false) use centered rmsprop.")
.SetDefault(false);
AddComment(R"DOC(
Rmsprop Optimizer.
Rmsprop Optimizer.
$$
MeanSquareOut = decay * MeanSquare + (1 - decay) * Grad * Grad \\
......
paddle/fluid/operators/pad2d_op.cc
浏览文件 @ 8dde7aea
......@@ -775,7 +775,7 @@ class Pad2dOpMaker : public framework::OpProtoAndCheckerMaker {
.SetDefault("NCHW");
AddComment(R"DOC(
Pad2d Operator.
Pad 2-d images according to 'paddings' and 'mode'.
Pad 2-d images according to 'paddings' and 'mode'.
If mode is 'reflect', paddings[0] and paddings[1] must be no greater
than height-1. And the width dimension has the same condition.
......
paddle/fluid/operators/pad3d_op.cc
浏览文件 @ 8dde7aea
......@@ -113,7 +113,7 @@ class Pad3dOpMaker : public framework::OpProtoAndCheckerMaker {
.SetDefault("NCDHW");
AddComment(R"DOC(
Pad3d Operator.
Pad 3-d images according to 'paddings' and 'mode'.
Pad 3-d images according to 'paddings' and 'mode'.
If mode is 'reflect', paddings[0] and paddings[1] must be no greater
than width-1. The height and depth dimension have the same condition.
......
paddle/fluid/operators/pad_op.cc
浏览文件 @ 8dde7aea
......@@ -59,7 +59,7 @@ class PadOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Pad Operator.
Pad input into output, as specified by paddings and pad_value.
Pad input into output, as specified by paddings and pad_value.
The input should be a k-D tensor(k > 0 and k < 7). As an example:
Given:
......
paddle/fluid/operators/partial_sum_op.cc
浏览文件 @ 8dde7aea
......@@ -161,9 +161,9 @@ class PartialSumOpMaker : public framework::OpProtoAndCheckerMaker {
.SetDefault(-1);
AddComment(R"DOC(
PartialSum Operator.
This Op can sum the vars by specifying the initial position(start_index) and length(length).
This Op can sum the vars by specifying the initial position(start_index) and length(length).
This OP exists in contrib, which means that it is not shown to the public.
Only 2-D Tensor or LodTensor input is supported. Slice and concat can only be
Only 2-D Tensor or LodTensor input is supported. Slice and concat can only be
performed along the second dimension.
Examples:
......
paddle/fluid/operators/pixel_shuffle_op.cc
浏览文件 @ 8dde7aea
......@@ -60,9 +60,9 @@ class PixelShuffleOpMaker : public framework::OpProtoAndCheckerMaker {
with a stride of :math:`1/r`.
Please refer to the paper:
`Real-Time Single Image and Video Super-Resolution Using an Efficient
`Real-Time Single Image and Video Super-Resolution Using an Efficient
Sub-Pixel Convolutional Neural Network <https://arxiv.org/abs/1609.05158v2>`_
by Shi et. al (2016) for more details.
by Shi et. al (2016) for more details.
)DOC");
}
......
paddle/fluid/operators/pixel_unshuffle_op.cc
浏览文件 @ 8dde7aea
......@@ -55,9 +55,9 @@ class PixelUnshuffleOpMaker : public framework::OpProtoAndCheckerMaker {
This operation is the reversion of PixelShuffle operation.
Please refer to the paper:
`Real-Time Single Image and Video Super-Resolution Using an Efficient
`Real-Time Single Image and Video Super-Resolution Using an Efficient
Sub-Pixel Convolutional Neural Network <https://arxiv.org/abs/1609.05158v2>`_
by Shi et. al (2016) for more details.
by Shi et. al (2016) for more details.
)DOC");
}
......
paddle/fluid/operators/pool_with_index_op.cc
浏览文件 @ 8dde7aea
......@@ -119,7 +119,7 @@ MaxPool2d Operator.
The maxPooling2d with index operation calculates the output and the mask
based on the input, ksize, strides, and paddings parameters. Input(X) and
output(Out, Mask) are in NCHW format, where N is batch size, C is the
number of channels, H is the height of the feature,
number of channels, H is the height of the feature,
and W is the width of the feature.
Parameters(ksize, strides, paddings) are two elements.
These two elements represent height and width, respectively.
......@@ -136,12 +136,12 @@ Example:
H_{out} = \frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1
$$
For adaptive = true:
$$
H_{out} = ksize[0] W_{out} = ksize[1]
$$
)DOC");
}
......@@ -210,7 +210,7 @@ The maxpooling3d with index operation calculates the output and the mask
based on the input and ksize, strides, paddings parameters.
Input(X) and output(Out, Mask) are in NCDHW format, where N is batch
size, C is the number of channels, and D, H and W are the depth, height and
width of the feature, respectively.
width of the feature, respectively.
Parameters(ksize, strides, paddings) are three elements.
These three elements represent depth, height and width, respectively.
The input(X) size and output(Out, Mask) size may be different.
......@@ -227,7 +227,7 @@ Example:
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2])}{strides[2]} + 1
$$
For adaptive = true:
$$
D_{out} = ksize[0] H_{out} = ksize[1] W_{out} = ksize[2]
......
paddle/fluid/operators/psroi_pool_op.cc
浏览文件 @ 8dde7aea
......@@ -70,8 +70,8 @@ class PSROIPoolOpMaker : public framework::OpProtoAndCheckerMaker {
.SetDefault(1);
AddComment(R"Doc(
Position sensitive region of interest pooling (also known as PSROIPooling) is to perform
position-sensitive average pooling on regions of interest specified by input, takes as
input N position-sensitive score maps and a list of num_rois regions of interest.
position-sensitive average pooling on regions of interest specified by input, takes as
input N position-sensitive score maps and a list of num_rois regions of interest.
PSROIPooling for R-FCN. Please refer to https://arxiv.org/abs/1605.06409 for more details.
)Doc");
......
paddle/fluid/operators/random_crop_op.cc
浏览文件 @ 8dde7aea
......@@ -79,7 +79,7 @@ class RandomCropOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
This operator takes a batch of instance, and do random cropping on each instance.
It means that cropping positions differs on each instance, which is determined
by an uniform random generator. All cropped instances have the same shape, which
by an uniform random generator. All cropped instances have the same shape, which
is determined by the operator's attribute 'shape'.
)DOC");
}
......
paddle/fluid/operators/randperm_op.cc
浏览文件 @ 8dde7aea
......@@ -71,7 +71,7 @@ class RandpermOpMaker : public framework::OpProtoAndCheckerMaker {
"Default: 0.")
.SetDefault(0);
AddComment(R"DOC(
AddComment(R"DOC(
This operator returns a random permutation of integers from 0 to n-1.
)DOC");
}
......
paddle/fluid/operators/rank_attention_op.cc
浏览文件 @ 8dde7aea
......@@ -146,7 +146,7 @@ class RankAttentionOpMaker : public framework::OpProtoAndCheckerMaker {
.SetDefault(0);
AddComment(R"DOC(
RankAttention Operator.
This Op can calculate rank attention between input and rank_param,
This Op can calculate rank attention between input and rank_param,
and rank_param gives the organization of data. Notice: It currently supports GPU device.
This Op exists in contrib, which means that it is not shown to the public.
)DOC");
......
paddle/fluid/operators/rank_loss_op.cc
浏览文件 @ 8dde7aea
......@@ -155,7 +155,7 @@ class RankLossOpMaker : public framework::OpProtoAndCheckerMaker {
RankLoss Operator.
RankLoss operator for RankNet
(http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf).
(http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf).
RankNet is a pairwise ranking model with
one training sample consisting of a pair of doc A and B, and the label P
indicating that A is ranked higher than B or not:
......@@ -164,8 +164,8 @@ P = {0, 1} or {0, 0.5, 1}, where 0.5 means no information about the rank of
the input pair.
The RankLoss operator takes three inputs: Left (o_i), Right (o_j) and Label
(P_{i,j}), which represent the output score of RankNet for the two docs and
the label respectively, and yields the rank loss C_{i,j} using the following
(P_{i,j}), which represent the output score of RankNet for the two docs and
the label respectively, and yields the rank loss C_{i,j} using the following
equation:
$$
......
paddle/fluid/operators/real_op.cc
浏览文件 @ 8dde7aea
......@@ -30,10 +30,10 @@ class RealOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() override {
AddInput("X", "(Tensor), The input tensor of real op.");
AddOutput("Out", "(Tensor), The output tensor of real op.");
AddComment(R"DOC(
Real Operator.
AddComment(R"DOC(
Real Operator.
This operator is used to get a new tensor containing real values
This operator is used to get a new tensor containing real values
from a tensor with complex data type.
)DOC");
......
paddle/fluid/operators/reorder_lod_tensor_by_rank_op.cc
浏览文件 @ 8dde7aea
......@@ -70,7 +70,7 @@ X = [Slice0, Slice1, Slice2, Slice3] and its LoD information is empty. The
indices in RankTable are [3, 0, 2, 1].
Out = [Slice3, Slice0, Slice2, Slice1] with no LoD information is appended.
**NOTE**:
**NOTE**:
This operator sorts Input(X) according to a given LoDRankTable which does
not need to be calculated according to Input(X). It can be calculated according
to another different sequence, and then this operator sorts Input(X) according
......
paddle/fluid/operators/reverse_op.cc
浏览文件 @ 8dde7aea
......@@ -69,7 +69,7 @@ class ReverseOpMaker : public framework::OpProtoAndCheckerMaker {
Out = [[11, 12, 13, 14, 15]
[6, 7, 8, 9, 10]
[1, 2, 3, 4, 5]].
Case 2:
Given
X = [[[1, 2, 3, 4]
......
paddle/fluid/operators/roi_align_op.cc
浏览文件 @ 8dde7aea
......@@ -116,17 +116,17 @@ class ROIAlignOpMaker : public framework::OpProtoAndCheckerMaker {
**RoIAlign Operator**
Region of interest align (also known as RoI align) is to perform
bilinear interpolation on inputs of nonuniform sizes to obtain
bilinear interpolation on inputs of nonuniform sizes to obtain
fixed-size feature maps (e.g. 7*7)
Dividing each region proposal into equal-sized sections with
the pooled_width and pooled_height. Location remains the origin
result.
In each ROI bin, the value of the four regularly sampled locations
In each ROI bin, the value of the four regularly sampled locations
are computed directly through bilinear interpolation. The output is
the mean of four locations.
Thus avoid the misaligned problem.
Thus avoid the misaligned problem.
)DOC");
}
};
......
paddle/fluid/operators/roi_pool_op.cc
浏览文件 @ 8dde7aea
......@@ -124,7 +124,7 @@ The operator has three steps:
3. Copying these max values to the output buffer
ROI Pooling for Faster-RCNN. The link below is a further introduction:
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");
}
......
paddle/fluid/operators/roll_op.cc
浏览文件 @ 8dde7aea
......@@ -85,10 +85,10 @@ class RollOpMaker : public framework::OpProtoAndCheckerMaker {
"with shifts or size == 0")
.SetDefault({});
AddComment(R"DOC(
Roll the tensor along the given dimension(s).
Roll the tensor along the given dimension(s).
Elements that are shifted beyond the last position
are re-introduced at the first position. If a dimension
is not specified, the tensor will be flattened before
is not specified, the tensor will be flattened before
rolling and then restored to the original shape.
)DOC");
}
......
paddle/fluid/operators/row_conv_op.cc
浏览文件 @ 8dde7aea
......@@ -100,20 +100,20 @@ class RowConvOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
:strong:`Row-convolution operator`
The row convolution is called lookahead convolution. This operator was
The row convolution is called lookahead convolution. This operator was
introduced in the following paper for DeepSpeech2:
http://www.cs.cmu.edu/~dyogatam/papers/wang+etal.iclrworkshop2016.pdf
http://www.cs.cmu.edu/~dyogatam/papers/wang+etal.iclrworkshop2016.pdf
The main motivation is that a bidirectional RNN, useful in DeepSpeech
like speech models, learns representation for a sequence by performing a
forward and a backward pass through the entire sequence. However, unlike
The main motivation is that a bidirectional RNN, useful in DeepSpeech
like speech models, learns representation for a sequence by performing a
forward and a backward pass through the entire sequence. However, unlike
unidirectional RNNs, bidirectional RNNs are challenging to deploy in an online
and low-latency setting. The lookahead convolution incorporates information
from future subsequences in a computationally efficient manner to improve
unidirectional recurrent neural networks. The row convolution operator is
and low-latency setting. The lookahead convolution incorporates information
from future subsequences in a computationally efficient manner to improve
unidirectional recurrent neural networks. The row convolution operator is
different from the 1D sequence convolution, and is computed as follows:
Given an input sequence $X$ of length $t$ and input dimension $D$,
Given an input sequence $X$ of length $t$ and input dimension $D$,
and a filter ($W$) of size $context \times D$,
the output sequence is convolved as:
......
paddle/fluid/operators/run_program_op.cc
浏览文件 @ 8dde7aea
......@@ -133,14 +133,14 @@ class RunProgramOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
RunProgram operator.
The RunProgram operator receives a program's feed targets, fetch targets,
and parameters, and receives the forward and backward program desc
The RunProgram operator receives a program's feed targets, fetch targets,
and parameters, and receives the forward and backward program desc
as attributes, and then executes the program by executor.
NOTE: This operator is added so that the inference model stored by
`fluid.io.save_inference_model` under the static graph mode can be loaded
NOTE: This operator is added so that the inference model stored by
`fluid.io.save_inference_model` under the static graph mode can be loaded
under the dynamic graph mode for fine-tuning or inferencing.
)DOC");
}
};
......
paddle/fluid/operators/sample_logits_op.cc
浏览文件 @ 8dde7aea
......@@ -102,7 +102,7 @@ class SampleLogitsOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
"""
Computes sampled output training logits and labels suitable for implementing
sampled softmax.
sampled softmax.
"""
)DOC");
......
paddle/fluid/operators/searchsorted_op.cc
浏览文件 @ 8dde7aea
......@@ -54,7 +54,7 @@ class SearchSortedOpMaker : public framework::OpProtoAndCheckerMaker {
Searchsorted Operator.
This OP is used to find the index of the corresponding sorted_sequence in the innermost dimension based on the given values.
)DOC");
}
};
......
paddle/fluid/operators/select_output_op.cc
浏览文件 @ 8dde7aea
......@@ -83,7 +83,7 @@ class SelectOutputOpProtoMaker : public framework::OpProtoAndCheckerMaker {
// (minimal viable product) here.
AddComment(R"DOC(
Split input variable into one output branch. The mask is an integer tensor to
specify which output branch should copy the input.
specify which output branch should copy the input.
)DOC");
}
};
......
paddle/fluid/operators/sequence_ops/sequence_enumerate_op.cc
浏览文件 @ 8dde7aea
......@@ -59,10 +59,10 @@ class SequenceEnumerateOpMaker : public framework::OpProtoAndCheckerMaker {
Sequence Enumerate Operator.
Generate a new sequence for the input index sequence, which enumerates all the
sub-sequences with length `win_size` of the input.
sub-sequences with length `win_size` of the input.
The enumerated sequence has the same 1st dimension with variable `input`, and
the 2nd dimension is `win_size`, padded by `pad_value` if necessary in generation.
Examples:
Case 1:
Input:
......
paddle/fluid/operators/sequence_ops/sequence_erase_op.cc
浏览文件 @ 8dde7aea
......@@ -62,17 +62,17 @@ class SequenceEraseOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Sequence Erase Operator.
Sequence erase operator erases tokens specified by Attr(tokens) from the input
sequences Input(X), and outputs the remaining data and modifies the LoD
Sequence erase operator erases tokens specified by Attr(tokens) from the input
sequences Input(X), and outputs the remaining data and modifies the LoD
information at the same time. For example, given a 2-D LoDTensor
X = [[2, 2, 6, 1, 3, 9, 6, 1, 0, 1]]^T
with lod = [[0, 3, 6, 10]], there are three sequences in the input:
X1 = [[2, 2, 6]]^T, X2 = [[1, 3, 9]]^T and X3 = [[6, 1, 0, 1]]^T.
If the tokens to be erased are Attr(tokens) = [2, 3, 5], after the erasing
If the tokens to be erased are Attr(tokens) = [2, 3, 5], after the erasing
operation, the three sequences become
X1' = [[6]]^T, X2' = [[1, 9]]^T and X3' = [[6, 1, 0, 1]]^T.
......@@ -83,8 +83,8 @@ Hence the LoDTensor Output(Out) should be
with lod = [[0, 1, 3, 7]].
An example usage for this operator is to remove the special tokens when
computing the edit distance between two strings, such as blank, start token,
An example usage for this operator is to remove the special tokens when
computing the edit distance between two strings, such as blank, start token,
and end token.
)DOC");
}
......
paddle/fluid/operators/sequence_ops/sequence_mask_op.cc
浏览文件 @ 8dde7aea
......@@ -85,7 +85,7 @@ This operator outputs a Mask according to Input(X) and Attr(maxlen).
Supposing Input(X) is a Tensor with shape [d_1, d_2, ..., d_n], the
Output(Y) is a mask with shape [d_1, d_2, ..., d_n, maxlen], where:
Y(i_1, i_2, ..., i_n, j) = (j < X(i_1, i_2, ..., i_n))
Y(i_1, i_2, ..., i_n, j) = (j < X(i_1, i_2, ..., i_n))
If maxlen < 0, maxlen = max(X)
)DOC");
......
paddle/fluid/operators/sequence_ops/sequence_pad_op.cc
浏览文件 @ 8dde7aea
......@@ -170,9 +170,9 @@ class SequencePadOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Sequence Pad Operator
This operator pads sequences in a same batch to a consistent length.
The length is specified by attribute 'padded_length'. New elements,
whose values are specified by input 'PadValue', will be appended to
This operator pads sequences in a same batch to a consistent length.
The length is specified by attribute 'padded_length'. New elements,
whose values are specified by input 'PadValue', will be appended to
the end of each sequence, to make their final lengths consistent.
Following are cases to better explain how this works:
......@@ -186,10 +186,10 @@ class SequencePadOpMaker : public framework::OpProtoAndCheckerMaker {
PadValue.data = [0]
and attribite 'padded_length' = 4,
then we get LoDTensor:
Out.data = [[a, b, 0, 0],
Out.data = [[a, b, 0, 0],
[c, d, e, 0]]
Length.data = [2, 3]
Case 2:
Given a 1-level LoDTensor input(X):
......@@ -197,13 +197,13 @@ class SequencePadOpMaker : public framework::OpProtoAndCheckerMaker {
X.data = [[a1, a2], [b1, b2], [c1, c2], [d1, d2], [e1, e2]]
and Input(PadValue):
PadValue.data = [0]
and attribite 'padded_length' = -1, which mean using the length
and attribite 'padded_length' = -1, which mean using the length
of longest input sequence(3 in this case),
then we get LoDTensor:
Out.data = [[[a1, a2], [b1, b2], [0, 0]],
Out.data = [[[a1, a2], [b1, b2], [0, 0]],
[[c1, c2], [d1, d2], [e1, e2]]]
Length.data = [2, 3]
Case 3:
Given a 1-level LoDTensor input(X):
......@@ -211,10 +211,10 @@ class SequencePadOpMaker : public framework::OpProtoAndCheckerMaker {
X.data = [[a1, a2], [b1, b2], [c1, c2], [d1, d2], [e1, e2]]
and Input(PadValue):
PadValue.data = [p1, p2]
and attribite 'padded_length' = -1, which mean using the length
and attribite 'padded_length' = -1, which mean using the length
of longest input sequence(3 in this case),
then we get LoDTensor:
Out.data = [[[a1, a2], [b1, b2], [p1, p2]],
Out.data = [[[a1, a2], [b1, b2], [p1, p2]],
[[c1, c2], [d1, d2], [e1, e2]]]
Length.data = [2, 3]
......
paddle/fluid/operators/sequence_ops/sequence_unpad_op.cc
浏览文件 @ 8dde7aea
......@@ -108,8 +108,8 @@ class SequenceUnpadOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Sequence Unpad Operator
This operator removes the padding data in the input sequences and convert
them into sequences with actual length as output, identitied by lod
This operator removes the padding data in the input sequences and convert
them into sequences with actual length as output, identitied by lod
information.
Example:
......@@ -117,9 +117,9 @@ class SequenceUnpadOpMaker : public framework::OpProtoAndCheckerMaker {
Given input tensor Input(X):
X.data = [[ 1.0, 2.0, 3.0, 4.0, 5.0],
[ 6.0, 7.0, 8.0, 9.0, 10.0],
[11.0, 12.0, 13.0, 14.0, 15.0]],
`
in which there are 3 sequences padded to length 5, and the actual length
[11.0, 12.0, 13.0, 14.0, 15.0]],
`
in which there are 3 sequences padded to length 5, and the actual length
specified by Input(Length):
Length.data = [2, 3, 4],
......@@ -127,7 +127,7 @@ class SequenceUnpadOpMaker : public framework::OpProtoAndCheckerMaker {
after unpadding, Output(Out) will be:
Out.data = [[1.0, 2.0, 6.0, 7.0, 8.0, 11.0, 12.0, 13.0, 14.0]]
Out.lod = [[0, 2, 5, 9]]
Out.lod = [[0, 2, 5, 9]]
)DOC");
}
......
paddle/fluid/operators/shard_index_op.cc
浏览文件 @ 8dde7aea
......@@ -55,10 +55,10 @@ class ShardIndexOpMaker : public framework::OpProtoAndCheckerMaker {
This layer creates the sharded index for input. This layers is used in
model- and data- parallel mixed training generally, in which the index
data (usually the label) should be recaculated in each trainer according
to
to
.. math::
assert index_num % nshards == 0
shard_size = index_num / nshards
......@@ -76,13 +76,13 @@ Examples:
X is a Tensor of integer values:
X.shape = [4, 1]
X.data = [[1], [6], [12], [19]]
suppose index_num = 20 and nshards = 2, then we get shard_size = 10
if shard_id == 0, we get the Out:
Out.shape = [4, 1]
Out.data = [[1], [6], [-1], [-1]]
if shard_id == 1, we get the Out:
Out.shape = [4, 1]
Out.data = [[-1], [-1], [2], [9]]
......
paddle/fluid/operators/similarity_focus_op.cc
浏览文件 @ 8dde7aea
......@@ -35,17 +35,17 @@ class SimilarityFocusOpMaker : public framework::OpProtoAndCheckerMaker {
SimilarityFocus Operator.
Generate a similarity focus mask with the same shape of input using the following method:
1. Extract the 3-D tensor(here the first dimension is BatchSize) corresponding
to the axis according to the indexes. For example, if axis=1 and indexes=[a],
it will get the matrix T=X[:, a, :, :]. In this case, if the shape of input X
1. Extract the 3-D tensor(here the first dimension is BatchSize) corresponding
to the axis according to the indexes. For example, if axis=1 and indexes=[a],
it will get the matrix T=X[:, a, :, :]. In this case, if the shape of input X
is (BatchSize, A, B, C), the shape of tensor T is (BatchSize, B, C).
2. For each index, find the largest numbers in the tensor T, so that the same
row and same column has at most one number(what it means is that if the
largest number has been found in the i-th row and the j-th column, then
the numbers in the i-th row or j-th column will be skipped. And then the
next largest number will be selected from the remaining numbers. Obviously
there will be min(B, C) numbers), and mark the corresponding position of the
3-D similarity focus mask as 1, otherwise as 0. Do elementwise-or for
2. For each index, find the largest numbers in the tensor T, so that the same
row and same column has at most one number(what it means is that if the
largest number has been found in the i-th row and the j-th column, then
the numbers in the i-th row or j-th column will be skipped. And then the
next largest number will be selected from the remaining numbers. Obviously
there will be min(B, C) numbers), and mark the corresponding position of the
3-D similarity focus mask as 1, otherwise as 0. Do elementwise-or for
each index.
3. Broadcast the 3-D similarity focus mask to the same shape of input X.
......
paddle/fluid/operators/sparse_attention_op.cc
浏览文件 @ 8dde7aea
......@@ -66,8 +66,8 @@ class SparseAttentionOpMaker : public framework::OpProtoAndCheckerMaker {
.AsIntermediate();
AddComment(R"DOC(
Compute the value of the sparse attention module. Its input value includes five tensors.
Q, K, and V represent query, key, and value in the Attention module, respectively.
The CSR format is used to represent the sparsity feature in the Attention module.
Q, K, and V represent query, key, and value in the Attention module, respectively.
The CSR format is used to represent the sparsity feature in the Attention module.
The CSR format contains two tensors, offset and columns.
)DOC");
}
......
paddle/fluid/operators/spectral_norm_op.cc
浏览文件 @ 8dde7aea
......@@ -114,7 +114,7 @@ class SpectralNormOpMaker : public framework::OpProtoAndCheckerMaker {
$$\sigma{\mathbf{W}} = \mathbf{u}^{T} \mathbf{W} \mathbf{v}$$
For details of spectral normalization, please refer to paper:
For details of spectral normalization, please refer to paper:
`Spectral Normalization <https://arxiv.org/abs/1802.05957>`_ .
)DOC");
}
......
paddle/fluid/operators/squared_l2_distance_op.cc
浏览文件 @ 8dde7aea
......@@ -140,15 +140,15 @@ class SquaredL2DistanceOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
SquaredL2Distance operator
This operator will cacluate the squared L2 distance for the input and
the target. Number of distance value will be equal to the first dimension
of input. First dimension of the target could be equal to the input or to 1.
If the first dimension of target is 1, the operator will broadcast target's
first dimension to input's first dimension. During backward propagation,
the user can decide whether to calculate the gradient of the input or
This operator will cacluate the squared L2 distance for the input and
the target. Number of distance value will be equal to the first dimension
of input. First dimension of the target could be equal to the input or to 1.
If the first dimension of target is 1, the operator will broadcast target's
first dimension to input's first dimension. During backward propagation,
the user can decide whether to calculate the gradient of the input or
the target or both.
Both the input X and Y can carry the LoD (Level of Details) information.
Both the input X and Y can carry the LoD (Level of Details) information.
However, the output only shares the LoD information with input X.
)DOC");
}
......
paddle/fluid/operators/tdm_child_op.cc
浏览文件 @ 8dde7aea
......@@ -49,7 +49,7 @@ class TDMChildOpMaker : public framework::OpProtoAndCheckerMaker {
.SetDefault(2);
AddComment(R"DOC("
**Tdm Child**
According to the input node_id on the given tree, return the corresponding child node_id and
According to the input node_id on the given tree, return the corresponding child node_id and
whether child is a leaf node by LeafMask.")DOC");
}
};
......
paddle/fluid/operators/teacher_student_sigmoid_loss_op.cc
浏览文件 @ 8dde7aea
......@@ -226,7 +226,7 @@ It's similarity to SigmoidCrossEntropyWithLogits Operator. The difference is tha
we add another label(z') to original.
loss = max(x, 0) - x * z + log(1 + exp(-abs(x))) + max(x, 0) - x * z' + log(1 + exp(-abs(x)))
z is click or not
z' is teacher value
z' is teacher value
label = {-2, -1, [0, 2]}
when z' is not exist, clk = 0 : label = -2;
when z' is not exist, clk = 1 : label = -1;
......
paddle/fluid/operators/temporal_shift_op.cc
浏览文件 @ 8dde7aea
......@@ -74,20 +74,20 @@ class TemporalShiftOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
This operator calculates the temporal shifting features for Input(X).
Input(X) should be in shape of [N*T, C, H, W] or [N*T, H, W, C], while
N is the batch size, T is the temporal segment number specified by
:attr:`seg_num`, C is the channel number, H and W is the height and
Input(X) should be in shape of [N*T, C, H, W] or [N*T, H, W, C], while
N is the batch size, T is the temporal segment number specified by
:attr:`seg_num`, C is the channel number, H and W is the height and
width of features.
Temporal Shifting is calculated as follows when data format is NCHW:
Step 1: Reshape Input(X) to [N, T, C, H, W].
Step 2: Pad 0 to reshaping result in the 2nd(T) dimension with
padding width as 1 on each side, padding result will be in shape
Step 2: Pad 0 to reshaping result in the 2nd(T) dimension with
padding width as 1 on each side, padding result will be in shape
of [N, T+2, C, H, W].
Step 3: Assume :attr:`shift_ratio` is :math:`1/4`, slice padding
Step 3: Assume :attr:`shift_ratio` is :math:`1/4`, slice padding
result as follows:
$$
......@@ -100,10 +100,10 @@ class TemporalShiftOpMaker : public framework::OpProtoAndCheckerMaker {
slice3 = x[:, 1:T+1, C/2:, :, :]
$$
Step 4: Concatenate three slices along the 3rd(C) dimension and
Step 4: Concatenate three slices along the 3rd(C) dimension and
reshape result to [N*T, C, H, W].
For details of temporal shifting, please refer to paper:
For details of temporal shifting, please refer to paper:
`Temporal Shift Module <http://arxiv.org/abs/1811.08383>`_ .
)DOC");
......
paddle/fluid/operators/top_k_op.cc
浏览文件 @ 8dde7aea
......@@ -90,8 +90,8 @@ class TopkOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Top K operator
If the input is a vector (1d tensor), this operator finds the k largest
entries in the vector and outputs their values and indices as vectors.
If the input is a vector (1d tensor), this operator finds the k largest
entries in the vector and outputs their values and indices as vectors.
Thus values[j] is the j-th largest entry in input, and its index is indices[j].
For matrices, this operator computes the top k entries in each row. )DOC");
......
paddle/fluid/operators/top_k_v2_op.cc
浏览文件 @ 8dde7aea
......@@ -51,8 +51,8 @@ class TopkV2OpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Top K operator
If the input is a vector (1d tensor), this operator finds the k largest
entries in the vector and outputs their values and indices as vectors.
If the input is a vector (1d tensor), this operator finds the k largest
entries in the vector and outputs their values and indices as vectors.
Thus values[j] is the j-th largest entry in input, and its index is indices[j].
For matrices, this operator computes the top k entries in each row. )DOC");
......
paddle/fluid/operators/tril_indices_op.cc
浏览文件 @ 8dde7aea
......@@ -61,9 +61,9 @@ class TrilIndicesOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
TrilIndices Operator.
The tril_indices operator returns the indices of the lower triangular part of the matrix
whose rows and cols is knowed. It is a 2-by-x tensor,where the first row contains row coordinates
of all indices and the second row contains column coordinates. Indices are ordered based on
The tril_indices operator returns the indices of the lower triangular part of the matrix
whose rows and cols is knowed. It is a 2-by-x tensor,where the first row contains row coordinates
of all indices and the second row contains column coordinates. Indices are ordered based on
rows and then columns. The lower triangular part of the matrix is defined as the elements on
and below the diagonal.
......
paddle/fluid/operators/tril_triu_op.cc
浏览文件 @ 8dde7aea
......@@ -41,9 +41,9 @@ class TrilTriuOpMaker : public framework::OpProtoAndCheckerMaker {
TrilTriu Operator.
The tril operator returns the lower triangular part of the matrix (2-D tensor)
or batch of matrices $input$. The lower triangular part of the matrix is defined
or batch of matrices $input$. The lower triangular part of the matrix is defined
as the elements on and below the diagonal.
The triu operator returns the upper triangular part of a matrix (2-D tensor)
The triu operator returns the upper triangular part of a matrix (2-D tensor)
or batch of matrices $input$. The upper triangular part of the matrix is defined
as the elements on and above the diagonal.
The other elements of the result tensor out are set to 0.
......
paddle/fluid/operators/unfold_op.cc
浏览文件 @ 8dde7aea
......@@ -54,7 +54,7 @@ class UnfoldOpMaker : public framework::OpProtoAndCheckerMaker {
This Operator is used to extract sliding local blocks from a batched input tensor, also known
as im2col when operated on batched 2D image tensor. For each block under the convolution filter,
all element will be rearranged as a column. While the convolution filter sliding over the input
feature map, a series of such columns will be formed.
feature map, a series of such columns will be formed.
)DOC");
}
};
......
paddle/fluid/operators/unique_op.cc
浏览文件 @ 8dde7aea
......@@ -153,9 +153,9 @@ class UniqueOpMaker : public framework::OpProtoAndCheckerMaker {
.SetDefault(false);
AddComment(R"DOC(
1. Return a unique subsequence for 1-D input tensor, and an index tensor
pointing to this unique subsequence when Attr(is_sorted) is false. This
pointing to this unique subsequence when Attr(is_sorted) is false. This
means paddle.unique is called.
2. Returns the unique elements of X in ascending order when Attr(is_sorted)
is true. This means fluid.layers.unique is called.
)DOC");
......
paddle/fluid/operators/unique_with_counts_op.cc
浏览文件 @ 8dde7aea
......@@ -64,7 +64,7 @@ class UniqueWithCountsOpMaker : public framework::OpProtoAndCheckerMaker {
"the attr `dtype`");
AddOutput("Count", "A subsequence for the count of unique index");
AddComment(R"DOC(
Return a unique subsequence for 1-D input tensor, index tensor pointing to this unique subsequence,
Return a unique subsequence for 1-D input tensor, index tensor pointing to this unique subsequence,
and the subsequence for the count of unique index.
)DOC");
}
......
paddle/fluid/operators/var_conv_2d_op.cc
浏览文件 @ 8dde7aea
......@@ -51,9 +51,9 @@ void VarConv2dOpMaker::Make() {
AddComment(R"DOC(
Var Size Conv Operator
This operator calculate Out = \sigma \left ( W * X + b \right ),
This operator calculate Out = \sigma \left ( W * X + b \right ),
only support 2-D for X.
NOTE: only support 'float32' data type now.
)DOC");
......
paddle/fluid/platform/profiler/chrometracing_logger.cc
浏览文件 @ 8dde7aea
......@@ -130,10 +130,10 @@ void ChromeTracingLogger::LogMemTraceEventNode(
output_file_stream_ << string_format(
std::string(
R"JSON(
{
{
"name": "[memory]", "pid": %lld, "tid": "%lld(C++)",
"ts": %lld,
"ph": "i", "cat": "%s",
"ts": %lld,
"ph": "i", "cat": "%s",
"args": {
"place": "%s",
"addr": "%llu",
......@@ -196,10 +196,10 @@ void ChromeTracingLogger::LogHostTraceEventNode(
output_file_stream_ << string_format(
std::string(
R"JSON(
{
{
"name": "%s[%s]", "pid": %lld, "tid": "%lld(Python)",
"ts": %lld, "dur": %.3f,
"ph": "X", "cat": "%s",
"ph": "X", "cat": "%s",
"cname": "thread_state_runnable",
"args": {
"start_time": "%.3f us",
......@@ -223,10 +223,10 @@ void ChromeTracingLogger::LogHostTraceEventNode(
output_file_stream_ << string_format(
std::string(
R"JSON(
{
{
"name": "%s[%s]", "pid": %lld, "tid": "%lld(C++)",
"ts": %lld, "dur": %.3f,
"ph": "X", "cat": "%s",
"ph": "X", "cat": "%s",
"cname": "thread_state_runnable",
"args": {
"start_time": "%.3f us",
......@@ -263,10 +263,10 @@ void ChromeTracingLogger::LogHostTraceEventNode(
output_file_stream_ << string_format(
std::string(
R"JSON(
{
{
"name": "%s[%s]", "pid": %lld, "tid": "%lld(C++)",
"ts": %lld, "dur": %.3f,
"ph": "X", "cat": "%s",
"ph": "X", "cat": "%s",
"cname": "thread_state_runnable",
"args": {
"start_time": "%.3f us",
......@@ -304,10 +304,10 @@ void ChromeTracingLogger::LogRuntimeTraceEventNode(
output_file_stream_ << string_format(
std::string(
R"JSON(
{
{
"name": "%s[%s]", "pid": %lld, "tid": "%lld(C++)",
"ts": %lld, "dur": %.3f,
"ph": "X", "cat": "%s",
"ph": "X", "cat": "%s",
"cname": "thread_state_running",
"args": {
"correlation id": %d,
......@@ -331,9 +331,9 @@ void ChromeTracingLogger::LogRuntimeTraceEventNode(
output_file_stream_ << string_format(
std::string(
R"JSON(
{
{
"name": "launch", "id": %d, "pid": %lld, "tid": "%lld(C++)",
"ts": %lld,
"ts": %lld,
"ph": "s", "cat": "async"
},
)JSON"),
......@@ -365,9 +365,9 @@ void ChromeTracingLogger::LogDeviceTraceEventNode(
if (nsToUs(device_node.Duration()) == 0) {
output_file_stream_ << string_format(std::string(
R"JSON(
{
{
"name": "launch", "id": %d, "pid": %lld, "tid": %lld,
"ts": %lld,
"ts": %lld,
"ph": "f", "cat": "async"
},
)JSON"),
......@@ -381,9 +381,9 @@ void ChromeTracingLogger::LogDeviceTraceEventNode(
output_file_stream_ << string_format(
std::string(
R"JSON(
{
{
"name": "launch", "id": %d, "pid": %lld, "tid": %lld,
"ts": %lld,
"ts": %lld,
"ph": "f", "cat": "async", "bp": "e"
},
)JSON"),
......@@ -410,10 +410,10 @@ void ChromeTracingLogger::HandleTypeKernel(
output_file_stream_ << string_format(
std::string(
R"JSON(
{
{
"name": "%s[%s]", "pid": %lld, "tid": %lld,
"ts": %lld, "dur": %.3f,
"ph": "X", "cat": "%s",
"ph": "X", "cat": "%s",
"cname": "cq_build_failed",
"args": {
"start_time": "%.3f us",
......@@ -476,7 +476,7 @@ void ChromeTracingLogger::HandleTypeMemcpy(
{
"name": "%s[%s]", "pid": %lld, "tid": %lld,
"ts": %lld, "dur": %.3f,
"ph": "X", "cat": "%s",
"ph": "X", "cat": "%s",
"cname": "cq_build_failed",
"args": {
"start_time": "%.3f us",
......@@ -517,7 +517,7 @@ void ChromeTracingLogger::HandleTypeMemset(
{
"name": "%s[%s]", "pid": %lld, "tid": %lld,
"ts": %lld, "dur": %.3f,
"ph": "X", "cat": "%s",
"ph": "X", "cat": "%s",
"cname": "cq_build_failed",
"args": {
"start_time": "%.3f us",
......@@ -548,7 +548,7 @@ void ChromeTracingLogger::HandleTypeMemset(
void ChromeTracingLogger::StartLog() {
output_file_stream_ << std::string(
R"JSON(
{
{
"displayTimeUnit": "ms",)JSON");
}
......@@ -717,49 +717,49 @@ void ChromeTracingLogger::RefineDisplayName(
R"JSON(
{
"name": "process_name", "pid": %lld, "tid": "%lld(Python)",
"ph": "M",
"ph": "M",
"args": {
"name": "Process %lld (CPU)"
}
},
{
"name": "process_name", "pid": %lld, "tid": "%lld(C++)",
"ph": "M",
"ph": "M",
"args": {
"name": "Process %lld (CPU)"
}
},
{
"name": "thread_name", "pid": %lld, "tid": "%lld(Python)",
"ph": "M",
"ph": "M",
"args": {
"name": "thread %lld:%s(Python)"
}
},
{
"name": "thread_name", "pid": %lld, "tid": "%lld(C++)",
"ph": "M",
"ph": "M",
"args": {
"name": "thread %lld:%s(C++)"
}
},
{
"name": "process_sort_index", "pid": %lld, "tid": %lld,
"ph": "M",
"ph": "M",
"args": {
"sort_index": %lld
}
},
},
{
"name": "thread_sort_index", "pid": %lld, "tid": "%lld(Python)",
"ph": "M",
"ph": "M",
"args": {
"sort_index": %lld
}
},
{
"name": "thread_sort_index", "pid": %lld, "tid": "%lld(C++)",
"ph": "M",
"ph": "M",
"args": {
"sort_index": %lld
}
......@@ -803,32 +803,32 @@ void ChromeTracingLogger::RefineDisplayName(
R"JSON(
{
"name": "process_name", "pid": %lld, "tid": %lld,
"ph": "M",
"ph": "M",
"args": {
"name": "Deivce %lld (%s)"
}
},
{
"name": "thread_name", "pid": %lld, "tid": %lld,
"ph": "M",
"ph": "M",
"args": {
"name": "stream %lld"
}
},
{
"name": "process_sort_index", "pid": %lld, "tid": %lld,
"ph": "M",
"ph": "M",
"args": {
"sort_index": %lld
}
},
},
{
"name": "thread_sort_index", "pid": %lld, "tid": %lld,
"ph": "M",
"ph": "M",
"args": {
"sort_index": %lld
}
},
},
)JSON"),
(*it).first,
(*it).second,
......
paddle/fluid/pybind/cuda_streams_py.cc
浏览文件 @ 8dde7aea
......@@ -98,12 +98,12 @@ void BindCudaStream(py::module *m_ptr) {
The handle of the CUDA stream.
Parameters:
device(paddle.CUDAPlace()|int|None, optional): The device which wanted to allocate the stream.
If device is None or negative integer, device will be the current device.
If device is positive integer, it must less than the device count. Default: None.
device(paddle.CUDAPlace()|int|None, optional): The device which wanted to allocate the stream.
If device is None or negative integer, device will be the current device.
If device is positive integer, it must less than the device count. Default: None.
priority(int|None, optional): The priority of stream. The priority can be 1(high) or 2(normal).
If priority is None, the priority is 2(normal). Default: None.
If priority is None, the priority is 2(normal). Default: None.
Examples:
.. code-block:: python
......@@ -126,7 +126,7 @@ void BindCudaStream(py::module *m_ptr) {
Parameters:
event(CUDAEvent): The event to wait on.
Examples:
.. code-block:: python
......@@ -149,7 +149,7 @@ void BindCudaStream(py::module *m_ptr) {
Parameters:
stream(CUDAStream): The stream to synchronize with.
Examples:
.. code-block:: python
......@@ -207,7 +207,7 @@ void BindCudaStream(py::module *m_ptr) {
Parameters:
event(CUDAEvent, optional): The event to be record. If event is None, a new event is created.
Default: None.
Returns:
The recored event.
......@@ -238,7 +238,7 @@ void BindCudaStream(py::module *m_ptr) {
import ctypes
cuda_stream = paddle.device.cuda.current_stream().cuda_stream
print(cuda_stream)
ptr = ctypes.c_void_p(cuda_stream) # convert back to void*
print(ptr)
......@@ -322,7 +322,7 @@ void BindCudaStream(py::module *m_ptr) {
enable_timing(bool, optional): Whether the event will measure time. Default: False.
blocking(bool, optional): Whether the wait() func will be blocking. Default: False;
interprocess(bool, optional): Whether the event can be shared between processes. Defalut: False.
Examples:
.. code-block:: python
......@@ -345,7 +345,7 @@ void BindCudaStream(py::module *m_ptr) {
Parameters:
stream(CUDAStream, optional): The handle of CUDA stream. If None, the stream is the current stream. Default: None.
Examples:
.. code-block:: python
......@@ -353,7 +353,7 @@ void BindCudaStream(py::module *m_ptr) {
import paddle
event = paddle.device.cuda.Event()
event.record()
)DOC",
py::arg("stream") = nullptr)
.def(
......
paddle/fluid/pybind/imperative.cc
浏览文件 @ 8dde7aea
......@@ -1231,7 +1231,7 @@ void BindImperative(py::module *m_ptr) {
},
R"DOC(
Returns a numpy array shows the value of current Tensor.
Returns:
ndarray: The numpy value of current Tensor.
......@@ -1348,10 +1348,10 @@ void BindImperative(py::module *m_ptr) {
# Due to sharing of data with origin Tensor, There are some unsafe operations:
y = 2 * x
detach_x[:] = 5.0
y.backward()
y.backward()
# It will raise Error:
# one of the variables needed for gradient computation has been modified by an inplace operation.
)DOC")
.def("clear_gradient",
&imperative::VarBase::ClearGradient,
......@@ -1618,7 +1618,7 @@ void BindImperative(py::module *m_ptr) {
import paddle
x = paddle.to_tensor(1.0, place=paddle.CUDAPlace(0))
print(x.place) # CUDAPlace(0)
y = x.cpu()
print(y.place) # CPUPlace
......@@ -1708,12 +1708,12 @@ void BindImperative(py::module *m_ptr) {
R"DOC(
Returns a copy of this Tensor in GPU memory.
If this Tensor is already in GPU memory and device_id is default,
If this Tensor is already in GPU memory and device_id is default,
then no copy is performed and the original Tensor is returned.
Args:
device_id(int, optional): The destination GPU device id. Default: None, means current device.
blocking(bool, optional): If False and the source is in pinned memory, the copy will be
blocking(bool, optional): If False and the source is in pinned memory, the copy will be
asynchronous with respect to the host. Otherwise, the argument has no effect. Default: False.
Examples:
......@@ -1726,7 +1726,7 @@ void BindImperative(py::module *m_ptr) {
y = x.cuda()
print(y.place) # Place(gpu:0)
y = x.cuda(None)
print(y.place) # Place(gpu:0)
......@@ -2011,7 +2011,7 @@ void BindImperative(py::module *m_ptr) {
})
.def("element_size", &imperative::VarBase::ElementSize, R"DOC(
Returns the size in bytes of an element in the Tensor.
Examples:
.. code-block:: python
......@@ -2076,8 +2076,8 @@ void BindImperative(py::module *m_ptr) {
R"DOC(
Whether a Tensor is leaf Tensor.
For the Tensor whose stop_gradient is ``True`` , it will be leaf Tensor.
For the Tensor whose stop_gradient is ``True`` , it will be leaf Tensor.
For the Tensor whose stop_gradient is ``False`` , it will be leaf Tensor too if it is created by user.
Returns:
......@@ -2721,7 +2721,7 @@ void BindImperative(py::module *m_ptr) {
Returns:
new_tensor(paddle.Tensor): Return the UVA Tensor with the sample dtype and
new_tensor(paddle.Tensor): Return the UVA Tensor with the sample dtype and
shape with the input numpy array.
Examples:
......@@ -2730,7 +2730,7 @@ void BindImperative(py::module *m_ptr) {
# required: gpu
import numpy as np
import paddle
data = np.random.randint(10, size=(3, 4))
tensor = paddle.fluid.core.to_uva_tensor(data)
print(tensor)
......@@ -2834,38 +2834,38 @@ void BindImperative(py::module *m_ptr) {
}
},
R"DOC(
This api provides a way to write pieces of source tensor to destination tensor
inplacely and asynchronously. In which, we use `offset` and `count` to determine
where to copy. `offset` means the begin points of the copy pieces of `src`, and
`count` means the lengths of the copy pieces of `src`. To be noted, the copy process
will run asynchronously from cuda to pin memory. We can simply remember this as
This api provides a way to write pieces of source tensor to destination tensor
inplacely and asynchronously. In which, we use `offset` and `count` to determine
where to copy. `offset` means the begin points of the copy pieces of `src`, and
`count` means the lengths of the copy pieces of `src`. To be noted, the copy process
will run asynchronously from cuda to pin memory. We can simply remember this as
"gpu async_write to pin_memory".
Arguments:
src (Tensor): The source tensor, and the data type should be `float32` currently.
src (Tensor): The source tensor, and the data type should be `float32` currently.
Besides, `src` should be placed on CUDAPlace.
dst (Tensor): The destination tensor, and the data type should be `float32` currently.
Besides, `dst` should be placed on CUDAPinnedPlace. The shape of `dst`
should be the same with `src` except for the first dimension.
dst (Tensor): The destination tensor, and the data type should be `float32` currently.
Besides, `dst` should be placed on CUDAPinnedPlace. The shape of `dst`
should be the same with `src` except for the first dimension.
offset (Tensor): The offset tensor, and the data type should be `int64` currently.
Besides, `offset` should be placed on CPUPlace. The shape of `offset`
should be one-dimensional.
count (Tensor): The count tensor, and the data type should be `int64` currently.
Besides, `count` should be placed on CPUPlace. The shape of `count`
should be one-dimensinal.
offset (Tensor): The offset tensor, and the data type should be `int64` currently.
Besides, `offset` should be placed on CPUPlace. The shape of `offset`
should be one-dimensional.
count (Tensor): The count tensor, and the data type should be `int64` currently.
Besides, `count` should be placed on CPUPlace. The shape of `count`
should be one-dimensinal.
Examples:
.. code-block:: python
import numpy as np
import paddle
from paddle.fluid import core
from paddle.fluid import core
from paddle.device import cuda
if core.is_compiled_with_cuda():
src = paddle.rand(shape=[100, 50, 50])
dst = paddle.emtpy(shape=[200, 50, 50]).pin_memory()
......@@ -3058,38 +3058,38 @@ void BindImperative(py::module *m_ptr) {
stream);
},
R"DOC(
This api provides a way to read from pieces of source tensor to destination tensor
asynchronously. In which, we use `index`, `offset` and `count` to determine where
to read. `index` means the index position of src tensor we want to read. `offset`
and count means the begin points and length of pieces of src tensor we want to read.
To be noted, the copy process will run asynchronously from pin memory to cuda place.
This api provides a way to read from pieces of source tensor to destination tensor
asynchronously. In which, we use `index`, `offset` and `count` to determine where
to read. `index` means the index position of src tensor we want to read. `offset`
and count means the begin points and length of pieces of src tensor we want to read.
To be noted, the copy process will run asynchronously from pin memory to cuda place.
We can simply remember this as "cuda async_read from pin_memory".
Arguments:
src (Tensor): The source tensor, and the data type should be `float32` currently.
src (Tensor): The source tensor, and the data type should be `float32` currently.
Besides, `src` should be placed on CUDAPinnedPlace.
dst (Tensor): The destination tensor, and the data type should be `float32` currently.
Besides, `dst` should be placed on CUDAPlace. The shape of `dst` should
dst (Tensor): The destination tensor, and the data type should be `float32` currently.
Besides, `dst` should be placed on CUDAPlace. The shape of `dst` should
be the same with `src` except for the first dimension.
index (Tensor): The index tensor, and the data type should be `int64` currently.
Besides, `index` should be on CPUplace. The shape of `index` should
index (Tensor): The index tensor, and the data type should be `int64` currently.
Besides, `index` should be on CPUplace. The shape of `index` should
be one-dimensional.
buffer (Tensor): The buffer tensor, used to buffer index copy tensor temporarily.
The data type should be `float32` currently, and should be placed
buffer (Tensor): The buffer tensor, used to buffer index copy tensor temporarily.
The data type should be `float32` currently, and should be placed
on CUDAPinnedPlace. The shape of `buffer` should be the same with `src` except for the first dimension.
offset (Tensor): The offset tensor, and the data type should be `int64` currently.
Besides, `offset` should be placed on CPUPlace. The shape of `offset`
offset (Tensor): The offset tensor, and the data type should be `int64` currently.
Besides, `offset` should be placed on CPUPlace. The shape of `offset`
should be one-dimensional.
count (Tensor): The count tensor, and the data type should be `int64` currently.
Besides, `count` should be placed on CPUPlace. The shape of `count`
count (Tensor): The count tensor, and the data type should be `int64` currently.
Besides, `count` should be placed on CPUPlace. The shape of `count`
should be one-dimensinal.
Examples:
.. code-block:: python
......@@ -3108,11 +3108,11 @@ void BindImperative(py::module *m_ptr) {
buffer = paddle.empty(shape=[50, 50, 50], dtype="float32").pin_memory()
index = paddle.to_tensor(
np.array([1, 3, 5, 7, 9], dtype="int64")).cpu()
stream = cuda.Stream()
with cuda.stream_guard(stream):
core.async_read(src, dst, index, buffer, offset, count)
)DOC");
#endif
}
......
paddle/fluid/pybind/parallel_executor.cc
浏览文件 @ 8dde7aea
......@@ -296,9 +296,9 @@ void BindParallelExecutor(pybind11::module &m) { // NOLINT
Default 100.
.. note::
1. If you fetch data when calling the 'run', the ParallelExecutor
will clean up the temp variables at the end of the current iteration.
2. In some NLP model, it may cause the GPU memory is insufficient,
1. If you fetch data when calling the 'run', the ParallelExecutor
will clean up the temp variables at the end of the current iteration.
2. In some NLP model, it may cause the GPU memory is insufficient,
in this case, you should reduce `num_iteration_per_drop_scope`.
Examples:
......@@ -859,7 +859,7 @@ void BindParallelExecutor(pybind11::module &m) { // NOLINT
synchronous batch normalization which synchronizes the mean
and variance through multi-devices in training phase.
Current implementation doesn't support FP16 training and CPU.
And only synchronous on one machine, not all machines.
And only synchronous on one machine, not all machines.
Default is False.
Examples:
......@@ -897,9 +897,9 @@ void BindParallelExecutor(pybind11::module &m) { // NOLINT
R"DOC((bool, optional): memory opitimize aims to save total memory
consumption, set to True to enable it.
Default None. None means framework would choose to use or not use
this strategy automatically. Currently, None means that it is
enabled when GC is disabled, and disabled when GC is enabled.
Default None. None means framework would choose to use or not use
this strategy automatically. Currently, None means that it is
enabled when GC is disabled, and disabled when GC is enabled.
True means enabling and False means disabling. Default is None.
Examples:
......@@ -912,7 +912,7 @@ void BindParallelExecutor(pybind11::module &m) { // NOLINT
build_strategy = static.BuildStrategy()
build_strategy.memory_optimize = True
)DOC")
.def_property(
"is_distribution",
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ 8dde7aea
......@@ -1038,7 +1038,7 @@ All parameter, weight, gradient are variables in Paddle.
py::arg("name"),
R"DOC(
Find variable named :code:`name` in the current scope or
its parent scope. Return None if not found.
its parent scope. Return None if not found.
Args:
name (str): the variable name.
......@@ -1053,7 +1053,7 @@ All parameter, weight, gradient are variables in Paddle.
py::arg("names"),
R"DOC(
Find variable named :code:`name` in the current scope or
its parent scope. Return None if not found.
its parent scope. Return None if not found.
Args:
name (str): the variable names to be erase.
......@@ -1248,12 +1248,12 @@ All parameter, weight, gradient are variables in Paddle.
R"DOC(
Prune the backward part of a program, mostly called in
program.clone(for_test=True).
Args:
program (ProgramDesc): The original program.
Returns:
tuple(ProgramDesc, map<int, int>): The first part is
tuple(ProgramDesc, map<int, int>): The first part is
the pruned program desc, and the second part is a map
which contains the id pair of pruned block and corresponding
origin block.
......@@ -1873,7 +1873,7 @@ All parameter, weight, gradient are variables in Paddle.
py::arg("tensor"),
R"DOC(
Append a LoDensor to LoDTensorArray.
Args:
tensor (LoDTensor): The LoDTensor to be appended.
......
paddle/fluid/pybind/tensor.cc
浏览文件 @ 8dde7aea
......@@ -430,7 +430,7 @@ void BindTensor(pybind11::module &m) { // NOLINT
py::arg("zero_copy") = false,
R"DOC(
Set the data of Tensor on place with given numpy array.
Args:
lod (numpy.ndarray): The data to set.
place (CPUPlace|CUDAPlace|XPUPlace|IPUPlace|CUDAPinnedPlace|NPUPlace|MLUPlace): The place where the
......@@ -613,7 +613,7 @@ void BindTensor(pybind11::module &m) { // NOLINT
Args:
recursive_sequence_lengths (list[list[int]]): The recursive sequence lengths.
Returns:
None.
......@@ -644,7 +644,7 @@ void BindTensor(pybind11::module &m) { // NOLINT
Returns:
list[list[int]]: The lod of the Tensor.
Examples:
.. code-block:: python
......@@ -668,7 +668,7 @@ void BindTensor(pybind11::module &m) { // NOLINT
return new_lod;
},
R"DOC(
Return the recursive sequence lengths corresponding to of the LodD
Return the recursive sequence lengths corresponding to of the LodD
of the Tensor.
Returns:
......
paddle/phi/core/enforce.h
浏览文件 @ 8dde7aea
......@@ -425,7 +425,7 @@ struct EnforceNotMet : public std::exception {
* __ROLE: (string), Input or Output
* __NAME: (string), Input or Output name
* __OP_TYPE: (string), the op type
*  
*
* Return: The data pointed to by the pointer.
*
* Examples:
......
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