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“2fa3ce2b4a9b7f2bbd53ae58d0d0b9b27eb00ca2”上不存在“paddle/fluid/git@gitcode.net:BaiXuePrincess/Paddle.git”
未验证 提交 52979565 编写于 作者: Y Yuan Shuai 提交者： GitHub
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API/OP (clip_by_norm/clip) error message enhancement (#23603) 

* error message enhance for clip_by_norm. test=develop

* fix clip_by_norm. test=develop

* fix clip error message. test=develop
上级 b4b6763a
隐藏空白更改
内联 并排
Showing with 681 addition and 657 deletion
paddle/fluid/operators/clip_by_norm_op.h
浏览文件 @ 52979565
...@@ -67,7 +67,10 @@ class ClipByNormKernel : public framework::OpKernel<T> { ...@@ -67,7 +67,10 @@ class ClipByNormKernel : public framework::OpKernel<T> {
framework::ToTypeName(in_var->Type())); framework::ToTypeName(in_var->Type()));
} }
PADDLE_ENFORCE_NOT_NULL(input); PADDLE_ENFORCE_NOT_NULL(input,
platform::errors::InvalidArgument(
"Input(X) of ClipByNormOp should not be null. "
"Please check if it is created correctly."));
auto x = EigenVector<T>::Flatten(*input); auto x = EigenVector<T>::Flatten(*input);
auto out = EigenVector<T>::Flatten(*output); auto out = EigenVector<T>::Flatten(*output);
...@@ -89,12 +92,19 @@ class ClipByNormOp : public framework::OperatorWithKernel { ...@@ -89,12 +92,19 @@ class ClipByNormOp : public framework::OperatorWithKernel {
protected: protected:
void InferShape(framework::InferShapeContext* ctx) const override { void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
"Input(X) of ClipByNormOp should not be null."); platform::errors::InvalidArgument(
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Input(X) of ClipByNormOp should not be null. Please "
"Output(Out) of ClipByNormOp should not be null."); "check if it is created correctly."));
PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
platform::errors::InvalidArgument(
"Output(Out) of ClipByNormOp should not be null. "
"Please check if it is created correctly."));
auto max_norm = ctx->Attrs().Get<float>("max_norm"); auto max_norm = ctx->Attrs().Get<float>("max_norm");
PADDLE_ENFORCE_GT(max_norm, 0, "max_norm should be greater than 0."); PADDLE_ENFORCE_GT(max_norm, 0, platform::errors::InvalidArgument(
"max_norm should be greater than 0. "
"Received max_norm is %f.",
max_norm));
auto x_dims = ctx->GetInputDim("X"); auto x_dims = ctx->GetInputDim("X");
ctx->SetOutputDim("Out", x_dims); ctx->SetOutputDim("Out", x_dims);
ctx->ShareLoD("X", /*->*/ "Out"); ctx->ShareLoD("X", /*->*/ "Out");
......
paddle/fluid/operators/clip_op.cc
浏览文件 @ 52979565
...@@ -23,14 +23,21 @@ class ClipOp : public framework::OperatorWithKernel { ...@@ -23,14 +23,21 @@ class ClipOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel; using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override { void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
"Input(X) of ClipOp should not be null."); platform::errors::InvalidArgument(
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Input(X) of ClipOp should not be null. Please check "
"Output(Out) of ClipOp should not be null."); "if it is created correctly."));
PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
platform::errors::InvalidArgument(
"Output(Out) of ClipOp should not be null. Please "
"check if it is created correctly."));
auto x_dims = ctx->GetInputDim("X"); auto x_dims = ctx->GetInputDim("X");
auto max = ctx->Attrs().Get<float>("max"); auto max = ctx->Attrs().Get<float>("max");
auto min = ctx->Attrs().Get<float>("min"); auto min = ctx->Attrs().Get<float>("min");
PADDLE_ENFORCE_LT(min, max, "max should be greater than min."); PADDLE_ENFORCE_LT(min, max, platform::errors::InvalidArgument(
"Max of ClipOp should be greater than min. "
"Received max is %f, received min is %f.",
max, min));
ctx->SetOutputDim("Out", x_dims); ctx->SetOutputDim("Out", x_dims);
ctx->ShareLoD("X", /*->*/ "Out"); ctx->ShareLoD("X", /*->*/ "Out");
} }
...@@ -52,7 +59,7 @@ class ClipOpMaker : public framework::OpProtoAndCheckerMaker { ...@@ -52,7 +59,7 @@ class ClipOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC( AddComment(R"DOC(
Clip Operator. Clip Operator.
The clip operator limits the value of given input within an interval [min, max], The clip operator limits the value of given input within an interval [min, max],
just as the following equation, just as the following equation,
$$ $$
...@@ -68,9 +75,14 @@ class ClipOpGrad : public framework::OperatorWithKernel { ...@@ -68,9 +75,14 @@ class ClipOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel; using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override { void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); PADDLE_ENFORCE_EQ(
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), ctx->HasInput("X"), true,
"Input(Out@GRAD) should not be null"); platform::errors::InvalidArgument("Input(X) should not be null. Please "
"check if it is created correctly."));
PADDLE_ENFORCE_EQ(ctx->HasInput(framework::GradVarName("Out")), true,
platform::errors::InvalidArgument(
"Input(Out@GRAD) should not be null. Please check if "
"it is created correctly."));
auto x_dims = ctx->GetInputDim("X"); auto x_dims = ctx->GetInputDim("X");
if (ctx->HasOutput(framework::GradVarName("X"))) { if (ctx->HasOutput(framework::GradVarName("X"))) {
ctx->SetOutputDim(framework::GradVarName("X"), x_dims); ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 52979565
...@@ -381,7 +381,7 @@ def embedding(input, ...@@ -381,7 +381,7 @@ def embedding(input,
of output Tensor is generated by replacing the last dimension of the input Tensor shape of output Tensor is generated by replacing the last dimension of the input Tensor shape
with emb_size. with emb_size.
**Note:** The id in :attr:`input` must satisfy :math:`0 =< id < size[0]` , **Note:** The id in :attr:`input` must satisfy :math:`0 =< id < size[0]` ,
otherwise the program will throw an exception and exit. otherwise the program will throw an exception and exit.
.. code-block:: text .. code-block:: text
...@@ -399,12 +399,12 @@ def embedding(input, ...@@ -399,12 +399,12 @@ def embedding(input,
[[0.345249859, 0.124939536, ..., 0.194353745], [[0.345249859, 0.124939536, ..., 0.194353745],
[0.945345345, 0.435394634, ..., 0.435345365]], [0.945345345, 0.435394634, ..., 0.435345365]],
[[0.945345345, 0.435394634, ..., 0.435345365], [[0.945345345, 0.435394634, ..., 0.435345365],
[0.0, 0.0, ..., 0.0 ]]] # padding data [0.0, 0.0, ..., 0.0 ]]] # padding data
The input padding_idx is less than 0, it is automatically converted to padding_idx = -1 + 128 = 127 The input padding_idx is less than 0, it is automatically converted to padding_idx = -1 + 128 = 127
It will pad all-zero data when ids is 127. It will pad all-zero data when ids is 127.
Case 2: Case 2:
input is a LoDTensor with 1-level LoD. padding_idx = 0 input is a LoDTensor with 1-level LoD. padding_idx = 0
...@@ -429,22 +429,22 @@ def embedding(input, ...@@ -429,22 +429,22 @@ def embedding(input,
size(tuple|list): The shape of lookup table parameter. It should have two elements which size(tuple|list): The shape of lookup table parameter. It should have two elements which
indicates the size of the dictionary of embeddings and the size of each embedding vector respectively. indicates the size of the dictionary of embeddings and the size of each embedding vector respectively.
is_sparse(bool): The flag indicating whether to use sparse update. This parameter only is_sparse(bool): The flag indicating whether to use sparse update. This parameter only
affects the performance of the backwards gradient update. It is recommended to set affects the performance of the backwards gradient update. It is recommended to set
True because sparse update is faster. But some optimizer does not support sparse update, True because sparse update is faster. But some optimizer does not support sparse update,
such as :ref:`api_fluid_optimizer_AdadeltaOptimizer` , :ref:`api_fluid_optimizer_AdamaxOptimizer` , such as :ref:`api_fluid_optimizer_AdadeltaOptimizer` , :ref:`api_fluid_optimizer_AdamaxOptimizer` ,
:ref:`api_fluid_optimizer_DecayedAdagradOptimizer` , :ref:`api_fluid_optimizer_FtrlOptimizer` , :ref:`api_fluid_optimizer_DecayedAdagradOptimizer` , :ref:`api_fluid_optimizer_FtrlOptimizer` ,
:ref:`api_fluid_optimizer_LambOptimizer` and :ref:`api_fluid_optimizer_LarsMomentumOptimizer` . :ref:`api_fluid_optimizer_LambOptimizer` and :ref:`api_fluid_optimizer_LarsMomentumOptimizer` .
In these case, is_sparse must be False. Default: False. In these case, is_sparse must be False. Default: False.
is_distributed(bool): Whether to store the embedding matrix in a distributed manner. Only used is_distributed(bool): Whether to store the embedding matrix in a distributed manner. Only used
in multi-machine distributed CPU training. Default: False. in multi-machine distributed CPU training. Default: False.
padding_idx(int|long|None): padding_idx needs to be in the interval [-vocab_size, vocab_size). padding_idx(int|long|None): padding_idx needs to be in the interval [-vocab_size, vocab_size).
If :math:`padding\_idx < 0`, the :math:`padding\_idx` will automatically be converted If :math:`padding\_idx < 0`, the :math:`padding\_idx` will automatically be converted
to :math:`vocab\_size + padding\_idx` . It will output all-zero padding data whenever lookup to :math:`vocab\_size + padding\_idx` . It will output all-zero padding data whenever lookup
encounters :math:`padding\_idx` in id. And the padding data will not be updated while training. encounters :math:`padding\_idx` in id. And the padding data will not be updated while training.
If set None, it makes no effect to output. Default: None. If set None, it makes no effect to output. Default: None.
param_attr(ParamAttr): To specify the weight parameter property. Default: None, which means the param_attr(ParamAttr): To specify the weight parameter property. Default: None, which means the
default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` . In addition, default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` . In addition,
user-defined or pre-trained word vectors can be loaded with the :attr:`param_attr` parameter. user-defined or pre-trained word vectors can be loaded with the :attr:`param_attr` parameter.
The local word vector needs to be transformed into numpy format, and the shape of local word The local word vector needs to be transformed into numpy format, and the shape of local word
vector should be consistent with :attr:`size` . Then :ref:`api_fluid_initializer_NumpyArrayInitializer` vector should be consistent with :attr:`size` . Then :ref:`api_fluid_initializer_NumpyArrayInitializer`
is used to load custom or pre-trained word vectors. See code example 2 for details. is used to load custom or pre-trained word vectors. See code example 2 for details.
...@@ -471,7 +471,7 @@ def embedding(input, ...@@ -471,7 +471,7 @@ def embedding(input,
learning_rate=0.5, learning_rate=0.5,
initializer=fluid.initializer.NumpyArrayInitializer(weight_data), initializer=fluid.initializer.NumpyArrayInitializer(weight_data),
trainable=True) trainable=True)
emb_2 = fluid.layers.embedding(input=data, size=(128, 100), param_attr=w_param_attrs, dtype='float32') emb_2 = fluid.layers.embedding(input=data, size=(128, 100), param_attr=w_param_attrs, dtype='float32')
""" """
helper = LayerHelper('embedding', **locals()) helper = LayerHelper('embedding', **locals())
...@@ -652,11 +652,11 @@ def _pull_box_sparse(input, size, dtype='float32'): ...@@ -652,11 +652,11 @@ def _pull_box_sparse(input, size, dtype='float32'):
:attr:`input`. :attr:`input`.
Args: Args:
input(Variable|list of Variable): Input is a Tensor<int64> Variable, which input(Variable|list of Variable): Input is a Tensor<int64> Variable, which
contains the IDs information. contains the IDs information.
size(int): The embedding size parameter, which indicates the size of size(int): The embedding size parameter, which indicates the size of
each embedding vector respectively. each embedding vector respectively.
dtype(str): The dtype refers to the data type of output tensor. Only supports dtype(str): The dtype refers to the data type of output tensor. Only supports
float32 now. float32 now.
Returns: Returns:
...@@ -668,7 +668,7 @@ def _pull_box_sparse(input, size, dtype='float32'): ...@@ -668,7 +668,7 @@ def _pull_box_sparse(input, size, dtype='float32'):
import paddle.fluid as fluid import paddle.fluid as fluid
data = fluid.layers.data(name='sequence', shape=[1], dtype='int64', lod_level=1) data = fluid.layers.data(name='sequence', shape=[1], dtype='int64', lod_level=1)
emb = fluid.layers.pull_box_sparse(input=data, size=[11]) emb = fluid.layers.pull_box_sparse(input=data, size=[11])
""" """
helper = LayerHelper('pull_box_sparse', **locals()) helper = LayerHelper('pull_box_sparse', **locals())
if dtype != 'float32': if dtype != 'float32':
...@@ -699,7 +699,7 @@ def linear_chain_crf(input, label, param_attr=None, length=None): ...@@ -699,7 +699,7 @@ def linear_chain_crf(input, label, param_attr=None, length=None):
${comment} ${comment}
Args: Args:
input(${emission_type}): ${emission_comment} input(${emission_type}): ${emission_comment}
label(${label_type}): ${label_comment} label(${label_type}): ${label_comment}
Length(${length_type}): ${length_comment} Length(${length_type}): ${length_comment}
param_attr(ParamAttr): The attribute of the learnable parameter for transition parameter. param_attr(ParamAttr): The attribute of the learnable parameter for transition parameter.
...@@ -727,17 +727,17 @@ def linear_chain_crf(input, label, param_attr=None, length=None): ...@@ -727,17 +727,17 @@ def linear_chain_crf(input, label, param_attr=None, length=None):
label=label, label=label,
param_attr=fluid.ParamAttr( param_attr=fluid.ParamAttr(
name='crfw', name='crfw',
learning_rate=0.01)) learning_rate=0.01))
use_cuda = False use_cuda = False
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place) exe = fluid.Executor(place)
exe.run(startup_program) exe.run(startup_program)
#define data, using LoDTensor #define data, using LoDTensor
a = fluid.create_lod_tensor(np.random.rand(12,10).astype('float32'), [[3,3,4,2]], place) a = fluid.create_lod_tensor(np.random.rand(12,10).astype('float32'), [[3,3,4,2]], place)
b = fluid.create_lod_tensor(np.array([[1],[1],[2],[3],[1],[1],[1],[3],[1],[1],[1],[1]]),[[3,3,4,2]] , place) b = fluid.create_lod_tensor(np.array([[1],[1],[2],[3],[1],[1],[1],[3],[1],[1],[1],[1]]),[[3,3,4,2]] , place)
feed1 = {'input_data':a,'label':b} feed1 = {'input_data':a,'label':b}
loss= exe.run(train_program,feed=feed1, fetch_list=[crf_cost]) loss= exe.run(train_program,feed=feed1, fetch_list=[crf_cost])
print(loss) print(loss)
#define net structure, using padding #define net structure, using padding
train_program = fluid.Program() train_program = fluid.Program()
...@@ -766,7 +766,7 @@ def linear_chain_crf(input, label, param_attr=None, length=None): ...@@ -766,7 +766,7 @@ def linear_chain_crf(input, label, param_attr=None, length=None):
ll=np.array([[3],[3],[4],[2]]) ll=np.array([[3],[3],[4],[2]])
feed2 = {'input_data2':cc,'label2':dd,'length':ll} feed2 = {'input_data2':cc,'label2':dd,'length':ll}
loss2= exe.run(train_program,feed=feed2, fetch_list=[crf_cost2]) loss2= exe.run(train_program,feed=feed2, fetch_list=[crf_cost2])
print(loss2) print(loss2)
#[array([[ 7.8902354], #[array([[ 7.8902354],
# [ 7.3602567], # [ 7.3602567],
# [ 10.004011], # [ 10.004011],
...@@ -775,7 +775,7 @@ def linear_chain_crf(input, label, param_attr=None, length=None): ...@@ -775,7 +775,7 @@ def linear_chain_crf(input, label, param_attr=None, length=None):
#you can use find_var to get transition parameter. #you can use find_var to get transition parameter.
transition=np.array(fluid.global_scope().find_var('crfw').get_tensor()) transition=np.array(fluid.global_scope().find_var('crfw').get_tensor())
print(transition) print(transition)
""" """
helper = LayerHelper('linear_chain_crf', **locals()) helper = LayerHelper('linear_chain_crf', **locals())
size = input.shape[2] if length else input.shape[1] size = input.shape[2] if length else input.shape[1]
...@@ -819,12 +819,12 @@ def crf_decoding(input, param_attr, label=None, length=None): ...@@ -819,12 +819,12 @@ def crf_decoding(input, param_attr, label=None, length=None):
Args: Args:
input(${emission_type}): ${emission_comment} input(${emission_type}): ${emission_comment}
param_attr (ParamAttr|None): To specify the weight parameter attribute. param_attr (ParamAttr|None): To specify the weight parameter attribute.
Default: None, which means the default weight parameter property is Default: None, which means the default weight parameter property is
used. See usage for details in :ref:`api_fluid_ParamAttr` . used. See usage for details in :ref:`api_fluid_ParamAttr` .
label(${label_type}, optional): ${label_comment} label(${label_type}, optional): ${label_comment}
length(${length_type}, optional): ${length_comment} length(${length_type}, optional): ${length_comment}
Returns: Returns:
...@@ -840,10 +840,10 @@ def crf_decoding(input, param_attr, label=None, length=None): ...@@ -840,10 +840,10 @@ def crf_decoding(input, param_attr, label=None, length=None):
feature = fluid.data(name='word_emb', shape=[-1, 784], dtype='float32', lod_level=1) feature = fluid.data(name='word_emb', shape=[-1, 784], dtype='float32', lod_level=1)
label = fluid.data(name='label', shape=[-1, 1], dtype='int64', lod_level=1) label = fluid.data(name='label', shape=[-1, 1], dtype='int64', lod_level=1)
emission = fluid.layers.fc(input=feature, size=num_labels) emission = fluid.layers.fc(input=feature, size=num_labels)
crf_cost = fluid.layers.linear_chain_crf(input=emission, label=label, crf_cost = fluid.layers.linear_chain_crf(input=emission, label=label,
param_attr=fluid.ParamAttr(name="crfw")) param_attr=fluid.ParamAttr(name="crfw"))
crf_decode = fluid.layers.crf_decoding(input=emission, crf_decode = fluid.layers.crf_decoding(input=emission,
param_attr=fluid.ParamAttr(name="crfw")) param_attr=fluid.ParamAttr(name="crfw"))
# Common tensor example # Common tensor example
...@@ -853,8 +853,8 @@ def crf_decoding(input, param_attr, label=None, length=None): ...@@ -853,8 +853,8 @@ def crf_decoding(input, param_attr, label=None, length=None):
length = fluid.data(name='length', shape=[-1, 1], dtype='int64') length = fluid.data(name='length', shape=[-1, 1], dtype='int64')
emission = fluid.layers.fc(input=feature, size=num_labels, emission = fluid.layers.fc(input=feature, size=num_labels,
num_flatten_dims=2) num_flatten_dims=2)
crf_cost = fluid.layers.linear_chain_crf(input=emission, label=label, length=length, crf_cost = fluid.layers.linear_chain_crf(input=emission, label=label, length=length,
param_attr=fluid.ParamAttr(name="crfw_pad")) param_attr=fluid.ParamAttr(name="crfw_pad"))
crf_decode = fluid.layers.crf_decoding(input=emission, length=length, crf_decode = fluid.layers.crf_decoding(input=emission, length=length,
param_attr=fluid.ParamAttr(name="crfw_pad")) param_attr=fluid.ParamAttr(name="crfw_pad"))
...@@ -1166,7 +1166,7 @@ def softmax(input, use_cudnn=False, name=None, axis=-1): ...@@ -1166,7 +1166,7 @@ def softmax(input, use_cudnn=False, name=None, axis=-1):
This operator implements the softmax layer. The calculation process is as follows: This operator implements the softmax layer. The calculation process is as follows:
1. The dimension :attr:`axis` of the ``input`` will be permuted to the last. 1. The dimension :attr:`axis` of the ``input`` will be permuted to the last.
2. Then the input tensor will be logically flattened to a 2-D matrix. The matrix's 2. Then the input tensor will be logically flattened to a 2-D matrix. The matrix's
second dimension(row length) is the same as the dimension :attr:`axis` of the input second dimension(row length) is the same as the dimension :attr:`axis` of the input
tensor, and the first dimension(column length) is the product of all other tensor, and the first dimension(column length) is the product of all other
...@@ -1175,7 +1175,7 @@ def softmax(input, use_cudnn=False, name=None, axis=-1): ...@@ -1175,7 +1175,7 @@ def softmax(input, use_cudnn=False, name=None, axis=-1):
of the input tensor's dimension :attr:`axis`) vector of arbitrary real values to a of the input tensor's dimension :attr:`axis`) vector of arbitrary real values to a
K-dimensional vector of real values in the range [0, 1] that add up to 1. K-dimensional vector of real values in the range [0, 1] that add up to 1.
3. After the softmax operation is completed, the inverse operations of steps 1 and 2 3. After the softmax operation is completed, the inverse operations of steps 1 and 2
are performed to restore the two-dimensional matrix to the same dimension as the ``input``. are performed to restore the two-dimensional matrix to the same dimension as the ``input``.
It computes the exponential of the given dimension and the sum of exponential It computes the exponential of the given dimension and the sum of exponential
...@@ -1235,7 +1235,7 @@ def softmax(input, use_cudnn=False, name=None, axis=-1): ...@@ -1235,7 +1235,7 @@ def softmax(input, use_cudnn=False, name=None, axis=-1):
[0.97555875, 0.97555875, 0.93623955, 0.93623955]], [0.97555875, 0.97555875, 0.93623955, 0.93623955]],
[[0.00490169, 0.00490169, 0.00490169, 0.00490169], [[0.00490169, 0.00490169, 0.00490169, 0.00490169],
[0.26762315, 0.26762315, 0.26762315, 0.26762315], [0.26762315, 0.26762315, 0.26762315, 0.26762315],
[0.72747516, 0.72747516, 0.72747516, 0.72747516]]] [0.72747516, 0.72747516, 0.72747516, 0.72747516]]]
Args: Args:
input (Variable): The input variable. A multi-dimension ``Tensor`` with type float32 or float64. input (Variable): The input variable. A multi-dimension ``Tensor`` with type float32 or float64.
...@@ -1353,30 +1353,30 @@ def conv2d(input, ...@@ -1353,30 +1353,30 @@ def conv2d(input,
W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1 W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1
Args: Args:
input (Variable): The input is 4-D Tensor with shape [N, C, H, W], the data type input (Variable): The input is 4-D Tensor with shape [N, C, H, W], the data type
of input is float16 or float32 or float64. of input is float16 or float32 or float64.
num_filters(int): The number of filter. It is as same as the output num_filters(int): The number of filter. It is as same as the output
image channel. image channel.
filter_size (int|tuple): The filter size. If filter_size filter_size (int|tuple): The filter size. If filter_size
is a tuple, it must contain two integers, (filter_size_height, is a tuple, it must contain two integers, (filter_size_height,
filter_size_width). Otherwise, filter_size_height = filter_size_width =\ filter_size_width). Otherwise, filter_size_height = filter_size_width =\
filter_size. filter_size.
stride (int|tuple): The stride size. It means the stride in convolution. stride (int|tuple): The stride size. It means the stride in convolution.
If stride is a tuple, it must contain two integers, (stride_height, stride_width). If stride is a tuple, it must contain two integers, (stride_height, stride_width).
Otherwise, stride_height = stride_width = stride. Default: stride = 1. Otherwise, stride_height = stride_width = stride. Default: stride = 1.
padding (string|int|list|tuple): The padding size. It means the number of zero-paddings padding (string|int|list|tuple): The padding size. It means the number of zero-paddings
on both sides for each dimension.If `padding` is a string, either 'VALID' or on both sides for each dimension.If `padding` is a string, either 'VALID' or
'SAME' which is the padding algorithm. If padding size is a tuple or list, 'SAME' which is the padding algorithm. If padding size is a tuple or list,
it could be in three forms: `[pad_height, pad_width]` or it could be in three forms: `[pad_height, pad_width]` or
`[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`, and when `[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`, and when
`data_format` is `"NCHW"`, `padding` can be in the form `[[0,0], [0,0], `data_format` is `"NCHW"`, `padding` can be in the form `[[0,0], [0,0],
[pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`. [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
when `data_format` is `"NHWC"`, `pool_padding` can be in the form when `data_format` is `"NHWC"`, `pool_padding` can be in the form
`[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`. `[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
Default: padding = 0. Default: padding = 0.
dilation (int|tuple): The dilation size. It means the spacing between the kernel dilation (int|tuple): The dilation size. It means the spacing between the kernel
points. If dilation is a tuple, it must contain two integers, (dilation_height, points. If dilation is a tuple, it must contain two integers, (dilation_height,
dilation_width). Otherwise, dilation_height = dilation_width = dilation. dilation_width). Otherwise, dilation_height = dilation_width = dilation.
Default: dilation = 1. Default: dilation = 1.
groups (int): The groups number of the Conv2d Layer. According to grouped groups (int): The groups number of the Conv2d Layer. According to grouped
convolution in Alex Krizhevsky's Deep CNN paper: when group=2, convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
...@@ -1397,18 +1397,18 @@ def conv2d(input, ...@@ -1397,18 +1397,18 @@ def conv2d(input,
library is installed. Default: True library is installed. Default: True
act (str): Activation type, if it is set to None, activation is not appended. act (str): Activation type, if it is set to None, activation is not appended.
Default: None Default: None
name(str|None): For detailed information, please refer name(str|None): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and to :ref:`api_guide_Name`. Usually name is no need to set and
None by default. None by default.
data_format (str, optional): Specify the data format of the input, and the data format of the output data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. `[batch_size, input_channels, input_height, input_width]`.
Returns: Returns:
A Variable holding Tensor representing the conv2d, whose data type is the A Variable holding Tensor representing the conv2d, whose data type is the
same with input. If act is None, the tensor variable storing the convolution same with input. If act is None, the tensor variable storing the convolution
result, and if act is not None, the tensor variable storing convolution result, and if act is not None, the tensor variable storing convolution
and non-linearity activation result. and non-linearity activation result.
Raises: Raises:
...@@ -1416,7 +1416,7 @@ def conv2d(input, ...@@ -1416,7 +1416,7 @@ def conv2d(input,
ValueError: If `data_format` is not "NCHW" or "NHWC". ValueError: If `data_format` is not "NCHW" or "NHWC".
ValueError: If the channel dimmention of the input is less than or equal to zero. ValueError: If the channel dimmention of the input is less than or equal to zero.
ValueError: If `padding` is a string, but not "SAME" or "VALID". ValueError: If `padding` is a string, but not "SAME" or "VALID".
ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0 ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0
or the element corresponding to the input's channel is not 0. or the element corresponding to the input's channel is not 0.
ShapeError: If the input is not 4-D Tensor. ShapeError: If the input is not 4-D Tensor.
ShapeError: If the input's dimension size and filter's dimension size not equal. ShapeError: If the input's dimension size and filter's dimension size not equal.
...@@ -1621,18 +1621,18 @@ def conv3d(input, ...@@ -1621,18 +1621,18 @@ def conv3d(input,
W_{out}&= \\frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{strides[2]} + 1 W_{out}&= \\frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{strides[2]} + 1
Args: Args:
input (Variable): The input is 5-D Tensor with shape [N, C, D, H, W], the data input (Variable): The input is 5-D Tensor with shape [N, C, D, H, W], the data
type of input is float16 or float32 or float64. type of input is float16 or float32 or float64.
num_filters(int): The number of filter. It is as same as the output num_filters(int): The number of filter. It is as same as the output
image channel. image channel.
filter_size (int|tuple): The filter size. If filter_size is a tuple, filter_size (int|tuple): The filter size. If filter_size is a tuple,
it must contain three integers, (filter_size_depth, filter_size_height, it must contain three integers, (filter_size_depth, filter_size_height,
filter_size_width). Otherwise, filter_size_depth = filter_size_height = \ filter_size_width). Otherwise, filter_size_depth = filter_size_height = \
filter_size_width = filter_size. filter_size_width = filter_size.
stride (int|tuple): The stride size. It means the stride in convolution. If stride is a stride (int|tuple): The stride size. It means the stride in convolution. If stride is a
tuple, it must contain three integers, (stride_depth, stride_height, stride_width). tuple, it must contain three integers, (stride_depth, stride_height, stride_width).
Otherwise, stride_depth = stride_height = stride_width = stride. Default: stride = 1. Otherwise, stride_depth = stride_height = stride_width = stride. Default: stride = 1.
padding (string|int|list|tuple): The padding size. It means the number of zero-paddings padding (string|int|list|tuple): The padding size. It means the number of zero-paddings
on both sides for each dimension. If `padding` is a string, either 'VALID' or on both sides for each dimension. If `padding` is a string, either 'VALID' or
'SAME' which is the padding algorithm. If padding size is a tuple or list, 'SAME' which is the padding algorithm. If padding size is a tuple or list,
it could be in three forms: `[pad_depth, pad_height, pad_width]` or it could be in three forms: `[pad_depth, pad_height, pad_width]` or
...@@ -1642,9 +1642,9 @@ def conv3d(input, ...@@ -1642,9 +1642,9 @@ def conv3d(input,
when `data_format` is `"NDHWC"`, `pool_padding` can be in the form when `data_format` is `"NDHWC"`, `pool_padding` can be in the form
`[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`. `[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
Default: padding = 0. Default: padding = 0.
dilation (int|tuple): The dilation size. It means the spacing between the kernel points. dilation (int|tuple): The dilation size. It means the spacing between the kernel points.
If dilation is a tuple, it must contain three integers, (dilation_depth, dilation_height, If dilation is a tuple, it must contain three integers, (dilation_depth, dilation_height,
dilation_width). Otherwise, dilation_depth = dilation_height = dilation_width = dilation. dilation_width). Otherwise, dilation_depth = dilation_height = dilation_width = dilation.
Default: dilation = 1. Default: dilation = 1.
groups (int): The groups number of the Conv3d Layer. According to grouped groups (int): The groups number of the Conv3d Layer. According to grouped
convolution in Alex Krizhevsky's Deep CNN paper: when group=2, convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
...@@ -1665,18 +1665,18 @@ def conv3d(input, ...@@ -1665,18 +1665,18 @@ def conv3d(input,
library is installed. Default: True library is installed. Default: True
act (str): Activation type, if it is set to None, activation is not appended. act (str): Activation type, if it is set to None, activation is not appended.
Default: None. Default: None.
name(str|None): For detailed information, please refer name(str|None): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and to :ref:`api_guide_Name`. Usually name is no need to set and
None by default. None by default.
data_format (str, optional): Specify the data format of the input, and the data format of the output data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. `[batch_size, input_channels, input_height, input_width]`.
Returns: Returns:
A Variable holding Tensor representing the conv3d, whose data type is A Variable holding Tensor representing the conv3d, whose data type is
the same with input. If act is None, the tensor variable storing the the same with input. If act is None, the tensor variable storing the
convolution result, and if act is not None, the tensor variable storing convolution result, and if act is not None, the tensor variable storing
convolution and non-linearity activation result. convolution and non-linearity activation result.
Raises: Raises:
...@@ -1684,7 +1684,7 @@ def conv3d(input, ...@@ -1684,7 +1684,7 @@ def conv3d(input,
ValueError: If `data_format` is not "NCDHW" or "NDHWC". ValueError: If `data_format` is not "NCDHW" or "NDHWC".
ValueError: If the channel dimmention of the input is less than or equal to zero. ValueError: If the channel dimmention of the input is less than or equal to zero.
ValueError: If `padding` is a string, but not "SAME" or "VALID". ValueError: If `padding` is a string, but not "SAME" or "VALID".
ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0 ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0
or the element corresponding to the input's channel is not 0. or the element corresponding to the input's channel is not 0.
ShapeError: If the input is not 5-D Tensor. ShapeError: If the input is not 5-D Tensor.
ShapeError: If the input's dimension size and filter's dimension size not equal. ShapeError: If the input's dimension size and filter's dimension size not equal.
...@@ -2062,8 +2062,8 @@ def pool3d(input, ...@@ -2062,8 +2062,8 @@ def pool3d(input,
the number of channels, `D` is the depth of the feature, the number of channels, `D` is the depth of the feature,
`H` is the height of the feature, and `W` is the width `H` is the height of the feature, and `W` is the width
of the feature. of the feature.
pool_size (int|list|tuple): The pool kernel size. If pool kernel size pool_size (int|list|tuple): The pool kernel size. If pool kernel size
is a tuple or list, it must contain three integers, is a tuple or list, it must contain three integers,
(pool_size_Depth, pool_size_Height, pool_size_Width). (pool_size_Depth, pool_size_Height, pool_size_Width).
Otherwise, the pool kernel size will be the cube of an int. Otherwise, the pool kernel size will be the cube of an int.
pool_type (string): ${pooling_type_comment} pool_type (string): ${pooling_type_comment}
...@@ -2308,7 +2308,7 @@ def adaptive_pool2d(input, ...@@ -2308,7 +2308,7 @@ def adaptive_pool2d(input,
None by default. None by default.
Returns: Returns:
Variable: The output tensor of adaptive pooling result. The data type is same Variable: The output tensor of adaptive pooling result. The data type is same
as input tensor. as input tensor.
Raises: Raises:
...@@ -2441,7 +2441,7 @@ def adaptive_pool3d(input, ...@@ -2441,7 +2441,7 @@ def adaptive_pool3d(input,
Output(i ,j, k) &= \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)} Output(i ,j, k) &= \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
Args: Args:
input (Variable): The input tensor of pooling operator, which is a 5-D tensor with input (Variable): The input tensor of pooling operator, which is a 5-D tensor with
shape [N, C, D, H, W]. The format of input tensor is NCDHW, where shape [N, C, D, H, W]. The format of input tensor is NCDHW, where
N is batch size, C is the number of channels, D is the depth of the feature, N is batch size, C is the number of channels, D is the depth of the feature,
H is the height of the feature, and W is the width of the feature. H is the height of the feature, and W is the width of the feature.
...@@ -2609,7 +2609,7 @@ def batch_norm(input, ...@@ -2609,7 +2609,7 @@ def batch_norm(input,
y_i &\\gets \\gamma \\hat{x_i} + \\beta \\qquad &//\ scale\ and\ shift y_i &\\gets \\gamma \\hat{x_i} + \\beta \\qquad &//\ scale\ and\ shift
moving\_mean = moving\_mean * momentum + mini-batch\_mean * (1. - momentum) \\\\ moving\_mean = moving\_mean * momentum + mini-batch\_mean * (1. - momentum) \\\\
moving\_var = moving\_var * momentum + mini-batch\_var * (1. - momentum) moving\_var = moving\_var * momentum + mini-batch\_var * (1. - momentum)
moving_mean is global mean and moving_var is global variance. moving_mean is global mean and moving_var is global variance.
...@@ -2627,12 +2627,12 @@ def batch_norm(input, ...@@ -2627,12 +2627,12 @@ def batch_norm(input,
y_i &\\gets \\gamma \\hat{x_i} + \\beta y_i &\\gets \\gamma \\hat{x_i} + \\beta
Note: Note:
if build_strategy.sync_batch_norm=True, the batch_norm in network will use if build_strategy.sync_batch_norm=True, the batch_norm in network will use
sync_batch_norm automatically. sync_batch_norm automatically.
`is_test = True` can only be used in test program and inference program, `is_test` CANNOT be set to True in train program, if you want to use global status from pre_train model in train program, please set `use_global_stats = True`. `is_test = True` can only be used in test program and inference program, `is_test` CANNOT be set to True in train program, if you want to use global status from pre_train model in train program, please set `use_global_stats = True`.
Args: Args:
input(Variable): The rank of input variable can be 2, 3, 4, 5. The data type input(Variable): The rank of input variable can be 2, 3, 4, 5. The data type
is float16 or float32 or float64. is float16 or float32 or float64.
act(string, Default None): Activation type, linear|relu|prelu|... act(string, Default None): Activation type, linear|relu|prelu|...
is_test (bool, Default False): A flag indicating whether it is in is_test (bool, Default False): A flag indicating whether it is in
...@@ -2648,25 +2648,25 @@ def batch_norm(input, ...@@ -2648,25 +2648,25 @@ def batch_norm(input,
param_attr(ParamAttr|None): The parameter attribute for Parameter `scale` param_attr(ParamAttr|None): The parameter attribute for Parameter `scale`
of batch_norm. If it is set to None or one attribute of ParamAttr, batch_norm of batch_norm. If it is set to None or one attribute of ParamAttr, batch_norm
will create ParamAttr as param_attr, the name of scale can be set in ParamAttr. will create ParamAttr as param_attr, the name of scale can be set in ParamAttr.
If the Initializer of the param_attr is not set, the parameter is initialized If the Initializer of the param_attr is not set, the parameter is initialized
with Xavier. Default: None. with Xavier. Default: None.
bias_attr(ParamAttr|None): The parameter attribute for the bias of batch_norm. bias_attr(ParamAttr|None): The parameter attribute for the bias of batch_norm.
If it is set to None or one attribute of ParamAttr, batch_norm If it is set to None or one attribute of ParamAttr, batch_norm
will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr. will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr.
If the Initializer of the bias_attr is not set, the bias is initialized zero. If the Initializer of the bias_attr is not set, the bias is initialized zero.
Default: None. Default: None.
data_layout (str, optional): Specify the data format of the input, and the data format of the output data_layout (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. `[batch_size, input_channels, input_height, input_width]`.
in_place(bool, Default False): Make the input and output of batch norm reuse memory. in_place(bool, Default False): Make the input and output of batch norm reuse memory.
name(str|None): For detailed information, please refer to :ref:`api_guide_Name`. name(str|None): For detailed information, please refer to :ref:`api_guide_Name`.
Usually name is no need to set and None by default. Usually name is no need to set and None by default.
moving_mean_name(str, Default None): The name of moving_mean which store the global Mean. If it moving_mean_name(str, Default None): The name of moving_mean which store the global Mean. If it
is set to None, batch_norm will save global mean with a random name, otherwise, batch_norm is set to None, batch_norm will save global mean with a random name, otherwise, batch_norm
will save global mean with the string. will save global mean with the string.
moving_variance_name(str, Default None): The name of the moving_variance which store the global Variance. moving_variance_name(str, Default None): The name of the moving_variance which store the global Variance.
If it is set to None, batch_norm will save global variance with a random name, otherwise, batch_norm If it is set to None, batch_norm will save global variance with a random name, otherwise, batch_norm
will save global variance with the string. will save global variance with the string.
do_model_average_for_mean_and_var(bool, Default True): Whether parameter mean and variance should do model do_model_average_for_mean_and_var(bool, Default True): Whether parameter mean and variance should do model
average when model average is enabled. average when model average is enabled.
...@@ -2676,8 +2676,8 @@ def batch_norm(input, ...@@ -2676,8 +2676,8 @@ def batch_norm(input,
In train mode, when setting use_global_stats True, the global mean In train mode, when setting use_global_stats True, the global mean
and variance are also used during train period. and variance are also used during train period.
Returns: Returns:
A Variable holding Tensor which is the result after applying batch normalization on the input, A Variable holding Tensor which is the result after applying batch normalization on the input,
has same shape and data type with input. has same shape and data type with input.
Examples: Examples:
...@@ -2842,10 +2842,10 @@ def inplace_abn(input, ...@@ -2842,10 +2842,10 @@ def inplace_abn(input,
act_alpha=1.0): act_alpha=1.0):
""" """
**In-place Activation Batch Normalization Layer** **In-place Activation Batch Normalization Layer**
This layer calculates batch normalization and activation with in-place memory. This layer calculates batch normalization and activation with in-place memory.
For batch normalization calculations, see `fluid.layers.batch_norm`. For batch normalization calculations, see `fluid.layers.batch_norm`.
For in-place activation batch normalization, see `In-Place Activated BatchNorm for For in-place activation batch normalization, see `In-Place Activated BatchNorm for
Memory-Optimized Training of DNNs <https://arxiv.org/abs/1712.02616>`_ Memory-Optimized Training of DNNs <https://arxiv.org/abs/1712.02616>`_
`inplace_abn` only support activation type as `None`, `identity`, `leaky_relu`, `inplace_abn` only support activation type as `None`, `identity`, `leaky_relu`,
...@@ -2853,12 +2853,12 @@ def inplace_abn(input, ...@@ -2853,12 +2853,12 @@ def inplace_abn(input,
`inplace_abn` only support data type as `float32`, `float64` currently. `inplace_abn` only support data type as `float32`, `float64` currently.
Note: Note:
if build_strategy.sync_batch_norm=True, the batch_norm in network will use if build_strategy.sync_batch_norm=True, the batch_norm in network will use
sync_batch_norm automatically. sync_batch_norm automatically.
`is_test = True` can only be used in test program and inference program, `is_test` CANNOT be set to True in train program, if you want to use global status from pre_train model in train program, please set `use_global_stats = True`. `is_test = True` can only be used in test program and inference program, `is_test` CANNOT be set to True in train program, if you want to use global status from pre_train model in train program, please set `use_global_stats = True`.
Args: Args:
input(Variable): The rank of input variable can be 2, 3, 4, 5. The data type input(Variable): The rank of input variable can be 2, 3, 4, 5. The data type
is float16 or float32 or float64. is float16 or float32 or float64.
act(string, Default None): Activation type, linear|relu|prelu|... act(string, Default None): Activation type, linear|relu|prelu|...
is_test (bool, Default False): A flag indicating whether it is in is_test (bool, Default False): A flag indicating whether it is in
...@@ -2872,26 +2872,26 @@ def inplace_abn(input, ...@@ -2872,26 +2872,26 @@ def inplace_abn(input,
epsilon(float, Default 1e-05): A value added to the denominator for epsilon(float, Default 1e-05): A value added to the denominator for
numerical stability. Default is 1e-5. numerical stability. Default is 1e-5.
param_attr(ParamAttr|None): The parameter attribute for Parameter `scale` param_attr(ParamAttr|None): The parameter attribute for Parameter `scale`
of inplace_abn. If it is set to None or one attribute of ParamAttr, inplace_abn of inplace_abn. If it is set to None or one attribute of ParamAttr, inplace_abn
will create ParamAttr as param_attr, the name of scale can be set in ParamAttr. will create ParamAttr as param_attr, the name of scale can be set in ParamAttr.
If the Initializer of the param_attr is not set, the parameter is initialized If the Initializer of the param_attr is not set, the parameter is initialized
with Xavier. Default: None. with Xavier. Default: None.
bias_attr(ParamAttr|None): The parameter attribute for the bias of inplace_abn. bias_attr(ParamAttr|None): The parameter attribute for the bias of inplace_abn.
If it is set to None or one attribute of ParamAttr, inplace_abn If it is set to None or one attribute of ParamAttr, inplace_abn
will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr. will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr.
If the Initializer of the bias_attr is not set, the bias is initialized zero. If the Initializer of the bias_attr is not set, the bias is initialized zero.
Default: None. Default: None.
data_layout (str, optional): Specify the data format of the input, and the data format of the output data_layout (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. `[batch_size, input_channels, input_height, input_width]`.
name(str|None): For detailed information, please refer to :ref:`api_guide_Name`. name(str|None): For detailed information, please refer to :ref:`api_guide_Name`.
Usually name is no need to set and None by default. Usually name is no need to set and None by default.
moving_mean_name(str, Default None): The name of moving_mean which store the global Mean. If it moving_mean_name(str, Default None): The name of moving_mean which store the global Mean. If it
is set to None, inplace_abn will save global mean with a random name, otherwise, inplace_abn is set to None, inplace_abn will save global mean with a random name, otherwise, inplace_abn
will save global mean with the string. will save global mean with the string.
moving_variance_name(str, Default None): The name of the moving_variance which store the global Variance. moving_variance_name(str, Default None): The name of the moving_variance which store the global Variance.
If it is set to None, inplace_abn, will save global variance with a random name, otherwise, inplace_abn If it is set to None, inplace_abn, will save global variance with a random name, otherwise, inplace_abn
will save global variance with the string. will save global variance with the string.
do_model_average_for_mean_and_var(bool, Default True): Whether parameter mean and variance should do model do_model_average_for_mean_and_var(bool, Default True): Whether parameter mean and variance should do model
average when model average is enabled. average when model average is enabled.
...@@ -2904,8 +2904,8 @@ def inplace_abn(input, ...@@ -2904,8 +2904,8 @@ def inplace_abn(input,
inplace activative batch normalization will be used, and alpha parameter for activation inplace activative batch normalization will be used, and alpha parameter for activation
can be given by this parameter. can be given by this parameter.
Returns: Returns:
A Variable holding Tensor which is the result after applying batch normalization and activation on the input, A Variable holding Tensor which is the result after applying batch normalization and activation on the input,
has same shape and data type with input. has same shape and data type with input.
Examples: Examples:
...@@ -3042,7 +3042,7 @@ def instance_norm(input, ...@@ -3042,7 +3042,7 @@ def instance_norm(input,
DataLayout: NCHW `[batch, in_channels, in_height, in_width]` DataLayout: NCHW `[batch, in_channels, in_height, in_width]`
Refer to `Instance Normalization: The Missing Ingredient for Refer to `Instance Normalization: The Missing Ingredient for
Fast Stylization <https://arxiv.org/pdf/1607.08022.pdf>`_ Fast Stylization <https://arxiv.org/pdf/1607.08022.pdf>`_
for more details. for more details.
...@@ -3062,26 +3062,26 @@ def instance_norm(input, ...@@ -3062,26 +3062,26 @@ def instance_norm(input,
`H` means height of feature map, `W` means width of feature map. `H` means height of feature map, `W` means width of feature map.
Args: Args:
input(variable): The rank of input variable can be 2, 3, 4, 5. input(variable): The rank of input variable can be 2, 3, 4, 5.
The data type is float32 or float64. The data type is float32 or float64.
epsilon(float, Default 1e-05): A value added to the denominator for epsilon(float, Default 1e-05): A value added to the denominator for
numerical stability. Default is 1e-5. numerical stability. Default is 1e-5.
param_attr(ParamAttr|None): The parameter attribute for Parameter `scale` param_attr(ParamAttr|None): The parameter attribute for Parameter `scale`
of instance_norm. If it is set to None or one attribute of ParamAttr, instance_norm of instance_norm. If it is set to None or one attribute of ParamAttr, instance_norm
will create ParamAttr as param_attr, the name of scale can be set in ParamAttr. will create ParamAttr as param_attr, the name of scale can be set in ParamAttr.
If the Initializer of the param_attr is not set, the parameter is initialized If the Initializer of the param_attr is not set, the parameter is initialized
with Xavier. Default: None. with Xavier. Default: None.
bias_attr(ParamAttr|None): The parameter attribute for the bias of instance_norm. bias_attr(ParamAttr|None): The parameter attribute for the bias of instance_norm.
If it is set to None or one attribute of ParamAttr, instance_norm If it is set to None or one attribute of ParamAttr, instance_norm
will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr. will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr.
If the Initializer of the bias_attr is not set, the bias is initialized zero. If the Initializer of the bias_attr is not set, the bias is initialized zero.
Default: None. Default: None.
name(string, Default None): A name for this layer(optional). If set None, the layer name(string, Default None): A name for this layer(optional). If set None, the layer
will be named automatically. will be named automatically.
Returns: Returns:
A Variable holding Tensor which is the result after applying instance normalization on the input, A Variable holding Tensor which is the result after applying instance normalization on the input,
has same shape and data type with input. has same shape and data type with input.
Examples: Examples:
...@@ -3185,7 +3185,7 @@ def data_norm(input, ...@@ -3185,7 +3185,7 @@ def data_norm(input,
act(string, Default None): Activation type, linear|relu|prelu|... act(string, Default None): Activation type, linear|relu|prelu|...
epsilon(float, Default 1e-05): epsilon(float, Default 1e-05):
param_attr(ParamAttr): The parameter attribute for Parameter `scale`. param_attr(ParamAttr): The parameter attribute for Parameter `scale`.
data_layout (str, optional): Specify the data format of the input, and the data format of the output data_layout (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. `[batch_size, input_channels, input_height, input_width]`.
...@@ -3196,11 +3196,11 @@ def data_norm(input, ...@@ -3196,11 +3196,11 @@ def data_norm(input,
moving_variance_name(string, Default None): The name of the moving_variance which store the global Variance. moving_variance_name(string, Default None): The name of the moving_variance which store the global Variance.
do_model_average_for_mean_and_var(bool, Default True): Whether parameter mean and variance do_model_average_for_mean_and_var(bool, Default True): Whether parameter mean and variance
should do model average when model average is enabled. should do model average when model average is enabled.
slot_dim(int): The embedding dimension of one slot. Slot is a set of one specific feature. In pslib mode, we slot_dim(int): The embedding dimension of one slot. Slot is a set of one specific feature. In pslib mode, we
distinguish feature ids by slot and pull their embeddings from parameter server (pslib). The first distinguish feature ids by slot and pull their embeddings from parameter server (pslib). The first
place of the embedding is the historical show number (occurence time of this feature id with a label 0). place of the embedding is the historical show number (occurence time of this feature id with a label 0).
If the input of this op is concated by slot-wise embeddings, and the show number is zero when this slot If the input of this op is concated by slot-wise embeddings, and the show number is zero when this slot
is new or empty, the normalization result may be impractical. To avoid this, we add slot_dim to locate is new or empty, the normalization result may be impractical. To avoid this, we add slot_dim to locate
the show number and judge if the show number is zero. If so, we choose to skip normalization on this the show number and judge if the show number is zero. If so, we choose to skip normalization on this
embedding. embedding.
sync_stats(bool, Default False): When running with multiple GPU cards, using allreduce to sync the sync_stats(bool, Default False): When running with multiple GPU cards, using allreduce to sync the
...@@ -3213,7 +3213,7 @@ def data_norm(input, ...@@ -3213,7 +3213,7 @@ def data_norm(input,
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
hidden1 = fluid.data(name="hidden1", shape=[64, 200]) hidden1 = fluid.data(name="hidden1", shape=[64, 200])
...@@ -3452,7 +3452,7 @@ def group_norm(input, ...@@ -3452,7 +3452,7 @@ def group_norm(input,
Default: None, the default bias parameter attribute is used. For more information, please Default: None, the default bias parameter attribute is used. For more information, please
refer to :ref:`api_guide_ParamAttr` . refer to :ref:`api_guide_ParamAttr` .
act(str, optional): Activation to be applied to the output of group normalization. act(str, optional): Activation to be applied to the output of group normalization.
data_layout(str, optional): Specify the data format of the input, and the data format of the output data_layout(str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. `[batch_size, input_channels, input_height, input_width]`.
...@@ -3544,7 +3544,7 @@ def spectral_norm(weight, dim=0, power_iters=1, eps=1e-12, name=None): ...@@ -3544,7 +3544,7 @@ def spectral_norm(weight, dim=0, power_iters=1, eps=1e-12, name=None):
calculations with U and V for :attr:`power_iters` rounds. Calculations calculations with U and V for :attr:`power_iters` rounds. Calculations
as follows: as follows:
.. math:: .. math::
\mathbf{v} := \\frac{\mathbf{W}^{T} \mathbf{u}}{\|\mathbf{W}^{T} \mathbf{u}\|_2} \mathbf{v} := \\frac{\mathbf{W}^{T} \mathbf{u}}{\|\mathbf{W}^{T} \mathbf{u}\|_2}
...@@ -3558,7 +3558,7 @@ def spectral_norm(weight, dim=0, power_iters=1, eps=1e-12, name=None): ...@@ -3558,7 +3558,7 @@ def spectral_norm(weight, dim=0, power_iters=1, eps=1e-12, name=None):
\sigma(\mathbf{W}) = \mathbf{u}^{T} \mathbf{W} \mathbf{v} \sigma(\mathbf{W}) = \mathbf{u}^{T} \mathbf{W} \mathbf{v}
\mathbf{W} = \\frac{\mathbf{W}}{\sigma(\mathbf{W})} \mathbf{W} = \\frac{\mathbf{W}}{\sigma(\mathbf{W})}
Refer to `Spectral Normalization <https://arxiv.org/abs/1802.05957>`_ . Refer to `Spectral Normalization <https://arxiv.org/abs/1802.05957>`_ .
...@@ -3692,13 +3692,13 @@ def conv2d_transpose(input, ...@@ -3692,13 +3692,13 @@ def conv2d_transpose(input,
W_{out} &\in [ W^\prime_{out}, W^\prime_{out} + strides[1] ] W_{out} &\in [ W^\prime_{out}, W^\prime_{out} + strides[1] ]
Note: Note:
The conv2d_transpose can be seen as the backward of the conv2d. For conv2d, The conv2d_transpose can be seen as the backward of the conv2d. For conv2d,
when stride > 1, conv2d maps multiple input shape to the same output shape, when stride > 1, conv2d maps multiple input shape to the same output shape,
so for conv2d_transpose, when stride > 1, input shape maps multiple output shape. so for conv2d_transpose, when stride > 1, input shape maps multiple output shape.
If output_size is None, :math:`H_{out} = H^\prime_{out}, W_{out} = W^\prime_{out}`; If output_size is None, :math:`H_{out} = H^\prime_{out}, W_{out} = W^\prime_{out}`;
else, the :math:`H_{out}` of the output size must between :math:`H^\prime_{out}` else, the :math:`H_{out}` of the output size must between :math:`H^\prime_{out}`
and :math:`H^\prime_{out} + strides[0]`, and the :math:`W_{out}` of the output size must and :math:`H^\prime_{out} + strides[0]`, and the :math:`W_{out}` of the output size must
between :math:`W^\prime_{out}` and :math:`W^\prime_{out} + strides[1]`, between :math:`W^\prime_{out}` and :math:`W^\prime_{out} + strides[1]`,
conv2d_transpose can compute the kernel size automatically. conv2d_transpose can compute the kernel size automatically.
Args: Args:
...@@ -3710,15 +3710,15 @@ def conv2d_transpose(input, ...@@ -3710,15 +3710,15 @@ def conv2d_transpose(input,
tuple, it must contain two integers, (image_height, image_width). None if use tuple, it must contain two integers, (image_height, image_width). None if use
filter_size, padding, and stride to calculate output_size. filter_size, padding, and stride to calculate output_size.
If output_size and filter_size are specified at the same time, They If output_size and filter_size are specified at the same time, They
should follow the formula above. Default: None. output_size and filter_size should follow the formula above. Default: None. output_size and filter_size
should not be None at the same time. should not be None at the same time.
filter_size(int|tuple, optional): The filter size. If filter_size is a tuple, filter_size(int|tuple, optional): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_height, filter_size_width). it must contain two integers, (filter_size_height, filter_size_width).
Otherwise, filter_size_height = filter_size_width = filter_size. None if Otherwise, filter_size_height = filter_size_width = filter_size. None if
use output size to calculate filter_size. Default: None. filter_size and use output size to calculate filter_size. Default: None. filter_size and
output_size should not be None at the same time. output_size should not be None at the same time.
stride(int|tuple, optional): The stride size. It means the stride in transposed convolution. stride(int|tuple, optional): The stride size. It means the stride in transposed convolution.
If stride is a tuple, it must contain two integers, (stride_height, stride_width). If stride is a tuple, it must contain two integers, (stride_height, stride_width).
Otherwise, stride_height = stride_width = stride. Default: stride = 1. Otherwise, stride_height = stride_width = stride. Default: stride = 1.
padding(int|list|str|tuple, optional): The padding size. The padding argument effectively adds padding(int|list|str|tuple, optional): The padding size. The padding argument effectively adds
`dilation * (kernel - 1)` amount of zero-padding on both sides of input. If `padding` is a `dilation * (kernel - 1)` amount of zero-padding on both sides of input. If `padding` is a
...@@ -3731,12 +3731,12 @@ def conv2d_transpose(input, ...@@ -3731,12 +3731,12 @@ def conv2d_transpose(input,
when `data_format` is `'NHWC'`, `padding` can be in the form when `data_format` is `'NHWC'`, `padding` can be in the form
`[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`. `[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
Default: padding = 0. Default: padding = 0.
dilation(int|tuple, optional): The dilation size. It means the spacing between the kernel points. dilation(int|tuple, optional): The dilation size. It means the spacing between the kernel points.
If dilation is a tuple, it must contain two integers, (dilation_height, dilation_width). If dilation is a tuple, it must contain two integers, (dilation_height, dilation_width).
Otherwise, dilation_height = dilation_width = dilation. Default: dilation = 1. Otherwise, dilation_height = dilation_width = dilation. Default: dilation = 1.
filter_size(int|tuple, optional): The filter size. If filter_size is a tuple, filter_size(int|tuple, optional): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_height, filter_size_width). it must contain two integers, (filter_size_height, filter_size_width).
Otherwise, filter_size_height = filter_size_width = filter_size. None if Otherwise, filter_size_height = filter_size_width = filter_size. None if
use output size to calculate filter_size. Default: None. use output size to calculate filter_size. Default: None.
groups(int, optional): The groups number of the Conv2d transpose layer. Inspired by groups(int, optional): The groups number of the Conv2d transpose layer. Inspired by
grouped convolution in Alex Krizhevsky's Deep CNN paper, in which grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
...@@ -3757,27 +3757,27 @@ def conv2d_transpose(input, ...@@ -3757,27 +3757,27 @@ def conv2d_transpose(input,
library is installed. Default: True. library is installed. Default: True.
act (str, optional): Activation type, if it is set to None, activation is not appended. act (str, optional): Activation type, if it is set to None, activation is not appended.
Default: None. Default: None.
name(str, optional): For detailed information, please refer name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and to :ref:`api_guide_Name`. Usually name is no need to set and
None by default. None by default.
data_format (str, optional): Specify the data format of the input, and the data format of the output data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. `[batch_size, input_channels, input_height, input_width]`.
Returns: Returns:
A Variable holding Tensor representing the conv2d_transpose, whose A Variable holding Tensor representing the conv2d_transpose, whose
data type is the same with input and shape is (num_batches, channels, out_h, data type is the same with input and shape is (num_batches, channels, out_h,
out_w) or (num_batches, out_h, out_w, channels). If act is None, the tensor variable out_w) or (num_batches, out_h, out_w, channels). If act is None, the tensor variable
storing the transposed convolution result, and if act is not None, the storing the transposed convolution result, and if act is not None, the
tensor variable storing transposed convolution and non-linearity activation tensor variable storing transposed convolution and non-linearity activation
result. result.
Raises: Raises:
ValueError: If the type of `use_cudnn` is not bool. ValueError: If the type of `use_cudnn` is not bool.
ValueError: If `data_format` is not "NCHW" or "NHWC". ValueError: If `data_format` is not "NCHW" or "NHWC".
ValueError: If `padding` is a string, but not "SAME" or "VALID". ValueError: If `padding` is a string, but not "SAME" or "VALID".
ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0 ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0
or the element corresponding to the input's channel is not 0. or the element corresponding to the input's channel is not 0.
ValueError: If `output_size` and filter_size are None at the same time. ValueError: If `output_size` and filter_size are None at the same time.
ShapeError: If the input is not 4-D Tensor. ShapeError: If the input is not 4-D Tensor.
...@@ -3982,32 +3982,32 @@ def conv3d_transpose(input, ...@@ -3982,32 +3982,32 @@ def conv3d_transpose(input,
W_{out} &\in [ W^\prime_{out}, W^\prime_{out} + strides[2] ] W_{out} &\in [ W^\prime_{out}, W^\prime_{out} + strides[2] ]
Note: Note:
The conv3d_transpose can be seen as the backward of the conv3d. For conv3d, The conv3d_transpose can be seen as the backward of the conv3d. For conv3d,
when stride > 1, conv3d maps multiple input shape to the same output shape, when stride > 1, conv3d maps multiple input shape to the same output shape,
so for conv3d_transpose, when stride > 1, input shape maps multiple output shape. so for conv3d_transpose, when stride > 1, input shape maps multiple output shape.
If output_size is None, :math:`H_{out} = H^\prime_{out}, :math:`H_{out} = \ If output_size is None, :math:`H_{out} = H^\prime_{out}, :math:`H_{out} = \
H^\prime_{out}, W_{out} = W^\prime_{out}`; else, the :math:`D_{out}` of the output H^\prime_{out}, W_{out} = W^\prime_{out}`; else, the :math:`D_{out}` of the output
size must between :math:`D^\prime_{out}` and :math:`D^\prime_{out} + strides[0]`, size must between :math:`D^\prime_{out}` and :math:`D^\prime_{out} + strides[0]`,
the :math:`H_{out}` of the output size must between :math:`H^\prime_{out}` the :math:`H_{out}` of the output size must between :math:`H^\prime_{out}`
and :math:`H^\prime_{out} + strides[1]`, and the :math:`W_{out}` of the output size must and :math:`H^\prime_{out} + strides[1]`, and the :math:`W_{out}` of the output size must
between :math:`W^\prime_{out}` and :math:`W^\prime_{out} + strides[2]`, between :math:`W^\prime_{out}` and :math:`W^\prime_{out} + strides[2]`,
conv3d_transpose can compute the kernel size automatically. conv3d_transpose can compute the kernel size automatically.
Args: Args:
input(Variable): The input is 5-D Tensor with shape [N, C, D, H, W] or [N, D, H, W, C], the data type input(Variable): The input is 5-D Tensor with shape [N, C, D, H, W] or [N, D, H, W, C], the data type
of input is float32 or float64. of input is float32 or float64.
num_filters(int): The number of the filter. It is as same as the output num_filters(int): The number of the filter. It is as same as the output
image channel. image channel.
output_size(int|tuple, optional): The output image size. If output size is a output_size(int|tuple, optional): The output image size. If output size is a
tuple, it must contain three integers, (image_depth, image_height, image_width). This tuple, it must contain three integers, (image_depth, image_height, image_width). This
parameter only works when filter_size is None. If output_size and filter_size are parameter only works when filter_size is None. If output_size and filter_size are
specified at the same time, They should follow the formula above. Default: None. specified at the same time, They should follow the formula above. Default: None.
Output_size and filter_size should not be None at the same time. Output_size and filter_size should not be None at the same time.
filter_size(int|tuple, optional): The filter size. If filter_size is a tuple, filter_size(int|tuple, optional): The filter size. If filter_size is a tuple,
it must contain three integers, (filter_size_depth, filter_size_height, it must contain three integers, (filter_size_depth, filter_size_height,
filter_size_width). Otherwise, filter_size_depth = filter_size_height = \ filter_size_width). Otherwise, filter_size_depth = filter_size_height = \
filter_size_width = filter_size. None if use output size to filter_size_width = filter_size. None if use output size to
calculate filter_size. Default: None. filter_size and output_size should not be calculate filter_size. Default: None. filter_size and output_size should not be
None at the same time. None at the same time.
padding(int|list|str|tuple, optional): The padding size. The padding argument effectively padding(int|list|str|tuple, optional): The padding size. The padding argument effectively
adds `dilation * (kernel - 1)` amount of zero-padding on both sides of input. If `padding` is a string, adds `dilation * (kernel - 1)` amount of zero-padding on both sides of input. If `padding` is a string,
...@@ -4019,13 +4019,13 @@ def conv3d_transpose(input, ...@@ -4019,13 +4019,13 @@ def conv3d_transpose(input,
when `data_format` is `'NDHWC'`, `padding` can be in the form when `data_format` is `'NDHWC'`, `padding` can be in the form
`[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`. `[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
Default: padding = 0. Default: padding = 0.
stride(int|tuple, optional): The stride size. It means the stride in transposed convolution. stride(int|tuple, optional): The stride size. It means the stride in transposed convolution.
If stride is a tuple, it must contain three integers, (stride_depth, stride_height, If stride is a tuple, it must contain three integers, (stride_depth, stride_height,
stride_width). Otherwise, stride_depth = stride_height = stride_width = stride. stride_width). Otherwise, stride_depth = stride_height = stride_width = stride.
Default: stride = 1. Default: stride = 1.
dilation(int|tuple, optional): The dilation size. It means the spacing between the kernel points. dilation(int|tuple, optional): The dilation size. It means the spacing between the kernel points.
If dilation is a tuple, it must contain three integers, (dilation_depth, dilation_height, If dilation is a tuple, it must contain three integers, (dilation_depth, dilation_height,
dilation_width). Otherwise, dilation_depth = dilation_height = dilation_width = dilation. dilation_width). Otherwise, dilation_depth = dilation_height = dilation_width = dilation.
Default: dilation = 1. Default: dilation = 1.
groups(int, optional): The groups number of the Conv3d transpose layer. Inspired by groups(int, optional): The groups number of the Conv3d transpose layer. Inspired by
grouped convolution in Alex Krizhevsky's Deep CNN paper, in which grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
...@@ -4046,26 +4046,26 @@ def conv3d_transpose(input, ...@@ -4046,26 +4046,26 @@ def conv3d_transpose(input,
library is installed. Default: True library is installed. Default: True
act (str, optional): Activation type, if it is set to None, activation is not appended. act (str, optional): Activation type, if it is set to None, activation is not appended.
Default: None. Default: None.
name(str, optional): For detailed information, please refer name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and to :ref:`api_guide_Name`. Usually name is no need to set and
None by default. None by default.
data_format (str, optional): Specify the data format of the input, and the data format of the output data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. `[batch_size, input_channels, input_height, input_width]`.
Returns: Returns:
A Variable holding Tensor representing the conv3d_transpose, whose data A Variable holding Tensor representing the conv3d_transpose, whose data
type is the same with input and shape is (num_batches, channels, out_d, out_h, type is the same with input and shape is (num_batches, channels, out_d, out_h,
out_w) or (num_batches, out_d, out_h, out_w, channels). If act is None, the tensor out_w) or (num_batches, out_d, out_h, out_w, channels). If act is None, the tensor
variable storing the transposed convolution result, and if act is not None, the tensor variable storing the transposed convolution result, and if act is not None, the tensor
variable storing transposed convolution and non-linearity activation result. variable storing transposed convolution and non-linearity activation result.
Raises: Raises:
ValueError: If the type of `use_cudnn` is not bool. ValueError: If the type of `use_cudnn` is not bool.
ValueError: If `data_format` is not "NCDHW" or "NDHWC". ValueError: If `data_format` is not "NCDHW" or "NDHWC".
ValueError: If `padding` is a string, but not "SAME" or "VALID". ValueError: If `padding` is a string, but not "SAME" or "VALID".
ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0 ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0
or the element corresponding to the input's channel is not 0. or the element corresponding to the input's channel is not 0.
ValueError: If `output_size` and filter_size are None at the same time. ValueError: If `output_size` and filter_size are None at the same time.
ShapeError: If the input is not 5-D Tensor. ShapeError: If the input is not 5-D Tensor.
...@@ -4240,7 +4240,7 @@ def reduce_sum(input, dim=None, keep_dim=False, name=None): ...@@ -4240,7 +4240,7 @@ def reduce_sum(input, dim=None, keep_dim=False, name=None):
Raises: Raises:
TypeError, if out data type is different with the input data type. TypeError, if out data type is different with the input data type.
Examples: Examples:
.. code-block:: python .. code-block:: python
...@@ -4304,18 +4304,18 @@ def reduce_mean(input, dim=None, keep_dim=False, name=None): ...@@ -4304,18 +4304,18 @@ def reduce_mean(input, dim=None, keep_dim=False, name=None):
:math:`rank(input) + dim[i]`. :math:`rank(input) + dim[i]`.
keep_dim (bool, optional): Whether to reserve the reduced dimension in the keep_dim (bool, optional): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true, default than the :attr:`input` unless :attr:`keep_dim` is true, default
value is False. value is False.
name(str, optional): The default value is None. Normally there is no need for name(str, optional): The default value is None. Normally there is no need for
user to set this property. For more information, please refer to :ref:`api_guide_Name` user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns: Returns:
Variable: Tensor, results of average on the specified dim of input tensor, Variable: Tensor, results of average on the specified dim of input tensor,
it's data type is the same as input's Tensor. it's data type is the same as input's Tensor.
Raises: Raises:
TypeError, if out data type is different with the input data type. TypeError, if out data type is different with the input data type.
Examples: Examples:
.. code-block:: python .. code-block:: python
...@@ -4380,7 +4380,7 @@ def reduce_max(input, dim=None, keep_dim=False, name=None): ...@@ -4380,7 +4380,7 @@ def reduce_max(input, dim=None, keep_dim=False, name=None):
output Tensor. The result tensor will have one fewer dimension output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true, default than the :attr:`input` unless :attr:`keep_dim` is true, default
value is False. value is False.
name(str, optional): The default value is None. Normally there is no need for name(str, optional): The default value is None. Normally there is no need for
user to set this property. For more information, please refer to :ref:`api_guide_Name` user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns: Returns:
...@@ -4441,7 +4441,7 @@ def reduce_min(input, dim=None, keep_dim=False, name=None): ...@@ -4441,7 +4441,7 @@ def reduce_min(input, dim=None, keep_dim=False, name=None):
output Tensor. The result tensor will have one fewer dimension output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true, default than the :attr:`input` unless :attr:`keep_dim` is true, default
value is False. value is False.
name(str, optional): The default value is None. Normally there is no need for name(str, optional): The default value is None. Normally there is no need for
user to set this property. For more information, please refer to :ref:`api_guide_Name` user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns: Returns:
...@@ -4502,13 +4502,13 @@ def reduce_prod(input, dim=None, keep_dim=False, name=None): ...@@ -4502,13 +4502,13 @@ def reduce_prod(input, dim=None, keep_dim=False, name=None):
output Tensor. The result tensor will have one fewer dimension output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true, default than the :attr:`input` unless :attr:`keep_dim` is true, default
value is False. value is False.
name(str, optional): The default value is None. Normally there is no need for name(str, optional): The default value is None. Normally there is no need for
user to set this property. For more information, please refer to :ref:`api_guide_Name` user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns: Returns:
Variable: Tensor, result of product on the specified dim of input tensor, Variable: Tensor, result of product on the specified dim of input tensor,
it's data type is the same as input's Tensor. it's data type is the same as input's Tensor.
Examples: Examples:
.. code-block:: python .. code-block:: python
...@@ -4558,19 +4558,19 @@ def reduce_all(input, dim=None, keep_dim=False, name=None): ...@@ -4558,19 +4558,19 @@ def reduce_all(input, dim=None, keep_dim=False, name=None):
If :attr:`None`, compute the logical and over all elements of If :attr:`None`, compute the logical and over all elements of
:attr:`input` and return a Tensor variable with a single element, :attr:`input` and return a Tensor variable with a single element,
otherwise must be in the range :math:`[-rank(input), rank(input))`. otherwise must be in the range :math:`[-rank(input), rank(input))`.
If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`. The default value is None. If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`. The default value is None.
keep_dim (bool): Whether to reserve the reduced dimension in the keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true. The default value is False. than the :attr:`input` unless :attr:`keep_dim` is true. The default value is False.
name(str|None): A name for this layer(optional). If set None, the layer name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically. The default value is None. will be named automatically. The default value is None.
Returns: Returns:
Variable, the output data type is bool. : The reduced tensor variable with ``logical and`` in given dims. Variable, the output data type is bool. : The reduced tensor variable with ``logical and`` in given dims.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
import paddle.fluid.layers as layers import paddle.fluid.layers as layers
import numpy as np import numpy as np
...@@ -4581,7 +4581,7 @@ def reduce_all(input, dim=None, keep_dim=False, name=None): ...@@ -4581,7 +4581,7 @@ def reduce_all(input, dim=None, keep_dim=False, name=None):
x = layers.assign(np.array([[1, 0], [1, 1]], dtype='int32')) x = layers.assign(np.array([[1, 0], [1, 1]], dtype='int32'))
x = layers.cast(x, 'bool') x = layers.cast(x, 'bool')
out = layers.reduce_all(x) # False out = layers.reduce_all(x) # False
out = layers.reduce_all(x, dim=0) # [True, False] out = layers.reduce_all(x, dim=0) # [True, False]
out = layers.reduce_all(x, dim=-1) # [False, True] out = layers.reduce_all(x, dim=-1) # [False, True]
# keep_dim=False, x.shape=(2,2), out.shape=(2,) # keep_dim=False, x.shape=(2,2), out.shape=(2,)
...@@ -4616,13 +4616,13 @@ def reduce_any(input, dim=None, keep_dim=False, name=None): ...@@ -4616,13 +4616,13 @@ def reduce_any(input, dim=None, keep_dim=False, name=None):
If :attr:`None`, compute the logical and over all elements of If :attr:`None`, compute the logical and over all elements of
:attr:`input` and return a Tensor variable with a single element, :attr:`input` and return a Tensor variable with a single element,
otherwise must be in the range :math:`[-rank(input), rank(input))`. otherwise must be in the range :math:`[-rank(input), rank(input))`.
If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`. The default value is None. If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`. The default value is None.
keep_dim (bool): Whether to reserve the reduced dimension in the keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true. The default value is False. than the :attr:`input` unless :attr:`keep_dim` is true. The default value is False.
name(str|None): A name for this layer(optional). If set None, the layer name(str|None): A name for this layer(optional). If set None, the layer
Returns: Returns:
Variable, the output data type is bool. : The reduced tensor variable with ``logical or`` in given dims. Variable, the output data type is bool. : The reduced tensor variable with ``logical or`` in given dims.
Examples: Examples:
...@@ -4832,14 +4832,14 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None): ...@@ -4832,14 +4832,14 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None):
epsilon(float): The epsilon value is used to avoid division by zero, \ epsilon(float): The epsilon value is used to avoid division by zero, \
the default value is 1e-12. the default value is 1e-12.
name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns: Returns:
Variable: The output has the same shape and data type with `x`. Variable: The output has the same shape and data type with `x`.
Examples: Examples:
.. code-block:: python .. code-block:: python
# declarative mode # declarative mode
import paddle.fluid as fluid import paddle.fluid as fluid
import numpy as np import numpy as np
...@@ -4848,18 +4848,18 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None): ...@@ -4848,18 +4848,18 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None):
place = fluid.CPUPlace() place = fluid.CPUPlace()
exe = fluid.Executor(place) exe = fluid.Executor(place)
exe.run(fluid.default_startup_program()) exe.run(fluid.default_startup_program())
input_data = np.random.rand(2,3).astype("float32") input_data = np.random.rand(2,3).astype("float32")
print(input_data) print(input_data)
# [[0.5171216 0.12704141 0.56018186] # [[0.5171216 0.12704141 0.56018186]
# [0.93251234 0.5382788 0.81709313]] # [0.93251234 0.5382788 0.81709313]]
output_data = exe.run(fluid.default_main_program(), output_data = exe.run(fluid.default_main_program(),
feed={"input":input_data}, feed={"input":input_data},
fetch_list=[output], fetch_list=[output],
return_numpy=True) return_numpy=True)
print(output_data) print(output_data)
# [array([[0.48496857, 0.22970329, 0.56545246], # [array([[0.48496857, 0.22970329, 0.56545246],
...@@ -4872,10 +4872,10 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None): ...@@ -4872,10 +4872,10 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None):
input = dg.to_variable(input_data) input = dg.to_variable(input_data)
output = fluid.layers.l2_normalize(x=input, axis=-1) output = fluid.layers.l2_normalize(x=input, axis=-1)
print(output.numpy()) print(output.numpy())
# [[0.66907585 0.16437206 0.7247892 ] # [[0.66907585 0.16437206 0.7247892 ]
# [0.6899054 0.3982376 0.6045142 ]] # [0.6899054 0.3982376 0.6045142 ]]
""" """
if len(x.shape) == 1: if len(x.shape) == 1:
...@@ -5080,7 +5080,7 @@ def ctc_greedy_decoder(input, ...@@ -5080,7 +5080,7 @@ def ctc_greedy_decoder(input,
blanks and delete all blanks. blanks and delete all blanks.
This op is implemented in two modes: lod and padding, either of them can be used. This op is implemented in two modes: lod and padding, either of them can be used.
The input can be either LoDTensor or Tensor, corresponding to lod and padding The input can be either LoDTensor or Tensor, corresponding to lod and padding
mode respectively. mode respectively.
A simple example as below: A simple example as below:
...@@ -5134,7 +5134,7 @@ def ctc_greedy_decoder(input, ...@@ -5134,7 +5134,7 @@ def ctc_greedy_decoder(input,
step1: Apply argmax to first input sequence which is input.data[0:4]. Then we get: step1: Apply argmax to first input sequence which is input.data[0:4]. Then we get:
[[0], [2], [1], [0]], for input.data[4:8] is [[0], [3], [3], [0]], shape is [2,4,1] [[0], [2], [1], [0]], for input.data[4:8] is [[0], [3], [3], [0]], shape is [2,4,1]
step2: Change the argmax result to use padding mode, then argmax result is step2: Change the argmax result to use padding mode, then argmax result is
[[0, 2, 1, 0], [0, 3, 3, 0]], shape is [2, 4], lod is [], input_length is [[4], [4]] [[0, 2, 1, 0], [0, 3, 3, 0]], shape is [2, 4], lod is [], input_length is [[4], [4]]
step3: Apply ctc_align to padding argmax result, padding_value is 0 step3: Apply ctc_align to padding argmax result, padding_value is 0
...@@ -5146,8 +5146,8 @@ def ctc_greedy_decoder(input, ...@@ -5146,8 +5146,8 @@ def ctc_greedy_decoder(input,
Parameters: Parameters:
input(Variable): the probabilities of variable-length sequences. When in lod mode, input(Variable): the probabilities of variable-length sequences. When in lod mode,
it is a 2-D LoDTensor with LoD information. It's shape is [Lp, num_classes + 1] it is a 2-D LoDTensor with LoD information. It's shape is [Lp, num_classes + 1]
where Lp is the sum of all input sequences' length and where Lp is the sum of all input sequences' length and
num_classes is the true number of classes. When in padding mode, num_classes is the true number of classes. When in padding mode,
it is a 3-D Tensor with padding, It's shape is [batch_size, N, num_classes + 1]. it is a 3-D Tensor with padding, It's shape is [batch_size, N, num_classes + 1].
...@@ -5158,9 +5158,9 @@ def ctc_greedy_decoder(input, ...@@ -5158,9 +5158,9 @@ def ctc_greedy_decoder(input,
input_length(Variable, optional): 2-D LoDTensor, shape is [batch_size, 1], data type is int64. input_length(Variable, optional): 2-D LoDTensor, shape is [batch_size, 1], data type is int64.
It is used for padding mode. In lod mode, input_length is None. It is used for padding mode. In lod mode, input_length is None.
padding_value(int): padding value. padding_value(int): padding value.
name(str, optional): The default value is None. name(str, optional): The default value is None.
Normally there is no need for user to set this property. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` For more information, please refer to :ref:`api_guide_Name`
Returns: Returns:
For lod mode, returns the result of CTC greedy decoder, 2-D LoDTensor, shape is [Lp, 1], \ For lod mode, returns the result of CTC greedy decoder, 2-D LoDTensor, shape is [Lp, 1], \
...@@ -5168,7 +5168,7 @@ def ctc_greedy_decoder(input, ...@@ -5168,7 +5168,7 @@ def ctc_greedy_decoder(input,
in result were empty, the result LoDTensor will be [-1] with empty \ in result were empty, the result LoDTensor will be [-1] with empty \
LoD [[]]. LoD [[]].
For padding mode, returns a tuple of (output, output_length), which was described as below: For padding mode, returns a tuple of (output, output_length), which was described as below:
output, 2-D Tensor, shape is [batch_size, N], data type is int64. output, 2-D Tensor, shape is [batch_size, N], data type is int64.
...@@ -5353,7 +5353,7 @@ def im2sequence(input, ...@@ -5353,7 +5353,7 @@ def im2sequence(input,
paddings of four direction. Or it can contain two integers :math:`[padding\_height, padding\_width]` which means paddings of four direction. Or it can contain two integers :math:`[padding\_height, padding\_width]` which means
padding_up = padding_down = padding_height and padding_up = padding_down = padding_height and
padding_left = padding_right = padding_width. Otherwise, a scalar padding means padding_left = padding_right = padding_width. Otherwise, a scalar padding means
padding_up = padding_down = padding_left = padding_right = padding. padding_up = padding_down = padding_left = padding_right = padding.
Default is 0. Default is 0.
input_image_size(Variable, optional): the input contains image real size.It's dim input_image_size(Variable, optional): the input contains image real size.It's dim
...@@ -5366,9 +5366,9 @@ def im2sequence(input, ...@@ -5366,9 +5366,9 @@ def im2sequence(input,
name (str, optional): The default value is None. Normally there is no need for name (str, optional): The default value is None. Normally there is no need for
user to set this property. For more information, please refer to :ref:`api_guide_Name` . user to set this property. For more information, please refer to :ref:`api_guide_Name` .
Returns: Returns:
The output is a 2-D LoDTensor with shape {input.batch\_size * output\_height * output\_width, \ The output is a 2-D LoDTensor with shape {input.batch\_size * output\_height * output\_width, \
filter\_size\_height * filter\_size\_width * input.channels}. The data type is float32. filter\_size\_height * filter\_size\_width * input.channels}. The data type is float32.
Return Type: Variable Return Type: Variable
...@@ -5617,7 +5617,7 @@ def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None): ...@@ -5617,7 +5617,7 @@ def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
output= exe.run(feed={"x":x, "y":y}, output= exe.run(feed={"x":x, "y":y},
fetch_list=[result]) fetch_list=[result])
print(output) print(output)
#[array([[0.08220536], #[array([[0.08220536],
# [0.36652038], # [0.36652038],
# [0.20541131]], dtype=float32)] # [0.20541131]], dtype=float32)]
...@@ -5681,7 +5681,7 @@ def one_hot(input, depth, allow_out_of_range=False): ...@@ -5681,7 +5681,7 @@ def one_hot(input, depth, allow_out_of_range=False):
output: output:
Out.shape = [4, 4] Out.shape = [4, 4]
Out.data = [[0., 1., 0., 0.], Out.data = [[0., 1., 0., 0.],
[0., 1., 0., 0.], [0., 1., 0., 0.],
[0., 0., 0., 0.], # This id is 5, which goes beyond depth, so set it all-zeros data. [0., 0., 0., 0.], # This id is 5, which goes beyond depth, so set it all-zeros data.
[1., 0., 0., 0.]] [1., 0., 0., 0.]]
...@@ -5694,7 +5694,7 @@ def one_hot(input, depth, allow_out_of_range=False): ...@@ -5694,7 +5694,7 @@ def one_hot(input, depth, allow_out_of_range=False):
allow_out_of_range = False allow_out_of_range = False
output: Throw an exception for Illegal value output: Throw an exception for Illegal value
The second dimension in X is 5, which is greater than depth. The second dimension in X is 5, which is greater than depth.
Allow_out_of_range =False means that does not allow the word id to exceed depth, Allow_out_of_range =False means that does not allow the word id to exceed depth,
so it throws an exception. so it throws an exception.
...@@ -5702,7 +5702,7 @@ def one_hot(input, depth, allow_out_of_range=False): ...@@ -5702,7 +5702,7 @@ def one_hot(input, depth, allow_out_of_range=False):
input(Variable): Tensor or LoDTensor with shape :math:`[N_1, N_2, ..., N_k, 1]` , input(Variable): Tensor or LoDTensor with shape :math:`[N_1, N_2, ..., N_k, 1]` ,
which contains at least one dimension and the last dimension must be 1. which contains at least one dimension and the last dimension must be 1.
The data type is int32 or int64. The data type is int32 or int64.
depth(scalar): An integer defining the :attr:`depth` of the one hot dimension. If input depth(scalar): An integer defining the :attr:`depth` of the one hot dimension. If input
is word id, depth is generally the dictionary size. is word id, depth is generally the dictionary size.
allow_out_of_range(bool): A bool value indicating whether the input allow_out_of_range(bool): A bool value indicating whether the input
indices could be out of range :math:`[0, depth)` . When input indices are indices could be out of range :math:`[0, depth)` . When input indices are
...@@ -5754,8 +5754,8 @@ def one_hot(input, depth, allow_out_of_range=False): ...@@ -5754,8 +5754,8 @@ def one_hot(input, depth, allow_out_of_range=False):
def autoincreased_step_counter(counter_name=None, begin=1, step=1): def autoincreased_step_counter(counter_name=None, begin=1, step=1):
""" """
Create an auto-increase variable. which will be automatically increased Create an auto-increase variable. which will be automatically increased
by 1 in every iteration. By default, the first return of this counter is 1, by 1 in every iteration. By default, the first return of this counter is 1,
and the step size is 1. and the step size is 1.
Args: Args:
...@@ -5995,7 +5995,7 @@ def squeeze(input, axes, name=None): ...@@ -5995,7 +5995,7 @@ def squeeze(input, axes, name=None):
to one will be deleted. to one will be deleted.
.. code-block:: text .. code-block:: text
Case1: Case1:
...@@ -6314,20 +6314,20 @@ def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None, ...@@ -6314,20 +6314,20 @@ def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None,
Args: Args:
input (Variable): Input feature, 4D-Tensor with the shape of [N,C,H,W] or [N, H, W, C], input (Variable): Input feature, 4D-Tensor with the shape of [N,C,H,W] or [N, H, W, C],
where N is the batch size, C is the input channel, H is Height, W is weight. The data where N is the batch size, C is the input channel, H is Height, W is weight. The data
type is float32. The rank of this tensor must be 4, otherwise it will raise ValueError. type is float32. The rank of this tensor must be 4, otherwise it will raise ValueError.
n (int, optional): The number of channels to sum over. Default: 5 n (int, optional): The number of channels to sum over. Default: 5
k (float, optional): An offset, positive. Default: 1.0 k (float, optional): An offset, positive. Default: 1.0
alpha (float, optional): The scaling parameter, positive. Default:1e-4 alpha (float, optional): The scaling parameter, positive. Default:1e-4
beta (float, optional): The exponent, positive. Default:0.75 beta (float, optional): The exponent, positive. Default:0.75
name (str, optional): The default value is None. Normally there is no need for user to set name (str, optional): The default value is None. Normally there is no need for user to set
this property. For more information, please refer to :ref:`api_guide_Name` this property. For more information, please refer to :ref:`api_guide_Name`
data_format (str, optional): Specify the data format of the input, and the data format of the output data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. `[batch_size, input_channels, input_height, input_width]`.
Returns: Returns:
Variable: A tensor variable storing the transformation result with the same shape and data type as input. Variable: A tensor variable storing the transformation result with the same shape and data type as input.
...@@ -6408,11 +6408,11 @@ def pad(x, paddings, pad_value=0., name=None): ...@@ -6408,11 +6408,11 @@ def pad(x, paddings, pad_value=0., name=None):
x (Variable): Tensor, data type is float32. x (Variable): Tensor, data type is float32.
paddings (list): A list of integers. Its elements specify the padded paddings (list): A list of integers. Its elements specify the padded
width before and after each dimension in turn. width before and after each dimension in turn.
The length of :attr:`paddings` must be equal to The length of :attr:`paddings` must be equal to
:math:`rank(x) \\times 2`. :math:`rank(x) \\times 2`.
pad_value (float): The constant value used to pad. pad_value (float): The constant value used to pad.
name(str, optional): The default value is None. name(str, optional): The default value is None.
Normally there is no need for user to set this property. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` For more information, please refer to :ref:`api_guide_Name`
Returns: Returns:
...@@ -6484,11 +6484,11 @@ def pad_constant_like(x, y, pad_value=0., name=None): ...@@ -6484,11 +6484,11 @@ def pad_constant_like(x, y, pad_value=0., name=None):
[ 0, 0, 0]], [ 0, 0, 0]],
[[41, 42, 43], [[41, 42, 43],
[ 0, 0, 0]]], [ 0, 0, 0]]],
[[[ 0, 0, 0], [[[ 0, 0, 0],
[ 0, 0, 0]], [ 0, 0, 0]],
[[ 0, 0, 0], [[ 0, 0, 0],
[ 0, 0, 0]], [ 0, 0, 0]],
[[ 0, 0, 0], [[ 0, 0, 0],
[ 0, 0, 0]]]] [ 0, 0, 0]]]]
Out.shape = [2, 3, 2, 3] Out.shape = [2, 3, 2, 3]
...@@ -6496,11 +6496,11 @@ def pad_constant_like(x, y, pad_value=0., name=None): ...@@ -6496,11 +6496,11 @@ def pad_constant_like(x, y, pad_value=0., name=None):
Args: Args:
x (Variable): Tensor, its shape specifies the shape of output. x (Variable): Tensor, its shape specifies the shape of output.
y (Variable): Tensor, its rank is the same with :attr:`x`, and for each dimension :math:`i` , y (Variable): Tensor, its rank is the same with :attr:`x`, and for each dimension :math:`i` ,
:math:`y\_shape[i] <= x\_shape[i]` . The data type can be float32 or float64. :math:`y\_shape[i] <= x\_shape[i]` . The data type can be float32 or float64.
pad_value (float): The constant value used to pad. pad_value (float): The constant value used to pad.
name(str, optional): The default value is None. name(str, optional): The default value is None.
Normally there is no need for user to set this property. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` For more information, please refer to :ref:`api_guide_Name`
Returns: Returns:
...@@ -6538,8 +6538,8 @@ def label_smooth(label, ...@@ -6538,8 +6538,8 @@ def label_smooth(label,
dtype="float32", dtype="float32",
name=None): name=None):
""" """
Label smoothing is a mechanism to regularize the classifier layer and is called Label smoothing is a mechanism to regularize the classifier layer and is called
label-smoothing regularization (LSR). label-smoothing regularization (LSR).
Label smoothing is proposed to encourage the model to be less confident, Label smoothing is proposed to encourage the model to be less confident,
since optimizing the log-likelihood of the correct label directly may since optimizing the log-likelihood of the correct label directly may
...@@ -6560,20 +6560,20 @@ def label_smooth(label, ...@@ -6560,20 +6560,20 @@ def label_smooth(label,
Parameters: Parameters:
label(Variable): The input variable containing the label data. The label(Variable): The input variable containing the label data. The
label data should use one-hot representation. It's label data should use one-hot representation. It's
a multidimensional tensor with a shape of a multidimensional tensor with a shape of
:math:`[N_1, ..., Depth]`, where Depth is class number. :math:`[N_1, ..., Depth]`, where Depth is class number.
prior_dist(Variable, optional): The prior distribution to be used to smooth prior_dist(Variable, optional): The prior distribution to be used to smooth
labels. If not provided, an uniform distribution labels. If not provided, an uniform distribution
is used. It's a multidimensional tensor with a shape of is used. It's a multidimensional tensor with a shape of
:math:`[1, class\_num]` . The default value is None. :math:`[1, class\_num]` . The default value is None.
epsilon(float, optional): The weight used to mix up the original ground-truth epsilon(float, optional): The weight used to mix up the original ground-truth
distribution and the fixed distribution. The default value is distribution and the fixed distribution. The default value is
0.1. 0.1.
dtype(np.dtype|core.VarDesc.VarType|str, optional): The data type can be set dtype(np.dtype|core.VarDesc.VarType|str, optional): The data type can be set
as 'float32', 'float64'. The default value is 'float32'. as 'float32', 'float64'. The default value is 'float32'.
name(str, optional): The default value is None. Normally there is no need for user name(str, optional): The default value is None. Normally there is no need for user
to set this property. For more information, please refer to to set this property. For more information, please refer to
:ref:`api_guide_Name`. :ref:`api_guide_Name`.
Returns: Returns:
...@@ -6581,7 +6581,7 @@ def label_smooth(label, ...@@ -6581,7 +6581,7 @@ def label_smooth(label,
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
import paddle.fluid.layers as layers import paddle.fluid.layers as layers
...@@ -6617,17 +6617,17 @@ def roi_pool(input, ...@@ -6617,17 +6617,17 @@ def roi_pool(input,
spatial_scale=1.0, spatial_scale=1.0,
rois_lod=None): rois_lod=None):
""" """
This operator implements the roi_pooling layer. This operator implements the roi_pooling layer.
Region of interest pooling (also known as RoI pooling) is to perform max pooling on inputs of nonuniform sizes to obtain fixed-size feature maps (e.g. 7*7). Region of interest pooling (also known as RoI pooling) is to perform max pooling on inputs of nonuniform sizes to obtain fixed-size feature maps (e.g. 7*7).
The operator has three steps: The operator has three steps:
1. Dividing each region proposal into equal-sized sections with the pooled_width and pooled_height; 1. Dividing each region proposal into equal-sized sections with the pooled_width and pooled_height;
2. Finding the largest value in each section; 2. Finding the largest value in each section;
3. Copying these max values to the output buffer. 3. Copying these max values to the output buffer.
For more information, please refer to https://stackoverflow.com/questions/43430056/what-is-roi-layer-in-fast-rcnn For more information, please refer to https://stackoverflow.com/questions/43430056/what-is-roi-layer-in-fast-rcnn
Args: Args:
input (Variable): Input feature, 4D-Tensor with the shape of [N,C,H,W], where N is the batch size, C is the input channel, H is Height, W is weight. The data type is float32 or float64. input (Variable): Input feature, 4D-Tensor with the shape of [N,C,H,W], where N is the batch size, C is the input channel, H is Height, W is weight. The data type is float32 or float64.
rois (Variable): ROIs (Regions of Interest) to pool over. 2D-LoDTensor with the shape of [num_rois,4], the lod level is 1. Given as [[x1, y1, x2, y2], ...], (x1, y1) is the top left coordinates, and (x2, y2) is the bottom right coordinates. rois (Variable): ROIs (Regions of Interest) to pool over. 2D-LoDTensor with the shape of [num_rois,4], the lod level is 1. Given as [[x1, y1, x2, y2], ...], (x1, y1) is the top left coordinates, and (x2, y2) is the bottom right coordinates.
...@@ -6635,30 +6635,30 @@ def roi_pool(input, ...@@ -6635,30 +6635,30 @@ def roi_pool(input,
pooled_height (int, optional): The pooled output height, data type is int32. Default: 1 pooled_height (int, optional): The pooled output height, data type is int32. Default: 1
pooled_width (int, optional): The pooled output height, data type is int32. Default: 1 pooled_width (int, optional): The pooled output height, data type is int32. Default: 1
spatial_scale (float, optional): Multiplicative spatial scale factor to translate ROI coords from their input scale to the scale used when pooling. Default: 1.0 spatial_scale (float, optional): Multiplicative spatial scale factor to translate ROI coords from their input scale to the scale used when pooling. Default: 1.0
Returns: Returns:
Variable: The pooled feature, 4D-Tensor with the shape of [num_rois, C, pooled_height, pooled_width]. Variable: The pooled feature, 4D-Tensor with the shape of [num_rois, C, pooled_height, pooled_width].
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
import numpy as np import numpy as np
DATATYPE='float32' DATATYPE='float32'
place = fluid.CPUPlace() place = fluid.CPUPlace()
#place = fluid.CUDAPlace(0) #place = fluid.CUDAPlace(0)
input_data = np.array([i for i in range(1,17)]).reshape(1,1,4,4).astype(DATATYPE) input_data = np.array([i for i in range(1,17)]).reshape(1,1,4,4).astype(DATATYPE)
roi_data =fluid.create_lod_tensor(np.array([[1., 1., 2., 2.], [1.5, 1.5, 3., 3.]]).astype(DATATYPE),[[2]], place) roi_data =fluid.create_lod_tensor(np.array([[1., 1., 2., 2.], [1.5, 1.5, 3., 3.]]).astype(DATATYPE),[[2]], place)
rois_lod_data = np.array([0, 2]) rois_lod_data = np.array([0, 2])
x = fluid.data(name='input', shape=[None,1,4,4], dtype=DATATYPE) x = fluid.data(name='input', shape=[None,1,4,4], dtype=DATATYPE)
rois = fluid.data(name='roi', shape=[None,4], dtype=DATATYPE) rois = fluid.data(name='roi', shape=[None,4], dtype=DATATYPE)
rois_lod = fluid.data(name='rois_lod', shape=[None], dtype='int64') rois_lod = fluid.data(name='rois_lod', shape=[None], dtype='int64')
pool_out = fluid.layers.roi_pool( pool_out = fluid.layers.roi_pool(
input=x, input=x,
...@@ -6667,7 +6667,7 @@ def roi_pool(input, ...@@ -6667,7 +6667,7 @@ def roi_pool(input,
pooled_width=1, pooled_width=1,
spatial_scale=1.0, spatial_scale=1.0,
rois_lod=rois_lod) rois_lod=rois_lod)
exe = fluid.Executor(place) exe = fluid.Executor(place)
out, = exe.run(feed={'input':input_data ,'roi':roi_data, 'rois_lod': rois_lod_data}, fetch_list=[pool_out.name]) out, = exe.run(feed={'input':input_data ,'roi':roi_data, 'rois_lod': rois_lod_data}, fetch_list=[pool_out.name])
print(out) #array([[[[11.]]], [[[16.]]]], dtype=float32) print(out) #array([[[[11.]]], [[[16.]]]], dtype=float32)
...@@ -6707,8 +6707,8 @@ def roi_align(input, ...@@ -6707,8 +6707,8 @@ def roi_align(input,
Args: Args:
input (Variable): ${x_comment} input (Variable): ${x_comment}
rois (Variable): ROIs (Regions of Interest) to pool over.It should be rois (Variable): ROIs (Regions of Interest) to pool over.It should be
a 2-D LoDTensor of shape (num_rois, 4), the lod level is 1. The a 2-D LoDTensor of shape (num_rois, 4), the lod level is 1. The
data type is float32 or float64. Given as [[x1, y1, x2, y2], ...], data type is float32 or float64. Given as [[x1, y1, x2, y2], ...],
(x1, y1) is the top left coordinates, and (x2, y2) is the bottom (x1, y1) is the top left coordinates, and (x2, y2) is the bottom
right coordinates. right coordinates.
rois_lod (Variable): The lod info of rois. Default: None rois_lod (Variable): The lod info of rois. Default: None
...@@ -6716,9 +6716,9 @@ def roi_align(input, ...@@ -6716,9 +6716,9 @@ def roi_align(input,
pooled_width (int32, optional): ${pooled_width_comment} Default: 1 pooled_width (int32, optional): ${pooled_width_comment} Default: 1
spatial_scale (float32, optional): ${spatial_scale_comment} Default: 1.0 spatial_scale (float32, optional): ${spatial_scale_comment} Default: 1.0
sampling_ratio(int32, optional): ${sampling_ratio_comment} Default: -1 sampling_ratio(int32, optional): ${sampling_ratio_comment} Default: -1
name(str, optional): For detailed information, please refer name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and to :ref:`api_guide_Name`. Usually name is no need to set and
None by default. None by default.
Returns: Returns:
Variable: Variable:
...@@ -6782,13 +6782,13 @@ def dice_loss(input, label, epsilon=0.00001, name=None): ...@@ -6782,13 +6782,13 @@ def dice_loss(input, label, epsilon=0.00001, name=None):
input (Variable): Tensor, rank>=2, shape is :math:`[N_1, N_2, ..., N_D]`, where :math:`N_1` is input (Variable): Tensor, rank>=2, shape is :math:`[N_1, N_2, ..., N_D]`, where :math:`N_1` is
the batch_size, :math:`N_D` is 1. It is usually the output predictions of sigmoid activation. the batch_size, :math:`N_D` is 1. It is usually the output predictions of sigmoid activation.
The data type can be float32 or float64. The data type can be float32 or float64.
label (Variable): Tensor, the groud truth with the same rank as input, shape is :math:`[N_1, N_2, ..., N_D]`. label (Variable): Tensor, the groud truth with the same rank as input, shape is :math:`[N_1, N_2, ..., N_D]`.
where :math:`N_1` is the batch_size, :math:`N_D` is 1. The data type can be float32 or float64. where :math:`N_1` is the batch_size, :math:`N_D` is 1. The data type can be float32 or float64.
epsilon (float): The epsilon will be added to the numerator and denominator. epsilon (float): The epsilon will be added to the numerator and denominator.
If both input and label are empty, it makes sure dice is 1. If both input and label are empty, it makes sure dice is 1.
Default: 0.00001 Default: 0.00001
name(str, optional): The default value is None. name(str, optional): The default value is None.
Normally there is no need for user to set this property. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` For more information, please refer to :ref:`api_guide_Name`
Returns: Returns:
...@@ -6827,9 +6827,9 @@ def image_resize(input, ...@@ -6827,9 +6827,9 @@ def image_resize(input,
""" """
This op resizes a batch of images. This op resizes a batch of images.
The input must be a 4-D Tensor of the shape (num_batches, channels, in_h, in_w) The input must be a 4-D Tensor of the shape (num_batches, channels, in_h, in_w)
or (num_batches, in_h, in_w, channels), or a 5-D Tensor of the shape or (num_batches, in_h, in_w, channels), or a 5-D Tensor of the shape
(num_batches, channels, in_d, in_h, in_w) or (num_batches, in_d, in_h, in_w, channels), (num_batches, channels, in_d, in_h, in_w) or (num_batches, in_d, in_h, in_w, channels),
and the resizing only applies on the three dimensions(depth, height and width). and the resizing only applies on the three dimensions(depth, height and width).
**Warning:** the parameter :attr:`actual_shape` will be deprecated in the **Warning:** the parameter :attr:`actual_shape` will be deprecated in the
...@@ -6844,21 +6844,21 @@ def image_resize(input, ...@@ -6844,21 +6844,21 @@ def image_resize(input,
'NEAREST' : Nearest neighbor interpolation 'NEAREST' : Nearest neighbor interpolation
Nearest neighbor interpolation is to perform nearest neighbor 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. direction) on input tensor.
Bilinear interpolation is an extension of linear interpolation for Bilinear interpolation is an extension of linear interpolation for
interpolating functions of two variables (e.g. H-direction and interpolating functions of two variables (e.g. H-direction and
W-direction in this op) on a rectilinear 2D grid. The key idea is W-direction in this op) on a rectilinear 2D grid. The key idea is
to perform linear interpolation first in one direction, and then to perform linear interpolation first in one direction, and then
again in the other direction. again in the other direction.
Trilinear interpolation is an extension of linear interpolation for Trilinear interpolation is an extension of linear interpolation for
interpolating functions of three variables (e.g. D-direction, interpolating functions of three variables (e.g. D-direction,
H-direction and W-direction in this op) on a rectilinear 3D grid. H-direction and W-direction in this op) on a rectilinear 3D grid.
The linear interpolation is performed on three directions. The linear interpolation is performed on three directions.
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. of interpolation can be selected by them.
Example: Example:
...@@ -6866,18 +6866,18 @@ def image_resize(input, ...@@ -6866,18 +6866,18 @@ def image_resize(input,
.. code-block:: text .. code-block:: text
For scale: For scale:
if align_corners = True && out_size > 1 : if align_corners = True && out_size > 1 :
scale_factor = (in_size-1.0)/(out_size-1.0) scale_factor = (in_size-1.0)/(out_size-1.0)
else: else:
scale_factor = float(in_size/out_size) scale_factor = float(in_size/out_size)
Nearest neighbor interpolation: Nearest neighbor interpolation:
if: if:
align_corners = False align_corners = False
...@@ -6900,15 +6900,15 @@ def image_resize(input, ...@@ -6900,15 +6900,15 @@ def image_resize(input,
if: if:
align_corners = False , align_mode = 0 align_corners = False , align_mode = 0
input : (N,C,H_in,W_in) input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where: output: (N,C,H_out,W_out) where:
H_out = (H_{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 W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else: else:
input : (N,C,H_in,W_in) input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where: output: (N,C,H_out,W_out) where:
...@@ -6919,31 +6919,31 @@ def image_resize(input, ...@@ -6919,31 +6919,31 @@ def image_resize(input,
if: if:
align_corners = False , align_mode = 0 align_corners = False , align_mode = 0
input : (N,C,D_in,H_in,W_in) input : (N,C,D_in,H_in,W_in)
output: (N,C,D_out,H_out,W_out) where: output: (N,C,D_out,H_out,W_out) where:
D_out = (D_{in}+0.5) * scale_{factor} - 0.5 D_out = (D_{in}+0.5) * scale_{factor} - 0.5
H_out = (H_{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 W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else: else:
input : (N,C,D_in,H_in,W_in) input : (N,C,D_in,H_in,W_in)
output: (N,C,D_out,H_out,W_out) where: output: (N,C,D_out,H_out,W_out) where:
D_out = D_{in} * scale_{factor} D_out = D_{in} * scale_{factor}
H_out = H_{in} * scale_{factor} H_out = H_{in} * scale_{factor}
W_out = W_{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. 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. 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. https://en.wikipedia.org/wiki/Trilinear_interpolation.
...@@ -6953,7 +6953,7 @@ def image_resize(input, ...@@ -6953,7 +6953,7 @@ def image_resize(input,
its data format is specified by :attr:`data_format`. its data format is specified by :attr:`data_format`.
out_shape(list|tuple|Variable|None): Output shape of image resize out_shape(list|tuple|Variable|None): Output shape of image resize
layer, the shape is (out_h, out_w) when input is a 4-D Tensor and is layer, the shape is (out_h, out_w) when input is a 4-D Tensor and is
(out_d, out_h, out_w) when input is a 5-D Tensor. Default: None. If (out_d, out_h, out_w) when input is a 5-D Tensor. Default: None. If
a list, each element can be an integer or a Tensor Variable of shape: [1]. a list, each element can be an integer or a Tensor Variable of shape: [1].
If a Tensor Variable, its dimensions size should be a 1. If a Tensor Variable, its dimensions size should be a 1.
scale(float|Variable|None): The multiplier for the input height or width. At scale(float|Variable|None): The multiplier for the input height or width. At
...@@ -6970,24 +6970,24 @@ def image_resize(input, ...@@ -6970,24 +6970,24 @@ def image_resize(input,
:attr:`out_shape` and :attr:`scale` specifying :attr:`out_shape` and :attr:`scale` specifying
shape. That is to say actual_shape has the shape. That is to say actual_shape has the
highest priority. It is recommended to use highest priority. It is recommended to use
:attr:`out_shape` if you want to specify output :attr:`out_shape` if you want to specify output
shape dynamically, because :attr:`actual_shape` shape dynamically, because :attr:`actual_shape`
will be deprecated. When using actual_shape to will be deprecated. When using actual_shape to
specify output shape, one of :attr:`out_shape` specify output shape, one of :attr:`out_shape`
and :attr:`scale` should also be set, otherwise and :attr:`scale` should also be set, otherwise
errors would be occurred in graph constructing stage. errors would be occurred in graph constructing stage.
Default: None Default: None
align_corners(bool) : An optional bool, If True, the centers of the 4 corner pixels of the align_corners(bool) : An optional bool, If True, the centers of the 4 corner pixels of the
input and output tensors are aligned, preserving the values at the input and output tensors are aligned, preserving the values at the
corner pixels. corner pixels.
Default: True Default: True
align_mode(int) : An optional for bilinear interpolation. can be \'0\' align_mode(int) : An optional for bilinear interpolation. can be \'0\'
for src_idx = scale*(dst_indx+0.5)-0.5 , can be \'1\' for for src_idx = scale*(dst_indx+0.5)-0.5 , can be \'1\' for
src_idx = scale*dst_index. src_idx = scale*dst_index.
data_format (str, optional): Specify the data format of the input, and the data format of the output data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`, `"NCDHW"`, will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`, `"NCDHW"`,
`"NDHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `"NDHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. When it is `"NCHW"`, the data is stored `[batch_size, input_channels, input_height, input_width]`. When it is `"NCHW"`, the data is stored
in the order of: `[batch_size, input_channels, input_depth, input_height, input_width]`. in the order of: `[batch_size, input_channels, input_depth, input_height, input_width]`.
Returns: Returns:
...@@ -7011,7 +7011,7 @@ def image_resize(input, ...@@ -7011,7 +7011,7 @@ def image_resize(input,
Examples: Examples:
.. code-block:: python .. code-block:: python
#declarative mode #declarative mode
import paddle.fluid as fluid import paddle.fluid as fluid
import numpy as np import numpy as np
...@@ -7041,14 +7041,14 @@ def image_resize(input, ...@@ -7041,14 +7041,14 @@ def image_resize(input,
place = fluid.CPUPlace() place = fluid.CPUPlace()
exe = fluid.Executor(place) exe = fluid.Executor(place)
exe.run(fluid.default_startup_program()) exe.run(fluid.default_startup_program())
input_data = np.random.rand(2,3,6,10).astype("float32") input_data = np.random.rand(2,3,6,10).astype("float32")
output_data = exe.run(fluid.default_main_program(), output_data = exe.run(fluid.default_main_program(),
feed={"input":input_data}, feed={"input":input_data},
fetch_list=[output], fetch_list=[output],
return_numpy=True) return_numpy=True)
print(output_data[0].shape) print(output_data[0].shape)
#1 #1
...@@ -7231,7 +7231,7 @@ def resize_bilinear(input, ...@@ -7231,7 +7231,7 @@ def resize_bilinear(input,
output shape which specified by actual_shape, out_shape and scale output shape which specified by actual_shape, out_shape and scale
in priority order. in priority order.
**Warning:** the parameter :attr:`actual_shape` will be deprecated in **Warning:** the parameter :attr:`actual_shape` will be deprecated in
the future and only use :attr:`out_shape` instead. the future and only use :attr:`out_shape` instead.
Bilinear interpolation is an extension of linear interpolation for Bilinear interpolation is an extension of linear interpolation for
...@@ -7243,7 +7243,7 @@ def resize_bilinear(input, ...@@ -7243,7 +7243,7 @@ def resize_bilinear(input,
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 https://en.wikipedia.org/wiki/Bilinear_interpolation
Align_corners and align_mode are optional parameters,the calculation Align_corners and align_mode are optional parameters,the calculation
method of interpolation can be selected by them. method of interpolation can be selected by them.
Example: Example:
...@@ -7251,23 +7251,23 @@ def resize_bilinear(input, ...@@ -7251,23 +7251,23 @@ def resize_bilinear(input,
.. code-block:: text .. code-block:: text
For scale: For scale:
if align_corners = True && out_size > 1 : if align_corners = True && out_size > 1 :
scale_factor = (in_size-1.0)/(out_size-1.0) scale_factor = (in_size-1.0)/(out_size-1.0)
else: else:
scale_factor = float(in_size/out_size) scale_factor = float(in_size/out_size)
Bilinear interpolation: Bilinear interpolation:
if: if:
align_corners = False , align_mode = 0 align_corners = False , align_mode = 0
input : (N,C,H_in,W_in) input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where: output: (N,C,H_out,W_out) where:
H_out = (H_{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 W_out = (W_{in}+0.5) * scale_{factor} - 0.5
...@@ -7282,12 +7282,12 @@ def resize_bilinear(input, ...@@ -7282,12 +7282,12 @@ def resize_bilinear(input,
input(Variable): 4-D Tensor(NCHW), its data type is float32, float64, or uint8, input(Variable): 4-D Tensor(NCHW), its data type is float32, float64, or uint8,
its data format is specified by :attr:`data_format`. its data format is specified by :attr:`data_format`.
out_shape(list|tuple|Variable|None): Output shape of resize bilinear out_shape(list|tuple|Variable|None): Output shape of resize bilinear
layer, the shape is (out_h, out_w).Default: None. If a list, each layer, the shape is (out_h, out_w).Default: None. If a list, each
element can be an integer or a Tensor Variable with shape: [1]. If a element can be an integer or a Tensor Variable with shape: [1]. If a
Tensor Variable, its dimension size should be 1. Tensor Variable, its dimension size should be 1.
scale(float|Variable|None): The multiplier for the input height or width. At scale(float|Variable|None): The multiplier for the input height or width. At
least one of :attr:`out_shape` or :attr:`scale` must be set. least one of :attr:`out_shape` or :attr:`scale` must be set.
And :attr:`out_shape` has a higher priority than :attr:`scale`. And :attr:`out_shape` has a higher priority than :attr:`scale`.
Default: None. Default: None.
actual_shape(Variable): An optional input to specify output shape actual_shape(Variable): An optional input to specify output shape
dynamically. If provided, image resize dynamically. If provided, image resize
...@@ -7295,16 +7295,16 @@ def resize_bilinear(input, ...@@ -7295,16 +7295,16 @@ def resize_bilinear(input,
:attr:`out_shape` and :attr:`scale` specifying :attr:`out_shape` and :attr:`scale` specifying
shape. That is to say actual_shape has the shape. That is to say actual_shape has the
highest priority. It is recommended to use highest priority. It is recommended to use
:attr:`out_shape` if you want to specify output :attr:`out_shape` if you want to specify output
shape dynamically, because :attr:`actual_shape` shape dynamically, because :attr:`actual_shape`
will be deprecated. When using actual_shape to will be deprecated. When using actual_shape to
specify output shape, one of :attr:`out_shape` specify output shape, one of :attr:`out_shape`
and :attr:`scale` should also be set, otherwise and :attr:`scale` should also be set, otherwise
errors would be occurred in graph constructing stage. errors would be occurred in graph constructing stage.
Default: None Default: None
align_corners(bool): ${align_corners_comment} align_corners(bool): ${align_corners_comment}
align_mode(bool): ${align_mode_comment} align_mode(bool): ${align_mode_comment}
data_format (str, optional): Specify the data format of the input, and the data format of the output data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. `[batch_size, input_channels, input_height, input_width]`.
...@@ -7312,10 +7312,10 @@ def resize_bilinear(input, ...@@ -7312,10 +7312,10 @@ def resize_bilinear(input,
Returns: Returns:
Variable: 4-D tensor(NCHW or NHWC). Variable: 4-D tensor(NCHW or NHWC).
Examples: Examples:
.. code-block:: python .. code-block:: python
#declarative mode #declarative mode
import paddle.fluid as fluid import paddle.fluid as fluid
import numpy as np import numpy as np
...@@ -7345,14 +7345,14 @@ def resize_bilinear(input, ...@@ -7345,14 +7345,14 @@ def resize_bilinear(input,
place = fluid.CPUPlace() place = fluid.CPUPlace()
exe = fluid.Executor(place) exe = fluid.Executor(place)
exe.run(fluid.default_startup_program()) exe.run(fluid.default_startup_program())
input_data = np.random.rand(2,3,6,10).astype("float32") input_data = np.random.rand(2,3,6,10).astype("float32")
output_data = exe.run(fluid.default_main_program(), output_data = exe.run(fluid.default_main_program(),
feed={"input":input_data}, feed={"input":input_data},
fetch_list=[output], fetch_list=[output],
return_numpy=True) return_numpy=True)
print(output_data[0].shape) print(output_data[0].shape)
#1 #1
...@@ -7394,18 +7394,18 @@ def resize_trilinear(input, ...@@ -7394,18 +7394,18 @@ def resize_trilinear(input,
output shape which specified by actual_shape, out_shape and scale output shape which specified by actual_shape, out_shape and scale
in priority order. in priority order.
**Warning:** the parameter :attr:`actual_shape` will be deprecated **Warning:** the parameter :attr:`actual_shape` will be deprecated
in the future and only use :attr:`out_shape` instead. in the future and only use :attr:`out_shape` instead.
Trilinear interpolation is an extension of linear interpolation for Trilinear interpolation is an extension of linear interpolation for
interpolating functions of three variables (e.g. D-direction, interpolating functions of three variables (e.g. D-direction,
H-direction and W-direction in this op) on a rectilinear 3D grid. H-direction and W-direction in this op) on a rectilinear 3D grid.
The linear interpolation is performed on three directions. The linear interpolation is performed on three directions.
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 https://en.wikipedia.org/wiki/Trilinear_interpolation
Align_corners and align_mode are optional parameters,the calculation Align_corners and align_mode are optional parameters,the calculation
method of interpolation can be selected by them. method of interpolation can be selected by them.
Example: Example:
...@@ -7413,24 +7413,24 @@ def resize_trilinear(input, ...@@ -7413,24 +7413,24 @@ def resize_trilinear(input,
.. code-block:: text .. code-block:: text
For scale: For scale:
if align_corners = True && out_size > 1 : if align_corners = True && out_size > 1 :
scale_factor = (in_size-1.0)/(out_size-1.0) scale_factor = (in_size-1.0)/(out_size-1.0)
else: else:
scale_factor = float(in_size/out_size) scale_factor = float(in_size/out_size)
Bilinear interpolation: Bilinear interpolation:
if: if:
align_corners = False , align_mode = 0 align_corners = False , align_mode = 0
input : (N,C,D_in,H_in,W_in) input : (N,C,D_in,H_in,W_in)
output: (N,C,D_out,H_out,W_out) where: output: (N,C,D_out,H_out,W_out) where:
D_out = (D_{in}+0.5) * scale_{factor} - 0.5 D_out = (D_{in}+0.5) * scale_{factor} - 0.5
H_out = (H_{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 W_out = (W_{in}+0.5) * scale_{factor} - 0.5
...@@ -7449,8 +7449,8 @@ def resize_trilinear(input, ...@@ -7449,8 +7449,8 @@ def resize_trilinear(input,
its data format is specified by :attr:`data_format`. its data format is specified by :attr:`data_format`.
out_shape(list|tuple|Variable|None): The output shape of resized tensor, the shape is (out_d, out_h, out_w). Default: None. Every element should be an integer or a Tensor Variable with shape: [1] if it is a list. If it is a Tensor Variable, its dimension size should be 1. out_shape(list|tuple|Variable|None): The output shape of resized tensor, the shape is (out_d, out_h, out_w). Default: None. Every element should be an integer or a Tensor Variable with shape: [1] if it is a list. If it is a Tensor Variable, its dimension size should be 1.
scale(float|Variable|None): The multiplier for the input depth, height or width. scale(float|Variable|None): The multiplier for the input depth, height or width.
At least one of :attr:`out_shape` or :attr:`scale` must be set. At least one of :attr:`out_shape` or :attr:`scale` must be set.
And :attr:`out_shape` has a higher priority than :attr:`scale`. And :attr:`out_shape` has a higher priority than :attr:`scale`.
Default: None. Default: None.
name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`
actual_shape(Variable): An optional input to specify output shape actual_shape(Variable): An optional input to specify output shape
...@@ -7459,26 +7459,26 @@ def resize_trilinear(input, ...@@ -7459,26 +7459,26 @@ def resize_trilinear(input,
:attr:`out_shape` and :attr:`scale` specifying :attr:`out_shape` and :attr:`scale` specifying
shape. That is to say actual_shape has the shape. That is to say actual_shape has the
highest priority. It is recommended to use highest priority. It is recommended to use
:attr:`out_shape` if you want to specify output :attr:`out_shape` if you want to specify output
shape dynamically, because :attr:`actual_shape` shape dynamically, because :attr:`actual_shape`
will be deprecated. When using actual_shape to will be deprecated. When using actual_shape to
specify output shape, one of :attr:`out_shape` specify output shape, one of :attr:`out_shape`
and :attr:`scale` should also be set, otherwise and :attr:`scale` should also be set, otherwise
errors would be occurred in graph constructing stage. errors would be occurred in graph constructing stage.
Default: None Default: None
align_corners(bool): ${align_corners_comment} align_corners(bool): ${align_corners_comment}
align_mode(bool): ${align_mode_comment} align_mode(bool): ${align_mode_comment}
data_format (str, optional): Specify the data format of the input, and the data format of the output data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCDHW"`, `"NDHWC"`. will be consistent with that of the input. An optional string from: `"NCDHW"`, `"NDHWC"`.
The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of: The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_depth, input_height, input_width]`. `[batch_size, input_channels, input_depth, input_height, input_width]`.
Returns: Returns:
Variable: A 5-D Tensor(NCDHW or NDHWC) Variable: A 5-D Tensor(NCDHW or NDHWC)
Examples: Examples:
.. code-block:: python .. code-block:: python
#declarative mode #declarative mode
import paddle.fluid as fluid import paddle.fluid as fluid
import numpy as np import numpy as np
...@@ -7508,14 +7508,14 @@ def resize_trilinear(input, ...@@ -7508,14 +7508,14 @@ def resize_trilinear(input,
place = fluid.CPUPlace() place = fluid.CPUPlace()
exe = fluid.Executor(place) exe = fluid.Executor(place)
exe.run(fluid.default_startup_program()) exe.run(fluid.default_startup_program())
input_data = np.random.rand(2,3,6,8,10).astype("float32") input_data = np.random.rand(2,3,6,8,10).astype("float32")
output_data = exe.run(fluid.default_main_program(), output_data = exe.run(fluid.default_main_program(),
feed={"input":input_data}, feed={"input":input_data},
fetch_list=[output], fetch_list=[output],
return_numpy=True) return_numpy=True)
print(output_data[0].shape) print(output_data[0].shape)
#1 #1
...@@ -7555,10 +7555,10 @@ def resize_nearest(input, ...@@ -7555,10 +7555,10 @@ def resize_nearest(input,
data_format='NCHW'): data_format='NCHW'):
""" """
This op resizes the input by performing nearest neighbor interpolation in both the This op resizes the input by performing nearest neighbor interpolation in both the
height direction and the width direction based on given output shape height direction and the width direction based on given output shape
which is specified by actual_shape, out_shape and scale in priority order. which is specified by actual_shape, out_shape and scale in priority order.
**Warning:** the parameter :attr:`actual_shape` will be deprecated in the **Warning:** the parameter :attr:`actual_shape` will be deprecated in the
future and only use :attr:`out_shape` instead. future and only use :attr:`out_shape` instead.
Example: Example:
...@@ -7566,16 +7566,16 @@ def resize_nearest(input, ...@@ -7566,16 +7566,16 @@ def resize_nearest(input,
.. code-block:: text .. code-block:: text
For scale: For scale:
if align_corners = True && out_size > 1 : if align_corners = True && out_size > 1 :
scale_factor = (in_size-1.0)/(out_size-1.0) scale_factor = (in_size-1.0)/(out_size-1.0)
else: else:
scale_factor = float(in_size/out_size) scale_factor = float(in_size/out_size)
Nearest neighbor interpolation: Nearest neighbor interpolation:
if: if:
align_corners = False align_corners = False
...@@ -7603,9 +7603,9 @@ def resize_nearest(input, ...@@ -7603,9 +7603,9 @@ def resize_nearest(input,
its data format is specified by :attr:`data_format`. its data format is specified by :attr:`data_format`.
out_shape(list|tuple|Variable|None): The output shape of resized tensor, the shape is (out_h, out_w). Default: None. Every element should be an integer or a tensor Variable with shape: [1] if it is a list. If it is a tensor Variable, its dimension size should be 1. out_shape(list|tuple|Variable|None): The output shape of resized tensor, the shape is (out_h, out_w). Default: None. Every element should be an integer or a tensor Variable with shape: [1] if it is a list. If it is a tensor Variable, its dimension size should be 1.
scale(float|Variable|None): The multiplier for the input height or width. At scale(float|Variable|None): The multiplier for the input height or width. At
least one of :attr:`out_shape` or :attr:`scale` must be set. least one of :attr:`out_shape` or :attr:`scale` must be set.
And :attr:`out_shape` has a higher priority than :attr:`scale`. And :attr:`out_shape` has a higher priority than :attr:`scale`.
Default: None. Default: None.
name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`
actual_shape(Variable): An optional input to specify output shape actual_shape(Variable): An optional input to specify output shape
dynamically. If provided, image resize dynamically. If provided, image resize
...@@ -7613,15 +7613,15 @@ def resize_nearest(input, ...@@ -7613,15 +7613,15 @@ def resize_nearest(input,
:attr:`out_shape` and :attr:`scale` specifying :attr:`out_shape` and :attr:`scale` specifying
shape. That is to say actual_shape has the shape. That is to say actual_shape has the
highest priority. It is recommended to use highest priority. It is recommended to use
:attr:`out_shape` if you want to specify output :attr:`out_shape` if you want to specify output
shape dynamically, because :attr:`actual_shape` shape dynamically, because :attr:`actual_shape`
will be deprecated. When using actual_shape to will be deprecated. When using actual_shape to
specify output shape, one of :attr:`out_shape` specify output shape, one of :attr:`out_shape`
and :attr:`scale` should also be set, otherwise and :attr:`scale` should also be set, otherwise
errors would be occurred in graph constructing stage. errors would be occurred in graph constructing stage.
Default: None Default: None
align_corners(bool): ${align_corners_comment} align_corners(bool): ${align_corners_comment}
data_format (str, optional): Specify the data format of the input, and the data format of the output data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. `[batch_size, input_channels, input_height, input_width]`.
...@@ -7631,7 +7631,7 @@ def resize_nearest(input, ...@@ -7631,7 +7631,7 @@ def resize_nearest(input,
Examples: Examples:
.. code-block:: python .. code-block:: python
#declarative mode #declarative mode
import paddle.fluid as fluid import paddle.fluid as fluid
import numpy as np import numpy as np
...@@ -7661,14 +7661,14 @@ def resize_nearest(input, ...@@ -7661,14 +7661,14 @@ def resize_nearest(input,
place = fluid.CPUPlace() place = fluid.CPUPlace()
exe = fluid.Executor(place) exe = fluid.Executor(place)
exe.run(fluid.default_startup_program()) exe.run(fluid.default_startup_program())
input_data = np.random.rand(2,3,6,10).astype("float32") input_data = np.random.rand(2,3,6,10).astype("float32")
output_data = exe.run(fluid.default_main_program(), output_data = exe.run(fluid.default_main_program(),
feed={"input":input_data}, feed={"input":input_data},
fetch_list=[output], fetch_list=[output],
return_numpy=True) return_numpy=True)
print(output_data[0].shape) print(output_data[0].shape)
#1 #1
...@@ -7772,15 +7772,15 @@ def gather(input, index, overwrite=True): ...@@ -7772,15 +7772,15 @@ def gather(input, index, overwrite=True):
[5, 6]] [5, 6]]
Args: Args:
input (Variable): The source input tensor with rank>=1. Supported data type is input (Variable): The source input tensor with rank>=1. Supported data type is
int32, int64, float32, float64 and uint8 (only for CPU), int32, int64, float32, float64 and uint8 (only for CPU),
float16 (only for GPU). float16 (only for GPU).
index (Variable): The index input tensor with rank=1. Data type is int32 or int64. index (Variable): The index input tensor with rank=1. Data type is int32 or int64.
overwrite (bool, optional): The mode that updating the grad when has same index. overwrite (bool, optional): The mode that updating the grad when has same index.
If True, use the overwrite mode to update the grad of the same index, If True, use the overwrite mode to update the grad of the same index,
if False, use the accumulate mode to update the grad of the same index. if False, use the accumulate mode to update the grad of the same index.
Default value is True. Default value is True.
Returns: Returns:
...@@ -7811,10 +7811,10 @@ def gather_nd(input, index, name=None): ...@@ -7811,10 +7811,10 @@ def gather_nd(input, index, name=None):
""" """
**Gather Nd Layer** **Gather Nd Layer**
This function is actually a high-dimensional extension of :code:`gather` This function is actually a high-dimensional extension of :code:`gather`
and supports for simultaneous indexing by multiple axes. :attr:`index` is a and supports for simultaneous indexing by multiple axes. :attr:`index` is a
K-dimensional integer tensor, which is regarded as a (K-1)-dimensional K-dimensional integer tensor, which is regarded as a (K-1)-dimensional
tensor of :attr:`index` into :attr:`input`, where each element defines tensor of :attr:`index` into :attr:`input`, where each element defines
a slice of params: a slice of params:
.. math:: .. math::
...@@ -7837,9 +7837,9 @@ def gather_nd(input, index, name=None): ...@@ -7837,9 +7837,9 @@ def gather_nd(input, index, name=None):
* Case 1: * Case 1:
index = [[1]] index = [[1]]
gather_nd(input, index) gather_nd(input, index)
= [input[1, :, :]] = [input[1, :, :]]
= [[12, 13, 14, 15], = [[12, 13, 14, 15],
[16, 17, 18, 19], [16, 17, 18, 19],
[20, 21, 22, 23]] [20, 21, 22, 23]]
...@@ -7897,7 +7897,7 @@ def scatter(input, index, updates, name=None, overwrite=True): ...@@ -7897,7 +7897,7 @@ def scatter(input, index, updates, name=None, overwrite=True):
.. code-block:: python .. code-block:: python
import numpy as np import numpy as np
#input: #input:
input = np.array([[1, 1], [2, 2], [3, 3]]) input = np.array([[1, 1], [2, 2], [3, 3]])
index = np.array([2, 1, 0, 1]) index = np.array([2, 1, 0, 1])
...@@ -7927,7 +7927,7 @@ def scatter(input, index, updates, name=None, overwrite=True): ...@@ -7927,7 +7927,7 @@ def scatter(input, index, updates, name=None, overwrite=True):
name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` .
overwrite (bool): The mode that updating the output when there are same indices. overwrite (bool): The mode that updating the output when there are same indices.
If True, use the overwrite mode to update the output of the same index, If True, use the overwrite mode to update the output of the same index,
if False, use the accumulate mode to update the output of the same index. if False, use the accumulate mode to update the output of the same index.
Default value is True. Default value is True.
Returns: Returns:
...@@ -7977,11 +7977,11 @@ def scatter_nd_add(ref, index, updates, name=None): ...@@ -7977,11 +7977,11 @@ def scatter_nd_add(ref, index, updates, name=None):
**Scatter_nd_add Layer** **Scatter_nd_add Layer**
Output is obtained by applying sparse addition to a single value Output is obtained by applying sparse addition to a single value
or slice in a Variable. or slice in a Variable.
:attr:`ref` is a Tensor with rank :math:`R` :attr:`ref` is a Tensor with rank :math:`R`
and :attr:`index` is a Tensor with rank :math:`K` . Thus, :attr:`index` and :attr:`index` is a Tensor with rank :math:`K` . Thus, :attr:`index`
has shape :math:`[i_0, i_1, ..., i_{K-2}, Q]` where :math:`Q \leq R` . :attr:`updates` has shape :math:`[i_0, i_1, ..., i_{K-2}, Q]` where :math:`Q \leq R` . :attr:`updates`
is a Tensor with rank :math:`K - 1 + R - Q` and its is a Tensor with rank :math:`K - 1 + R - Q` and its
shape is :math:`index.shape[:-1] + ref.shape[index.shape[-1]:]` . shape is :math:`index.shape[:-1] + ref.shape[index.shape[-1]:]` .
...@@ -7990,7 +7990,7 @@ def scatter_nd_add(ref, index, updates, name=None): ...@@ -7990,7 +7990,7 @@ def scatter_nd_add(ref, index, updates, name=None):
which is obtained by the last one dimension of :attr:`index` . which is obtained by the last one dimension of :attr:`index` .
.. code-block:: text .. code-block:: text
Given: Given:
* Case 1: * Case 1:
...@@ -7999,7 +7999,7 @@ def scatter_nd_add(ref, index, updates, name=None): ...@@ -7999,7 +7999,7 @@ def scatter_nd_add(ref, index, updates, name=None):
updates = [9, 10, 11, 12] updates = [9, 10, 11, 12]
we get: we get:
output = [0, 22, 12, 14, 4, 5] output = [0, 22, 12, 14, 4, 5]
* Case 2: * Case 2:
...@@ -8012,7 +8012,7 @@ def scatter_nd_add(ref, index, updates, name=None): ...@@ -8012,7 +8012,7 @@ def scatter_nd_add(ref, index, updates, name=None):
updates.shape = (2, 2, 2) updates.shape = (2, 2, 2)
we get: we get:
output = [[67, 19], [-16, -27]] output = [[67, 19], [-16, -27]]
Args: Args:
...@@ -8057,13 +8057,13 @@ def scatter_nd(index, updates, shape, name=None): ...@@ -8057,13 +8057,13 @@ def scatter_nd(index, updates, shape, name=None):
""" """
**Scatter_nd Layer** **Scatter_nd Layer**
Output is obtained by scattering the :attr:`updates` in a new tensor according Output is obtained by scattering the :attr:`updates` in a new tensor according
to :attr:`index` . This op is similar to :code:`scatter_nd_add`, except the to :attr:`index` . This op is similar to :code:`scatter_nd_add`, except the
tensor of :attr:`shape` is zero-initialized. Correspondingly, :code:`scatter_nd(index, updates, shape)` tensor of :attr:`shape` is zero-initialized. Correspondingly, :code:`scatter_nd(index, updates, shape)`
is equal to :code:`scatter_nd_add(fluid.layers.zeros(shape, updates.dtype), index, updates)` . is equal to :code:`scatter_nd_add(fluid.layers.zeros(shape, updates.dtype), index, updates)` .
If :attr:`index` has repeated elements, then the corresponding updates are accumulated. If :attr:`index` has repeated elements, then the corresponding updates are accumulated.
Because of the numerical approximation issues, the different order of repeated elements Because of the numerical approximation issues, the different order of repeated elements
in :attr:`index` may cause different results. The specific calculation method can be in :attr:`index` may cause different results. The specific calculation method can be
seen :code:`scatter_nd_add` . This op is the inverse of the :code:`gather_nd` op. seen :code:`scatter_nd_add` . This op is the inverse of the :code:`gather_nd` op.
Args: Args:
...@@ -8156,7 +8156,7 @@ def log(x, name=None): ...@@ -8156,7 +8156,7 @@ def log(x, name=None):
Args: Args:
x (Variable): Input LoDTensor or Tensor. Must be one of the following types: float32, float64. x (Variable): Input LoDTensor or Tensor. Must be one of the following types: float32, float64.
name (str|None): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` name (str|None): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns: Returns:
Variable: The natural log of the input LoDTensor or Tensor computed element-wise. Variable: The natural log of the input LoDTensor or Tensor computed element-wise.
...@@ -8239,14 +8239,14 @@ def selu(x, scale=None, alpha=None, name=None): ...@@ -8239,14 +8239,14 @@ def selu(x, scale=None, alpha=None, name=None):
Selu Operator. Selu Operator.
The equation is: The equation is:
.. math:: .. math::
selu= \\lambda* selu= \\lambda*
\\begin{cases} \\begin{cases}
x &\\quad \\text{ if } x>0 \n x &\\quad \\text{ if } x>0 \n
\\alpha * e^x - \\alpha &\\quad \\text{ if } x<=0 \\alpha * e^x - \\alpha &\\quad \\text{ if } x<=0
\\end{cases} \\end{cases}
The input `X` can carry the LoD (Level of Details) information, The input `X` can carry the LoD (Level of Details) information,
or not. And the output shares the LoD information with input `X`. or not. And the output shares the LoD information with input `X`.
...@@ -8270,7 +8270,7 @@ def selu(x, scale=None, alpha=None, name=None): ...@@ -8270,7 +8270,7 @@ def selu(x, scale=None, alpha=None, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
import numpy as np import numpy as np
...@@ -8322,7 +8322,7 @@ def mean_iou(input, label, num_classes): ...@@ -8322,7 +8322,7 @@ def mean_iou(input, label, num_classes):
Its shape should be the same as input. Its shape should be the same as input.
num_classes (int32): The possible number of labels. num_classes (int32): The possible number of labels.
Returns: Returns:
Three Variables. Three Variables.
- mean_iou(Variable) : A 1-D Tensor representing the mean intersection-over-union with shape [1]. \ - mean_iou(Variable) : A 1-D Tensor representing the mean intersection-over-union with shape [1]. \
...@@ -8330,8 +8330,8 @@ def mean_iou(input, label, num_classes): ...@@ -8330,8 +8330,8 @@ def mean_iou(input, label, num_classes):
- out_wrong(Variable) : A 1-D Tensor with shape [num_classes]. Data type is int32. \ - out_wrong(Variable) : A 1-D Tensor with shape [num_classes]. Data type is int32. \
The wrong numbers of each class. The wrong numbers of each class.
- out_correct(Variable): A 1-D Tensor with shape [num_classes]. Data type is int32. The correct numbers of each class. - out_correct(Variable): A 1-D Tensor with shape [num_classes]. Data type is int32. The correct numbers of each class.
Examples: Examples:
.. code-block:: python .. code-block:: python
...@@ -8401,7 +8401,7 @@ def crop(x, shape=None, offsets=None, name=None): ...@@ -8401,7 +8401,7 @@ def crop(x, shape=None, offsets=None, name=None):
x (Variable): Tensor, data type can be float32 or float64. x (Variable): Tensor, data type can be float32 or float64.
shape (Variable|list/tuple of integers): The output shape is specified shape (Variable|list/tuple of integers): The output shape is specified
by `shape`, which can be a Tensor or a list/tuple of integers. by `shape`, which can be a Tensor or a list/tuple of integers.
If it is a Tensor, it's rank must be the same as `x` , only If it is a Tensor, it's rank must be the same as `x` , only
it's shape will be used, and the value of it will be ignored. This way it's shape will be used, and the value of it will be ignored. This way
is suitable for the case that the output shape may be changed each is suitable for the case that the output shape may be changed each
iteration. If it is a list/tuple of integers, it's length must be the same iteration. If it is a list/tuple of integers, it's length must be the same
...@@ -8412,9 +8412,9 @@ def crop(x, shape=None, offsets=None, name=None): ...@@ -8412,9 +8412,9 @@ def crop(x, shape=None, offsets=None, name=None):
This way is suitable for the case that the offsets may be changed This way is suitable for the case that the offsets may be changed
each iteration. If it is a list/tuple of integers, it's length must be the each iteration. If it is a list/tuple of integers, it's length must be the
same as the rank of `x`. If None, the offsets are 0 at each dimension. same as the rank of `x`. If None, the offsets are 0 at each dimension.
name(str, optional): For detailed information, please refer name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name` . Usually name is no need to set and to :ref:`api_guide_Name` . Usually name is no need to set and
None by default. None by default.
Returns: Returns:
The cropped Tensor, which has the same rank and data type with `x` The cropped Tensor, which has the same rank and data type with `x`
...@@ -8680,7 +8680,7 @@ def affine_grid(theta, out_shape, name=None): ...@@ -8680,7 +8680,7 @@ def affine_grid(theta, out_shape, name=None):
name(str|None): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. name(str|None): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
Variable: A Tensor with shape [batch_size, H, W, 2] while 'H' and 'W' are the height and width of feature map in affine transformation. The data type is the same as `theta`. Variable: A Tensor with shape [batch_size, H, W, 2] while 'H' and 'W' are the height and width of feature map in affine transformation. The data type is the same as `theta`.
Raises: Raises:
ValueError: If the type of arguments is not supported. ValueError: If the type of arguments is not supported.
...@@ -8835,7 +8835,7 @@ def elu(x, alpha=1.0, name=None): ...@@ -8835,7 +8835,7 @@ def elu(x, alpha=1.0, name=None):
Args: Args:
x(${x_type}): ${x_comment} x(${x_type}): ${x_comment}
alpha(${alpha_type}|1.0): ${alpha_comment} alpha(${alpha_type}|1.0): ${alpha_comment}
name(str|None): The default value is None. Normally there is no need for user to set this property. name(str|None): The default value is None. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`. For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
${out_type}: ${out_comment} ${out_type}: ${out_comment}
...@@ -8846,7 +8846,7 @@ def elu(x, alpha=1.0, name=None): ...@@ -8846,7 +8846,7 @@ def elu(x, alpha=1.0, name=None):
import paddle.fluid as fluid import paddle.fluid as fluid
import numpy as np import numpy as np
input_elu = np.array([[-1,6],[1,15.6]]) input_elu = np.array([[-1,6],[1,15.6]])
with fluid.dygraph.guard(): with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(input_elu) x = fluid.dygraph.to_variable(input_elu)
...@@ -8970,7 +8970,7 @@ def stanh(x, scale_a=0.67, scale_b=1.7159, name=None): ...@@ -8970,7 +8970,7 @@ def stanh(x, scale_a=0.67, scale_b=1.7159, name=None):
will be named automatically. will be named automatically.
Returns: Returns:
output(${out_type}): ${out_comment}. output(${out_type}): ${out_comment}.
Examples: Examples:
...@@ -9047,17 +9047,17 @@ def hard_sigmoid(x, slope=0.2, offset=0.5, name=None): ...@@ -9047,17 +9047,17 @@ def hard_sigmoid(x, slope=0.2, offset=0.5, name=None):
def swish(x, beta=1.0, name=None): def swish(x, beta=1.0, name=None):
""" """
Elementwise swish activation function. See `Searching for Activation Functions <https://arxiv.org/abs/1710.05941>`_ for more details. Elementwise swish activation function. See `Searching for Activation Functions <https://arxiv.org/abs/1710.05941>`_ for more details.
Equation: Equation:
.. math:: .. math::
out = \\frac{x}{1 + e^{- beta * x}} out = \\frac{x}{1 + e^{- beta * x}}
Args: Args:
x(Variable): Tensor or LoDTensor, dtype: float32 or float64, the input of swish activation. x(Variable): Tensor or LoDTensor, dtype: float32 or float64, the input of swish activation.
beta(float): Constant beta of swish operator, default 1.0. beta(float): Constant beta of swish operator, default 1.0.
name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
...@@ -9067,23 +9067,23 @@ def swish(x, beta=1.0, name=None): ...@@ -9067,23 +9067,23 @@ def swish(x, beta=1.0, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
# declarative mode # declarative mode
import numpy as np import numpy as np
from paddle import fluid from paddle import fluid
x = fluid.data(name="x", shape=(-1, 3), dtype="float32") x = fluid.data(name="x", shape=(-1, 3), dtype="float32")
y = fluid.layers.swish(x, beta=2.0) y = fluid.layers.swish(x, beta=2.0)
place = fluid.CPUPlace() place = fluid.CPUPlace()
exe = fluid.Executor(place) exe = fluid.Executor(place)
start = fluid.default_startup_program() start = fluid.default_startup_program()
main = fluid.default_main_program() main = fluid.default_main_program()
data = np.random.randn(2, 3).astype("float32") data = np.random.randn(2, 3).astype("float32")
exe.run(start) exe.run(start)
y_np, = exe.run(main, feed={"x": data}, fetch_list=[y]) y_np, = exe.run(main, feed={"x": data}, fetch_list=[y])
data data
# array([[-1.1239197 , 1.3391294 , 0.03921051], # array([[-1.1239197 , 1.3391294 , 0.03921051],
# [ 1.1970421 , 0.02440812, 1.2055548 ]], dtype=float32) # [ 1.1970421 , 0.02440812, 1.2055548 ]], dtype=float32)
...@@ -9098,7 +9098,7 @@ def swish(x, beta=1.0, name=None): ...@@ -9098,7 +9098,7 @@ def swish(x, beta=1.0, name=None):
import numpy as np import numpy as np
from paddle import fluid from paddle import fluid
import paddle.fluid.dygraph as dg import paddle.fluid.dygraph as dg
data = np.random.randn(2, 3).astype("float32") data = np.random.randn(2, 3).astype("float32")
place = fluid.CPUPlace() place = fluid.CPUPlace()
with dg.guard(place) as g: with dg.guard(place) as g:
...@@ -9141,13 +9141,13 @@ def prelu(x, mode, param_attr=None, name=None): ...@@ -9141,13 +9141,13 @@ def prelu(x, mode, param_attr=None, name=None):
Args: Args:
x (Variable): The input Tensor or LoDTensor with data type float32. x (Variable): The input Tensor or LoDTensor with data type float32.
mode (str): The mode for weight sharing. mode (str): The mode for weight sharing.
param_attr(ParamAttr|None): The parameter attribute for the learnable param_attr(ParamAttr|None): The parameter attribute for the learnable
weight (alpha), it can be create by ParamAttr. None by default. weight (alpha), it can be create by ParamAttr. None by default.
For detailed information, please refer to :ref:`api_fluid_ParamAttr`. For detailed information, please refer to :ref:`api_fluid_ParamAttr`.
name(str|None): For detailed information, please refer name(str|None): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and to :ref:`api_guide_Name`. Usually name is no need to set and
None by default. None by default.
Returns: Returns:
Variable: Variable:
...@@ -9202,7 +9202,7 @@ def brelu(x, t_min=0.0, t_max=24.0, name=None): ...@@ -9202,7 +9202,7 @@ def brelu(x, t_min=0.0, t_max=24.0, name=None):
x(${x_type}): ${x_comment} x(${x_type}): ${x_comment}
t_min(${t_min_type}|0.0): ${t_min_comment} t_min(${t_min_type}|0.0): ${t_min_comment}
t_max(${t_max_type}|24.0): ${t_max_comment} t_max(${t_max_type}|24.0): ${t_max_comment}
name(str|None): The default value is None. Normally there is no need for user to set this property. name(str|None): The default value is None. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`. For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
${out_type}: ${out_comment} ${out_type}: ${out_comment}
...@@ -9213,14 +9213,14 @@ def brelu(x, t_min=0.0, t_max=24.0, name=None): ...@@ -9213,14 +9213,14 @@ def brelu(x, t_min=0.0, t_max=24.0, name=None):
import paddle.fluid as fluid import paddle.fluid as fluid
import numpy as np import numpy as np
input_brelu = np.array([[-1,6],[1,15.6]]) input_brelu = np.array([[-1,6],[1,15.6]])
with fluid.dygraph.guard(): with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(input_brelu) x = fluid.dygraph.to_variable(input_brelu)
y = fluid.layers.brelu(x, t_min=1.0, t_max=10.0) y = fluid.layers.brelu(x, t_min=1.0, t_max=10.0)
print(y.numpy()) print(y.numpy())
#[[ 1. 6.] #[[ 1. 6.]
#[ 1. 10.]] #[ 1. 10.]]
""" """
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'brelu') check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'brelu')
...@@ -9297,8 +9297,8 @@ def soft_relu(x, threshold=40.0, name=None): ...@@ -9297,8 +9297,8 @@ def soft_relu(x, threshold=40.0, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
import numpy as np import numpy as np
...@@ -9520,14 +9520,14 @@ def stack(x, axis=0): ...@@ -9520,14 +9520,14 @@ def stack(x, axis=0):
def filter_by_instag(ins, ins_tag, filter_tag, is_lod, out_val_if_empty=0): def filter_by_instag(ins, ins_tag, filter_tag, is_lod, out_val_if_empty=0):
""" """
**Filter By Instag Layer** **Filter By Instag Layer**
This function filter a batch of ins by instag, This function filter a batch of ins by instag,
There are multiple ins, and every ins belongs to some tags. There are multiple ins, and every ins belongs to some tags.
We can specify some tags we want. So the ins which belongs to that tags We can specify some tags we want. So the ins which belongs to that tags
remains in the output, and others removed. remains in the output, and others removed.
For example, one batch has 4 ins. Every ins has its tag list. For example, one batch has 4 ins. Every ins has its tag list.
| Ins | Ins_Tag | | Ins | Ins_Tag |
|:-----:|:------:| |:-----:|:------:|
| 0 | 0, 1 | | 0 | 0, 1 |
...@@ -9543,7 +9543,7 @@ def filter_by_instag(ins, ins_tag, filter_tag, is_lod, out_val_if_empty=0): ...@@ -9543,7 +9543,7 @@ def filter_by_instag(ins, ins_tag, filter_tag, is_lod, out_val_if_empty=0):
So Ins 0 and Ins 1 can pass and be seen in the output, So Ins 0 and Ins 1 can pass and be seen in the output,
Ins 2 and 3 cannot pass because they do not has tag 1. Ins 2 and 3 cannot pass because they do not has tag 1.
Actually, if is_lod is false, it is normal tensor that equals to Actually, if is_lod is false, it is normal tensor that equals to
lod_tensor with all 1, similar to the example above. lod_tensor with all 1, similar to the example above.
Args: Args:
...@@ -9551,7 +9551,7 @@ def filter_by_instag(ins, ins_tag, filter_tag, is_lod, out_val_if_empty=0): ...@@ -9551,7 +9551,7 @@ def filter_by_instag(ins, ins_tag, filter_tag, is_lod, out_val_if_empty=0):
And first dimension can have lod info or not. And first dimension can have lod info or not.
ins_tag (Variable): Input Variable (LoDTensor), usually it is 1D list ins_tag (Variable): Input Variable (LoDTensor), usually it is 1D list
And split them by lod info And split them by lod info
filter_tag (Variable): Input Variable (1D Tensor/List), usually it is filter_tag (Variable): Input Variable (1D Tensor/List), usually it is
list that holds the tags. list that holds the tags.
is_lod (Bool): Boolean value to indicate ins is lod tensor or not. is_lod (Bool): Boolean value to indicate ins is lod tensor or not.
out_val_if_empty(Int64): If the output after filter is empty, this value out_val_if_empty(Int64): If the output after filter is empty, this value
...@@ -9568,7 +9568,7 @@ def filter_by_instag(ins, ins_tag, filter_tag, is_lod, out_val_if_empty=0): ...@@ -9568,7 +9568,7 @@ def filter_by_instag(ins, ins_tag, filter_tag, is_lod, out_val_if_empty=0):
ins_tag = layers.data(name='Ins_tag', shape=[-1,16], lod_level=0, dtype='int64') ins_tag = layers.data(name='Ins_tag', shape=[-1,16], lod_level=0, dtype='int64')
filter_tag = layers.data(name='Filter_tag', shape=[-1,16], dtype='int64') filter_tag = layers.data(name='Filter_tag', shape=[-1,16], dtype='int64')
out, loss_weight = layers.filter_by_instag(ins, ins_tag, filter_tag, True) out, loss_weight = layers.filter_by_instag(ins, ins_tag, filter_tag, True)
""" """
helper = LayerHelper('filter_by_instag', **locals()) helper = LayerHelper('filter_by_instag', **locals())
...@@ -9776,7 +9776,7 @@ def expand_as(x, target_tensor, name=None): ...@@ -9776,7 +9776,7 @@ def expand_as(x, target_tensor, name=None):
[[4], [5], [6]] [[4], [5], [6]]
] ]
target_tensor's shape: [2, 6, 2] target_tensor's shape: [2, 6, 2]
Output(Out) is a 3-D tensor with shape [2, 6, 2]: Output(Out) is a 3-D tensor with shape [2, 6, 2]:
...@@ -9784,7 +9784,7 @@ def expand_as(x, target_tensor, name=None): ...@@ -9784,7 +9784,7 @@ def expand_as(x, target_tensor, name=None):
[[1, 1], [2, 2], [3, 3], [1, 1], [2, 2], [3, 3]], [[1, 1], [2, 2], [3, 3], [1, 1], [2, 2], [3, 3]],
[[4, 4], [5, 5], [6, 6], [4, 4], [5, 5], [6, 6]] [[4, 4], [5, 5], [6, 6], [4, 4], [5, 5], [6, 6]]
] ]
Args: Args:
x (Variable): A Tensor with dtype float64, float32, int32. x (Variable): A Tensor with dtype float64, float32, int32.
...@@ -9793,22 +9793,22 @@ def expand_as(x, target_tensor, name=None): ...@@ -9793,22 +9793,22 @@ def expand_as(x, target_tensor, name=None):
target_tensor for expanding to Input(X). Only use target_tensor'shape. target_tensor for expanding to Input(X). Only use target_tensor'shape.
Returns: Returns:
Variable: A Tensor with dtype float64, float32, int32. Variable: A Tensor with dtype float64, float32, int32.
After expanding, size of each dimension of Output(Out) is equal to the size After expanding, size of each dimension of Output(Out) is equal to the size
of the corresponding dimension of target_tensor multiplying the corresponding of the corresponding dimension of target_tensor multiplying the corresponding
value given by target_tensor. value given by target_tensor.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
import numpy as np import numpy as np
data = fluid.layers.data(name="data", shape=[-1,10], dtype='float64') data = fluid.layers.data(name="data", shape=[-1,10], dtype='float64')
target_tensor = fluid.layers.data( target_tensor = fluid.layers.data(
name="target_tensor", shape=[-1,20], dtype='float64') name="target_tensor", shape=[-1,20], dtype='float64')
result = fluid.layers.expand_as(x=data, target_tensor=target_tensor) result = fluid.layers.expand_as(x=data, target_tensor=target_tensor)
use_cuda = False use_cuda = False
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place) exe = fluid.Executor(place)
...@@ -9854,31 +9854,31 @@ def uniform_random_batch_size_like(input, ...@@ -9854,31 +9854,31 @@ def uniform_random_batch_size_like(input,
shape=[2,4] shape=[2,4]
result.shape[output_dim_idx] = input.shape[input_dim_idx], result.shape[output_dim_idx] = input.shape[input_dim_idx],
output_dim_idx = 0, output_dim_idx = 0,
input_dim_idx = 0, input_dim_idx = 0,
result.shape[0] = input.shape[0], result.shape[0] = input.shape[0],
then: then:
result=[[ 0.3443427 , -0.23056602, 0.3477049 , 0.06139076]] # result.shape=[1,4] result=[[ 0.3443427 , -0.23056602, 0.3477049 , 0.06139076]] # result.shape=[1,4]
*Case 2: *Case 2:
Given: Given:
input =[[0.946741 , 0.1357001 , 0.38086128]] # input.shape=[1,3] input =[[0.946741 , 0.1357001 , 0.38086128]] # input.shape=[1,3]
shape=[2,4] shape=[2,4]
input_dim_idx=1 input_dim_idx=1
output_dim_idx=1 output_dim_idx=1
result.shape[output_dim_idx] = input.shape[input_dim_idx], result.shape[output_dim_idx] = input.shape[input_dim_idx],
output_dim_idx = 1, output_dim_idx = 1,
input_dim_idx = 1, input_dim_idx = 1,
result.shape[1] = input.shape[1], result.shape[1] = input.shape[1],
then: then:
result=[[-0.23133647, -0.84195036, 0.21441269], result=[[-0.23133647, -0.84195036, 0.21441269],
[-0.08774924, 0.25605237, -0.09403259]] # result.shape=[2,3] [-0.08774924, 0.25605237, -0.09403259]] # result.shape=[2,3]
Args: Args:
input (Variable): A Tensor. Supported data types: float32, float64. input (Variable): A Tensor. Supported data types: float32, float64.
shape (tuple|list): A python list or python tuple. The shape of the output Tensor, the data type is int. shape (tuple|list): A python list or python tuple. The shape of the output Tensor, the data type is int.
input_dim_idx (int, optional): An index used to get the input dimension value which will be used to resize the output dimension. Default 0. input_dim_idx (int, optional): An index used to get the input dimension value which will be used to resize the output dimension. Default 0.
output_dim_idx (int, optional): An index used to indicate the specific dimension that will be replaced by corresponding input dimension value. Default 0. output_dim_idx (int, optional): An index used to indicate the specific dimension that will be replaced by corresponding input dimension value. Default 0.
min (float, optional): The lower bound on the range of random values to generate, the min is included in the range. Default -1.0. min (float, optional): The lower bound on the range of random values to generate, the min is included in the range. Default -1.0.
max (float, optional): The upper bound on the range of random values to generate, the max is excluded in the range. Default 1.0. max (float, optional): The upper bound on the range of random values to generate, the max is excluded in the range. Default 1.0.
...@@ -9891,15 +9891,15 @@ def uniform_random_batch_size_like(input, ...@@ -9891,15 +9891,15 @@ def uniform_random_batch_size_like(input,
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
# example 1: # example 1:
input = fluid.data(name="input", shape=[1, 3], dtype='float32') input = fluid.data(name="input", shape=[1, 3], dtype='float32')
out_1 = fluid.layers.uniform_random_batch_size_like(input, [2, 4]) # out_1.shape=[1, 4] out_1 = fluid.layers.uniform_random_batch_size_like(input, [2, 4]) # out_1.shape=[1, 4]
# example 2: # example 2:
out_2 = fluid.layers.uniform_random_batch_size_like(input, [2, 4], input_dim_idx=1, output_dim_idx=1) # out_2.shape=[2, 3] out_2 = fluid.layers.uniform_random_batch_size_like(input, [2, 4], input_dim_idx=1, output_dim_idx=1) # out_2.shape=[2, 3]
""" """
helper = LayerHelper('uniform_random_batch_size_like', **locals()) helper = LayerHelper('uniform_random_batch_size_like', **locals())
...@@ -9929,13 +9929,13 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'): ...@@ -9929,13 +9929,13 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
Args: Args:
shape (Tuple[int] | List[int]): Shape of the generated random tensor. shape (Tuple[int] | List[int]): Shape of the generated random tensor.
mean (float): Mean of the random tensor, defaults to 0.0. mean (float): Mean of the random tensor, defaults to 0.0.
std (float): Standard deviation of the random tensor, defaults to 1.0. std (float): Standard deviation of the random tensor, defaults to 1.0.
seed (int): ${seed_comment} seed (int): ${seed_comment}
dtype(np.dtype | core.VarDesc.VarType | str): Output data type, float32 or float64. dtype(np.dtype | core.VarDesc.VarType | str): Output data type, float32 or float64.
Returns: Returns:
...@@ -9943,18 +9943,18 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'): ...@@ -9943,18 +9943,18 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
Examples: Examples:
.. code-block:: python .. code-block:: python
# declarative mode # declarative mode
import numpy as np import numpy as np
from paddle import fluid from paddle import fluid
x = fluid.layers.gaussian_random((2, 3), std=2., seed=10) x = fluid.layers.gaussian_random((2, 3), std=2., seed=10)
place = fluid.CPUPlace() place = fluid.CPUPlace()
exe = fluid.Executor(place) exe = fluid.Executor(place)
start = fluid.default_startup_program() start = fluid.default_startup_program()
main = fluid.default_main_program() main = fluid.default_main_program()
exe.run(start) exe.run(start)
x_np, = exe.run(main, feed={}, fetch_list=[x]) x_np, = exe.run(main, feed={}, fetch_list=[x])
...@@ -9968,11 +9968,11 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'): ...@@ -9968,11 +9968,11 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
import numpy as np import numpy as np
from paddle import fluid from paddle import fluid
import paddle.fluid.dygraph as dg import paddle.fluid.dygraph as dg
place = fluid.CPUPlace() place = fluid.CPUPlace()
with dg.guard(place) as g: with dg.guard(place) as g:
x = fluid.layers.gaussian_random((2, 4), mean=2., dtype="float32", seed=10) x = fluid.layers.gaussian_random((2, 4), mean=2., dtype="float32", seed=10)
x_np = x.numpy() x_np = x.numpy()
x_np x_np
# array([[2.3060477 , 2.676496 , 3.9911983 , 0.9990833 ], # array([[2.3060477 , 2.676496 , 3.9911983 , 0.9990833 ],
# [2.8675377 , 2.2279181 , 0.79029655, 2.8447366 ]], dtype=float32) # [2.8675377 , 2.2279181 , 0.79029655, 2.8447366 ]], dtype=float32)
...@@ -10005,7 +10005,7 @@ def sampling_id(x, min=0.0, max=1.0, seed=0, dtype='float32'): ...@@ -10005,7 +10005,7 @@ def sampling_id(x, min=0.0, max=1.0, seed=0, dtype='float32'):
x (Variable): 2-D tensor, [batch_size, input_feature_dimensions] x (Variable): 2-D tensor, [batch_size, input_feature_dimensions]
min (Float): minimum , default 0.0. min (Float): minimum , default 0.0.
max (Float): maximum, default 1.0. max (Float): maximum, default 1.0.
seed (Float): Random seed, default 0. if seed is not 0, will generate same number every time. seed (Float): Random seed, default 0. if seed is not 0, will generate same number every time.
dtype(np.dtype|core.VarDesc.VarType|str): The type of output data : float32, float_16, int etc dtype(np.dtype|core.VarDesc.VarType|str): The type of output data : float32, float_16, int etc
Returns: Returns:
...@@ -10095,7 +10095,7 @@ def gaussian_random_batch_size_like(input, ...@@ -10095,7 +10095,7 @@ def gaussian_random_batch_size_like(input,
def sum(x): def sum(x):
""" """
${comment} ${comment}
Case 1: Case 1:
:: ::
Input: Input:
...@@ -10155,8 +10155,8 @@ def sum(x): ...@@ -10155,8 +10155,8 @@ def sum(x):
# the sum of input0 and input1 is 2-D Tensor with shape [2,3]. # the sum of input0 and input1 is 2-D Tensor with shape [2,3].
# dtype is the corresponding C++ data type, which may vary in different environments. # dtype is the corresponding C++ data type, which may vary in different environments.
# Eg: if the data type of tensor is int64, then the corresponding C++ data type is int64_t, # Eg: if the data type of tensor is int64, then the corresponding C++ data type is int64_t,
# so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux, # so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux,
# and '__int64' on Windows. They both represent 64-bit integer variables. # and '__int64' on Windows. They both represent 64-bit integer variables.
""" """
...@@ -10363,7 +10363,7 @@ def strided_slice(input, axes, starts, ends, strides): ...@@ -10363,7 +10363,7 @@ def strided_slice(input, axes, starts, ends, strides):
strides = [1, 1] strides = [1, 1]
Then: Then:
result = [ [5, 6, 7], ] result = [ [5, 6, 7], ]
Case2: Case2:
Given: Given:
data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
...@@ -10373,7 +10373,7 @@ def strided_slice(input, axes, starts, ends, strides): ...@@ -10373,7 +10373,7 @@ def strided_slice(input, axes, starts, ends, strides):
strides = [1, -1] strides = [1, -1]
Then: Then:
result = [ [8, 7, 6], ] result = [ [8, 7, 6], ]
Case3: Case3:
Given: Given:
data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
...@@ -10669,7 +10669,7 @@ def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None): ...@@ -10669,7 +10669,7 @@ def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None):
bias(float): The bias to be put on the input. bias(float): The bias to be put on the input.
bias_after_scale(bool): Apply bias addition after or before scaling. It is useful for numeric stability in some circumstances. bias_after_scale(bool): Apply bias addition after or before scaling. It is useful for numeric stability in some circumstances.
act(str, optional): Activation applied to the output such as tanh, softmax, sigmoid, relu. act(str, optional): Activation applied to the output such as tanh, softmax, sigmoid, relu.
name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns: Returns:
Variable(Tensor|LoDTensor): Output tensor of scale operator, with shape and data type same as input. Variable(Tensor|LoDTensor): Output tensor of scale operator, with shape and data type same as input.
...@@ -10804,7 +10804,7 @@ Examples: ...@@ -10804,7 +10804,7 @@ Examples:
"x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'), "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'),
"y": np.random.randint(1, 5, size=[5]).astype('float32') "y": np.random.randint(1, 5, size=[5]).astype('float32')
} }
x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32')
y = fluid.data(name="y", shape=[5], dtype='float32') y = fluid.data(name="y", shape=[5], dtype='float32')
z = fluid.layers.elementwise_add(x, y, axis=3) z = fluid.layers.elementwise_add(x, y, axis=3)
...@@ -10888,7 +10888,7 @@ Examples: ...@@ -10888,7 +10888,7 @@ Examples:
"x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'), "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'),
"y": np.random.randint(1, 5, size=[5]).astype('float32') "y": np.random.randint(1, 5, size=[5]).astype('float32')
} }
x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32')
y = fluid.data(name="y", shape=[5], dtype='float32') y = fluid.data(name="y", shape=[5], dtype='float32')
z = fluid.layers.elementwise_div(x, y, axis=3) z = fluid.layers.elementwise_div(x, y, axis=3)
...@@ -10896,7 +10896,7 @@ Examples: ...@@ -10896,7 +10896,7 @@ Examples:
place = fluid.CPUPlace() place = fluid.CPUPlace()
exe = fluid.Executor(place) exe = fluid.Executor(place)
z_value = exe.run(feed=gen_data(), z_value = exe.run(feed=gen_data(),
fetch_list=[z.name]) fetch_list=[z.name])
print(z_value) # z.shape=[2,3,4,5] print(z_value) # z.shape=[2,3,4,5]
...@@ -10972,7 +10972,7 @@ Examples: ...@@ -10972,7 +10972,7 @@ Examples:
"x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'), "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'),
"y": np.random.randint(1, 5, size=[5]).astype('float32') "y": np.random.randint(1, 5, size=[5]).astype('float32')
} }
x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32')
y = fluid.data(name="y", shape=[5], dtype='float32') y = fluid.data(name="y", shape=[5], dtype='float32')
z = fluid.layers.elementwise_sub(x, y, axis=3) z = fluid.layers.elementwise_sub(x, y, axis=3)
...@@ -10980,7 +10980,7 @@ Examples: ...@@ -10980,7 +10980,7 @@ Examples:
place = fluid.CPUPlace() place = fluid.CPUPlace()
exe = fluid.Executor(place) exe = fluid.Executor(place)
z_value = exe.run(feed=gen_data(), z_value = exe.run(feed=gen_data(),
fetch_list=[z.name]) fetch_list=[z.name])
print(z_value) # z.shape=[2,3,4,5] print(z_value) # z.shape=[2,3,4,5]
...@@ -11056,7 +11056,7 @@ Examples: ...@@ -11056,7 +11056,7 @@ Examples:
"x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'), "x": np.random.randint(1, 5, size=[2, 3, 4, 5]).astype('float32'),
"y": np.random.randint(1, 5, size=[5]).astype('float32') "y": np.random.randint(1, 5, size=[5]).astype('float32')
} }
x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32') x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32')
y = fluid.data(name="y", shape=[5], dtype='float32') y = fluid.data(name="y", shape=[5], dtype='float32')
z = fluid.layers.elementwise_mul(x, y, axis=3) z = fluid.layers.elementwise_mul(x, y, axis=3)
...@@ -11064,11 +11064,11 @@ Examples: ...@@ -11064,11 +11064,11 @@ Examples:
place = fluid.CPUPlace() place = fluid.CPUPlace()
exe = fluid.Executor(place) exe = fluid.Executor(place)
z_value = exe.run(feed=gen_data(), z_value = exe.run(feed=gen_data(),
fetch_list=[z.name]) fetch_list=[z.name])
print(z_value) # z.shape=[2,3,4,5] print(z_value) # z.shape=[2,3,4,5]
""" """
if in_dygraph_mode(): if in_dygraph_mode():
return _elementwise_op_in_dygraph( return _elementwise_op_in_dygraph(
...@@ -11328,7 +11328,7 @@ for func in []: ...@@ -11328,7 +11328,7 @@ for func in []:
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
# example 1: shape(x) = (2, 3, 4, 5), shape(y) = (2, 3, 4, 5) # example 1: shape(x) = (2, 3, 4, 5), shape(y) = (2, 3, 4, 5)
x0 = fluid.layers.data(name="x0", shape=[2, 3, 4, 5], dtype='float32') x0 = fluid.layers.data(name="x0", shape=[2, 3, 4, 5], dtype='float32')
...@@ -11389,7 +11389,7 @@ def logical_and(x, y, out=None, name=None): ...@@ -11389,7 +11389,7 @@ def logical_and(x, y, out=None, name=None):
It operates element-wise on X and Y, and returns the Out. X, Y and Out are N-dim boolean LoDTensor or Tensor. It operates element-wise on X and Y, and returns the Out. X, Y and Out are N-dim boolean LoDTensor or Tensor.
Each element of Out is calculated by Each element of Out is calculated by
.. math:: .. math::
Out = X \land Y Out = X \land Y
...@@ -11438,7 +11438,7 @@ def logical_or(x, y, out=None, name=None): ...@@ -11438,7 +11438,7 @@ def logical_or(x, y, out=None, name=None):
It operates element-wise on X and Y, and returns the Out. X, Y and Out are N-dim boolean LoDTensor or Tensor. It operates element-wise on X and Y, and returns the Out. X, Y and Out are N-dim boolean LoDTensor or Tensor.
Each element of Out is calculated by Each element of Out is calculated by
.. math:: .. math::
Out = X \lor Y Out = X \lor Y
...@@ -11487,7 +11487,7 @@ def logical_xor(x, y, out=None, name=None): ...@@ -11487,7 +11487,7 @@ def logical_xor(x, y, out=None, name=None):
It operates element-wise on X and Y, and returns the Out. X, Y and Out are N-dim boolean LoDTensor or Tensor. It operates element-wise on X and Y, and returns the Out. X, Y and Out are N-dim boolean LoDTensor or Tensor.
Each element of Out is calculated by Each element of Out is calculated by
.. math:: .. math::
Out = (X \lor Y) \land \lnot (X \land Y) Out = (X \lor Y) \land \lnot (X \land Y)
...@@ -11536,7 +11536,7 @@ def logical_not(x, out=None, name=None): ...@@ -11536,7 +11536,7 @@ def logical_not(x, out=None, name=None):
It operates element-wise on X, and returns the Out. X and Out are N-dim boolean LoDTensor or Tensor. It operates element-wise on X, and returns the Out. X and Out are N-dim boolean LoDTensor or Tensor.
Each element of Out is calculated by Each element of Out is calculated by
.. math:: .. math::
Out = \lnot X Out = \lnot X
...@@ -11584,8 +11584,8 @@ def clip(x, min, max, name=None): ...@@ -11584,8 +11584,8 @@ def clip(x, min, max, name=None):
x(${x_type}): ${x_comment} x(${x_type}): ${x_comment}
min(float): ${min_comment} min(float): ${min_comment}
max(float): ${max_comment} max(float): ${max_comment}
name(str, optional): The default value is None. name(str, optional): The default value is None.
Normally there is no need for user to set this property. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` For more information, please refer to :ref:`api_guide_Name`
Returns: Returns:
...@@ -11630,9 +11630,9 @@ def clip_by_norm(x, max_norm, name=None): ...@@ -11630,9 +11630,9 @@ def clip_by_norm(x, max_norm, name=None):
Args: Args:
x(${x_type}): ${x_comment} x(${x_type}): ${x_comment}
max_norm(${max_norm_type}): ${max_norm_comment} max_norm(${max_norm_type}): ${max_norm_comment}
name(str, optional): For detailed information, please refer name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and to :ref:`api_guide_Name`. Usually name is no need to set and
None by default. None by default.
Returns: Returns:
Variable: Variable:
...@@ -11650,6 +11650,8 @@ def clip_by_norm(x, max_norm, name=None): ...@@ -11650,6 +11650,8 @@ def clip_by_norm(x, max_norm, name=None):
""" """
helper = LayerHelper("clip_by_norm", **locals()) helper = LayerHelper("clip_by_norm", **locals())
check_variable_and_dtype(x, 'X', ['float32'], 'clip_by_norm')
check_type(max_norm, 'max_norm', (float), 'clip_by_norm')
if name is None: if name is None:
name = unique_name.generate_with_ignorable_key(".".join( name = unique_name.generate_with_ignorable_key(".".join(
...@@ -11747,23 +11749,23 @@ def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None): ...@@ -11747,23 +11749,23 @@ def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None):
Args: Args:
x (Variable): The first input Tensor/LoDTensor of mul_op. x (Variable): The first input Tensor/LoDTensor of mul_op.
y (Variable): The second input Tensor/LoDTensor of mul_op. y (Variable): The second input Tensor/LoDTensor of mul_op.
x_num_col_dims (int, optional): The mul_op can take tensors with more than two dimensions as its inputs. If the input $x$ is a tensor with more than two dimensions, $x$ will be flattened into a two-dimensional matrix first. The flattening rule is: the first `num_col_dims` will be flattened to form the first dimension of the final matrix (the height of the matrix), and the rest `rank(x) - num_col_dims` dimensions are flattened to form the second dimension of the final matrix (the width of the matrix). As a result, height of the flattened matrix is equal to the product of $x$'s first `x_num_col_dims` dimensions' sizes, and width of the flattened matrix is equal to the product of $x$'s last `rank(x) - num_col_dims` dimensions' size. For example, suppose $x$ is a 6-dimensional tensor with the shape [2, 3, 4, 5, 6], and `x_num_col_dims` = 3. Thus, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30]. Default is 1. x_num_col_dims (int, optional): The mul_op can take tensors with more than two dimensions as its inputs. If the input $x$ is a tensor with more than two dimensions, $x$ will be flattened into a two-dimensional matrix first. The flattening rule is: the first `num_col_dims` will be flattened to form the first dimension of the final matrix (the height of the matrix), and the rest `rank(x) - num_col_dims` dimensions are flattened to form the second dimension of the final matrix (the width of the matrix). As a result, height of the flattened matrix is equal to the product of $x$'s first `x_num_col_dims` dimensions' sizes, and width of the flattened matrix is equal to the product of $x$'s last `rank(x) - num_col_dims` dimensions' size. For example, suppose $x$ is a 6-dimensional tensor with the shape [2, 3, 4, 5, 6], and `x_num_col_dims` = 3. Thus, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30]. Default is 1.
y_num_col_dims (int, optional): The mul_op can take tensors with more than two dimensions as its inputs. If the input $y$ is a tensor with more than two dimensions, $y$ will be flattened into a two-dimensional matrix first. The attribute `y_num_col_dims` determines how $y$ is flattened. See comments of `x_num_col_dims` for more details. Default is 1. y_num_col_dims (int, optional): The mul_op can take tensors with more than two dimensions as its inputs. If the input $y$ is a tensor with more than two dimensions, $y$ will be flattened into a two-dimensional matrix first. The attribute `y_num_col_dims` determines how $y$ is flattened. See comments of `x_num_col_dims` for more details. Default is 1.
name (str, optional): Name of the output. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default is None. name (str, optional): Name of the output. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default is None.
Returns: Returns:
Variable(Tensor/LoDTensor): The output Tensor/LoDTensor of mul op. Variable(Tensor/LoDTensor): The output Tensor/LoDTensor of mul op.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
dataX = fluid.layers.data(name="dataX", append_batch_size = False, shape=[2, 5], dtype="float32") dataX = fluid.layers.data(name="dataX", append_batch_size = False, shape=[2, 5], dtype="float32")
dataY = fluid.layers.data(name="dataY", append_batch_size = False, shape=[5, 3], dtype="float32") dataY = fluid.layers.data(name="dataY", append_batch_size = False, shape=[5, 3], dtype="float32")
output = fluid.layers.mul(dataX, dataY, output = fluid.layers.mul(dataX, dataY,
x_num_col_dims = 1, x_num_col_dims = 1,
y_num_col_dims = 1) y_num_col_dims = 1)
""" """
if in_dygraph_mode(): if in_dygraph_mode():
...@@ -11792,8 +11794,8 @@ def maxout(x, groups, name=None, axis=1): ...@@ -11792,8 +11794,8 @@ def maxout(x, groups, name=None, axis=1):
x(${x_type}): ${x_comment} x(${x_type}): ${x_comment}
groups(int): ${groups_comment} groups(int): ${groups_comment}
axis(int, optional): ${axis_comment} axis(int, optional): ${axis_comment}
name(str, optional): For detailed information, please refer name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and to :ref:`api_guide_Name`. Usually name is no need to set and
None by default. None by default.
Returns: Returns:
...@@ -11808,8 +11810,8 @@ def maxout(x, groups, name=None, axis=1): ...@@ -11808,8 +11810,8 @@ def maxout(x, groups, name=None, axis=1):
import paddle.fluid as fluid import paddle.fluid as fluid
input = fluid.data( input = fluid.data(
name='data', name='data',
shape=[None, 256, 32, 32], shape=[None, 256, 32, 32],
dtype='float32') dtype='float32')
out = fluid.layers.maxout(input, groups=2) out = fluid.layers.maxout(input, groups=2)
""" """
...@@ -11888,7 +11890,7 @@ def space_to_depth(x, blocksize, name=None): ...@@ -11888,7 +11890,7 @@ def space_to_depth(x, blocksize, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
import numpy as np import numpy as np
...@@ -11955,10 +11957,10 @@ def affine_channel(x, ...@@ -11955,10 +11957,10 @@ def affine_channel(x,
bias (Variable): 1D input of shape (C), the c-th element is the bias bias (Variable): 1D input of shape (C), the c-th element is the bias
of the affine transformation for the c-th channel of the input. of the affine transformation for the c-th channel of the input.
The data type is float32 or float64. The data type is float32 or float64.
data_layout (str, optional): Specify the data format of the input, and the data format of the output data_layout (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`. will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. If input is 2D Tensor, you can ignore `[batch_size, input_channels, input_height, input_width]`. If input is 2D Tensor, you can ignore
data_layout. data_layout.
name (str, default None): The name of this layer. For more information, name (str, default None): The name of this layer. For more information,
please refer to :ref:`api_guide_Name` . please refer to :ref:`api_guide_Name` .
...@@ -12081,7 +12083,7 @@ def similarity_focus(input, axis, indexes, name=None): ...@@ -12081,7 +12083,7 @@ def similarity_focus(input, axis, indexes, name=None):
Args: Args:
input(Variable): The input tensor variable(default float). It should input(Variable): The input tensor variable(default float). It should
be a 4-D tensor with shape [BatchSize, A, B, C]. Data type is be a 4-D tensor with shape [BatchSize, A, B, C]. Data type is
float32 or float64. float32 or float64.
axis(int): Indicating the dimension to be selected. It can only be axis(int): Indicating the dimension to be selected. It can only be
1, 2 or 3. 1, 2 or 3.
...@@ -12187,7 +12189,7 @@ def grid_sampler(x, grid, name=None): ...@@ -12187,7 +12189,7 @@ def grid_sampler(x, grid, name=None):
with shape [N, H, W] each, where x is indexing the 4th dimension with shape [N, H, W] each, where x is indexing the 4th dimension
(in width dimension) of input data x and y is indexing the 3rd (in width dimension) of input data x and y is indexing the 3rd
dimension (in height dimension), finally results is the bilinear dimension (in height dimension), finally results is the bilinear
interpolation value of 4 nearest corner points. The output tensor interpolation value of 4 nearest corner points. The output tensor
shape will be [N, C, H, W]. shape will be [N, C, H, W].
.. code-block:: text .. code-block:: text
...@@ -12296,10 +12298,10 @@ def log_loss(input, label, epsilon=1e-4, name=None): ...@@ -12296,10 +12298,10 @@ def log_loss(input, label, epsilon=1e-4, name=None):
batch size. This input is a probability computed batch size. This input is a probability computed
by the previous operator. Data type float32. by the previous operator. Data type float32.
label (Variable|list): The ground truth which is a 2-D tensor with label (Variable|list): The ground truth which is a 2-D tensor with
shape [N x 1], where N is the batch size. shape [N x 1], where N is the batch size.
Data type float32. Data type float32.
epsilon (float, optional): A small number for numerical stability. Default 1e-4. epsilon (float, optional): A small number for numerical stability. Default 1e-4.
name(str|None): For detailed information, please refer to name(str|None): For detailed information, please refer to
:ref:`api_guide_Name` . Usually name is no need to set and None by default. :ref:`api_guide_Name` . Usually name is no need to set and None by default.
Returns: Returns:
...@@ -12331,7 +12333,7 @@ def add_position_encoding(input, alpha, beta, name=None): ...@@ -12331,7 +12333,7 @@ def add_position_encoding(input, alpha, beta, name=None):
This operator performs weighted sum of input feature at each position This operator performs weighted sum of input feature at each position
(position in the sequence) and the corresponding position encoding. (position in the sequence) and the corresponding position encoding.
For more details of position encoding, please refer to `Attention Is All You For more details of position encoding, please refer to `Attention Is All You
Need <http://arxiv.org/pdf/1706.03762.pdf>`_ . Need <http://arxiv.org/pdf/1706.03762.pdf>`_ .
The formula is as follows: The formula is as follows:
...@@ -12356,8 +12358,8 @@ def add_position_encoding(input, alpha, beta, name=None): ...@@ -12356,8 +12358,8 @@ def add_position_encoding(input, alpha, beta, name=None):
weighted sum. weighted sum.
beta(float): Indicate the weight coefficient for position encoding when beta(float): Indicate the weight coefficient for position encoding when
performing weighted sum. performing weighted sum.
name(str, optional): For detailed information, please refer name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and to :ref:`api_guide_Name`. Usually name is no need to set and
None by default. None by default.
Returns: Returns:
...@@ -12414,19 +12416,19 @@ def bilinear_tensor_product(x, ...@@ -12414,19 +12416,19 @@ def bilinear_tensor_product(x,
- :math:`y^\mathrm{T}`: the transpose of :math:`y_{2}`. - :math:`y^\mathrm{T}`: the transpose of :math:`y_{2}`.
Args: Args:
x (Variable): 2-D input tensor with shape [batch_size, M]. Data type x (Variable): 2-D input tensor with shape [batch_size, M]. Data type
is float32 or float64. is float32 or float64.
y (Variable): 2-D input tensor with shape [batch_size, N]. Data type y (Variable): 2-D input tensor with shape [batch_size, N]. Data type
should be same as **x**. should be same as **x**.
size (int): The dimension of this layer. size (int): The dimension of this layer.
act (str|None): Activation to be applied to the output of this layer. Default None. act (str|None): Activation to be applied to the output of this layer. Default None.
name(str|None): For detailed information, please refer to name(str|None): For detailed information, please refer to
:ref:`api_guide_Name` . Usually name is no need to set and None by default. :ref:`api_guide_Name` . Usually name is no need to set and None by default.
param_attr (ParamAttr|None): To specify the weight parameter attribute. param_attr (ParamAttr|None): To specify the weight parameter attribute.
Default: None, which means the default weight parameter property is Default: None, which means the default weight parameter property is
used. See usage for details in :ref:`api_fluid_ParamAttr` . used. See usage for details in :ref:`api_fluid_ParamAttr` .
bias_attr (ParamAttr|None): To specify the bias parameter attribute. bias_attr (ParamAttr|None): To specify the bias parameter attribute.
Default: None, which means the default bias parameter property is Default: None, which means the default bias parameter property is
used. See usage for details in :ref:`api_fluid_ParamAttr` . used. See usage for details in :ref:`api_fluid_ParamAttr` .
Returns: Returns:
Variable: A 2-D Tensor of shape [batch_size, size]. Data type is the same as input **x**. Variable: A 2-D Tensor of shape [batch_size, size]. Data type is the same as input **x**.
...@@ -12491,7 +12493,7 @@ def get_tensor_from_selected_rows(x, name=None): ...@@ -12491,7 +12493,7 @@ def get_tensor_from_selected_rows(x, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
b = fluid.default_main_program().global_block() b = fluid.default_main_program().global_block()
input = b.create_var(name="X", dtype="float32", persistable=True, type=fluid.core.VarDesc.VarType.SELECTED_ROWS) input = b.create_var(name="X", dtype="float32", persistable=True, type=fluid.core.VarDesc.VarType.SELECTED_ROWS)
...@@ -12516,7 +12518,7 @@ def shuffle_channel(x, group, name=None): ...@@ -12516,7 +12518,7 @@ def shuffle_channel(x, group, name=None):
Please refer to the paper Please refer to the paper
https://arxiv.org/pdf/1707.01083.pdf https://arxiv.org/pdf/1707.01083.pdf
.. code-block:: text .. code-block:: text
Given a 4-D tensor input with the shape (N, C, H, W): Given a 4-D tensor input with the shape (N, C, H, W):
...@@ -12537,22 +12539,22 @@ def shuffle_channel(x, group, name=None): ...@@ -12537,22 +12539,22 @@ def shuffle_channel(x, group, name=None):
out.shape = (1, 4, 2, 2) out.shape = (1, 4, 2, 2)
out.data = [[[[0.1, 0.2], out.data = [[[[0.1, 0.2],
[0.2, 0.3]], [0.2, 0.3]],
[[0.5, 0.6], [[0.5, 0.6],
[0.6, 0.7]], [0.6, 0.7]],
[[0.3, 0.4], [[0.3, 0.4],
[0.4, 0.5]], [0.4, 0.5]],
[[0.7, 0.8], [[0.7, 0.8],
[0.8, 0.9]]]] [0.8, 0.9]]]]
Args: Args:
x(Variable): The input tensor variable. It should be a 4-D tensor with shape [N, C, H, W] x(Variable): The input tensor variable. It should be a 4-D tensor with shape [N, C, H, W]
group(int): Indicating the counts of subgroups, It should divide the number of channels. group(int): Indicating the counts of subgroups, It should divide the number of channels.
Returns: Returns:
out(Variable): the channels shuffling result is a tensor variable with the out(Variable): the channels shuffling result is a tensor variable with the
same shape and same type as the input. same shape and same type as the input.
Raises: Raises:
...@@ -12584,10 +12586,10 @@ def shuffle_channel(x, group, name=None): ...@@ -12584,10 +12586,10 @@ def shuffle_channel(x, group, name=None):
def temporal_shift(x, seg_num, shift_ratio=0.25, name=None): def temporal_shift(x, seg_num, shift_ratio=0.25, name=None):
""" """
**Temporal Shift Operator** **Temporal Shift Operator**
${comment} ${comment}
Args: Args:
x(Variable): ${x_comment} x(Variable): ${x_comment}
seg_num(int): ${seg_num_comment} seg_num(int): ${seg_num_comment}
shift_ratio(float): ${shift_ratio_comment} shift_ratio(float): ${shift_ratio_comment}
...@@ -12596,7 +12598,7 @@ def temporal_shift(x, seg_num, shift_ratio=0.25, name=None): ...@@ -12596,7 +12598,7 @@ def temporal_shift(x, seg_num, shift_ratio=0.25, name=None):
None by default. None by default.
Returns: Returns:
out(Variable): The temporal shifting result is a tensor variable with the out(Variable): The temporal shifting result is a tensor variable with the
same shape and same data type as the input. same shape and same data type as the input.
Raises: Raises:
...@@ -12705,23 +12707,23 @@ class PyFuncRegistry(object): ...@@ -12705,23 +12707,23 @@ class PyFuncRegistry(object):
@templatedoc() @templatedoc()
def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None): def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
""" """
This OP is used to register customized Python OP to Paddle Fluid. The design This OP is used to register customized Python OP to Paddle Fluid. The design
principe of py_func is that LodTensor and numpy array can be converted to each principe of py_func is that LodTensor and numpy array can be converted to each
other easily. So you can use Python and numpy API to register a python OP. other easily. So you can use Python and numpy API to register a python OP.
The forward function of the registered OP is ``func`` and the backward function The forward function of the registered OP is ``func`` and the backward function
of that is ``backward_func``. Paddle will call ``func`` at forward runtime and of that is ``backward_func``. Paddle will call ``func`` at forward runtime and
call ``backward_func`` at backward runtime(if ``backward_func`` is not None). call ``backward_func`` at backward runtime(if ``backward_func`` is not None).
``x`` is the input of ``func``, whose type must be LoDTensor; ``out`` is ``x`` is the input of ``func``, whose type must be LoDTensor; ``out`` is
the output of ``func``, whose type can be either LoDTensor or numpy array. the output of ``func``, whose type can be either LoDTensor or numpy array.
The input of the backward function ``backward_func`` is ``x``, ``out`` and The input of the backward function ``backward_func`` is ``x``, ``out`` and
the gradient of ``out``. If some variables of ``out`` have no gradient, the the gradient of ``out``. If some variables of ``out`` have no gradient, the
relevant input variable of ``backward_func`` is None. If some variables of relevant input variable of ``backward_func`` is None. If some variables of
``x`` do not have a gradient, the user should return None in ``backward_func``. ``x`` do not have a gradient, the user should return None in ``backward_func``.
The data type and shape of ``out`` should also be set correctly before this The data type and shape of ``out`` should also be set correctly before this
API is called, and the data type and shape of the gradient of ``out`` and API is called, and the data type and shape of the gradient of ``out`` and
``x`` will be inferred automatically. ``x`` will be inferred automatically.
This API can also be used to debug the neural network by setting the ``func`` This API can also be used to debug the neural network by setting the ``func``
...@@ -12729,35 +12731,35 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None): ...@@ -12729,35 +12731,35 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
Args: Args:
func (callable): The forward function of the registered OP. When the network func (callable): The forward function of the registered OP. When the network
is running, the forward output ``out`` will be calculated according to this is running, the forward output ``out`` will be calculated according to this
function and the forward input ``x``. In ``func`` , it's suggested that we function and the forward input ``x``. In ``func`` , it's suggested that we
actively convert LoDTensor into a numpy array, so that we can use Python and actively convert LoDTensor into a numpy array, so that we can use Python and
numpy API arbitrarily. If not, some operations of numpy may not be compatible. numpy API arbitrarily. If not, some operations of numpy may not be compatible.
x (Variable|tuple(Variale)|list[Variale]): The input of the forward function ``func``. x (Variable|tuple(Variale)|list[Variale]): The input of the forward function ``func``.
It can be Variable|tuple(Variale)|list[Variale], where Variable is LoDTensor or It can be Variable|tuple(Variale)|list[Variale], where Variable is LoDTensor or
Tenosor. In addition, Multiple Variable should be passed in the form of tuple(Variale) Tenosor. In addition, Multiple Variable should be passed in the form of tuple(Variale)
or list[Variale]. or list[Variale].
out (Variable|tuple(Variale)|list[Variale]): The output of the forward function ``func``, out (Variable|tuple(Variale)|list[Variale]): The output of the forward function ``func``,
it can be Variable|tuple(Variale)|list[Variale], where Variable can be either LoDTensor it can be Variable|tuple(Variale)|list[Variale], where Variable can be either LoDTensor
or numpy array. Since Paddle cannot automatically infer the shape and type of ``out``, or numpy array. Since Paddle cannot automatically infer the shape and type of ``out``,
you must create ``out`` in advance. you must create ``out`` in advance.
backward_func (callable, optional): The backward function of the registered OP. backward_func (callable, optional): The backward function of the registered OP.
Its default value is None, which means there is no reverse calculation. If Its default value is None, which means there is no reverse calculation. If
it is not None, ``backward_func`` is called to calculate the gradient of it is not None, ``backward_func`` is called to calculate the gradient of
``x`` when the network is at backward runtime. ``x`` when the network is at backward runtime.
skip_vars_in_backward_input (Variable, optional): It's used to limit the input skip_vars_in_backward_input (Variable, optional): It's used to limit the input
variable list of ``backward_func``, and it can be Variable|tuple(Variale)|list[Variale]. variable list of ``backward_func``, and it can be Variable|tuple(Variale)|list[Variale].
It must belong to either ``x`` or ``out``. The default value is None, which means It must belong to either ``x`` or ``out``. The default value is None, which means
that no variables need to be removed from ``x`` and ``out``. If it is not None, that no variables need to be removed from ``x`` and ``out``. If it is not None,
these variables will not be the input of ``backward_func``. This parameter is only these variables will not be the input of ``backward_func``. This parameter is only
useful when ``backward_func`` is not None. useful when ``backward_func`` is not None.
Returns: Returns:
Variable|tuple(Variale)|list[Variale]: The output ``out`` of the forward function ``func``. Variable|tuple(Variale)|list[Variale]: The output ``out`` of the forward function ``func``.
Examples: Examples:
.. code-block:: python .. code-block:: python
# example 1: # example 1:
import paddle.fluid as fluid import paddle.fluid as fluid
import six import six
...@@ -12768,15 +12770,15 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None): ...@@ -12768,15 +12770,15 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
return np.tanh(x) return np.tanh(x)
# Skip x in backward function and return the gradient of x # Skip x in backward function and return the gradient of x
# LodTensor must be actively converted to numpy array, otherwise, # LodTensor must be actively converted to numpy array, otherwise,
# operations such as +/- can't be used. # operations such as +/- can't be used.
def tanh_grad(y, dy): def tanh_grad(y, dy):
return np.array(dy) * (1 - np.square(np.array(y))) return np.array(dy) * (1 - np.square(np.array(y)))
# Creates a forward function for debugging running networks(print value) # Creates a forward function for debugging running networks(print value)
def debug_func(x): def debug_func(x):
print(x) print(x)
def create_tmp_var(name, dtype, shape): def create_tmp_var(name, dtype, shape):
return fluid.default_main_program().current_block().create_var( return fluid.default_main_program().current_block().create_var(
name=name, dtype=dtype, shape=shape) name=name, dtype=dtype, shape=shape)
...@@ -12788,7 +12790,7 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None): ...@@ -12788,7 +12790,7 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
new_hidden = create_tmp_var(name='hidden_{}'.format(idx), new_hidden = create_tmp_var(name='hidden_{}'.format(idx),
dtype=hidden.dtype, shape=hidden.shape) dtype=hidden.dtype, shape=hidden.shape)
# User-defined forward and backward # User-defined forward and backward
hidden = fluid.layers.py_func(func=tanh, x=hidden, hidden = fluid.layers.py_func(func=tanh, x=hidden,
out=new_hidden, backward_func=tanh_grad, out=new_hidden, backward_func=tanh_grad,
skip_vars_in_backward_input=hidden) skip_vars_in_backward_input=hidden)
...@@ -12800,16 +12802,16 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None): ...@@ -12800,16 +12802,16 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
loss = fluid.layers.cross_entropy(input=prediction, label=label) loss = fluid.layers.cross_entropy(input=prediction, label=label)
return fluid.layers.mean(loss) return fluid.layers.mean(loss)
# example 2: # example 2:
# This example shows how to turn LoDTensor into numpy array and # This example shows how to turn LoDTensor into numpy array and
# use numpy API to register an Python OP # use numpy API to register an Python OP
import paddle.fluid as fluid import paddle.fluid as fluid
import numpy as np import numpy as np
def element_wise_add(x, y): def element_wise_add(x, y):
# LodTensor must be actively converted to numpy array, otherwise, # LodTensor must be actively converted to numpy array, otherwise,
# numpy.shape can't be used. # numpy.shape can't be used.
x = np.array(x) x = np.array(x)
y = np.array(y) y = np.array(y)
if x.shape != y.shape: if x.shape != y.shape:
...@@ -12833,7 +12835,7 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None): ...@@ -12833,7 +12835,7 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
# Input of the forward function # Input of the forward function
x = fluid.data(name='x', shape=[2,3], dtype='int32') x = fluid.data(name='x', shape=[2,3], dtype='int32')
y = fluid.data(name='y', shape=[2,3], dtype='int32') y = fluid.data(name='y', shape=[2,3], dtype='int32')
# Output of the forward function, name/dtype/shape must be specified # Output of the forward function, name/dtype/shape must be specified
output = create_tmp_var('output','int32', [3,1]) output = create_tmp_var('output','int32', [3,1])
...@@ -12846,7 +12848,7 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None): ...@@ -12846,7 +12848,7 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
# Feed numpy array to main_program # Feed numpy array to main_program
input1 = np.random.randint(1, 10, size=[2,3], dtype='int32') input1 = np.random.randint(1, 10, size=[2,3], dtype='int32')
input2 = np.random.randint(1, 10, size=[2,3], dtype='int32') input2 = np.random.randint(1, 10, size=[2,3], dtype='int32')
out = exe.run(main_program, out = exe.run(main_program,
feed={'x':input1, 'y':input2}, feed={'x':input1, 'y':input2},
fetch_list=[output.name]) fetch_list=[output.name])
print("{0} + {1} = {2}".format(input1, input2, out)) print("{0} + {1} = {2}".format(input1, input2, out))
...@@ -12945,8 +12947,8 @@ def psroi_pool(input, ...@@ -12945,8 +12947,8 @@ def psroi_pool(input,
spatial_scale (float): ${spatial_scale_comment} Default: 1.0 spatial_scale (float): ${spatial_scale_comment} Default: 1.0
pooled_height (int): ${pooled_height_comment} Default: 1 pooled_height (int): ${pooled_height_comment} Default: 1
pooled_width (int): ${pooled_width_comment} Default: 1 pooled_width (int): ${pooled_width_comment} Default: 1
name(str, optional): The default value is None. name(str, optional): The default value is None.
Normally there is no need for user to set this property. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` For more information, please refer to :ref:`api_guide_Name`
Returns: Returns:
...@@ -13016,8 +13018,8 @@ def prroi_pool(input, ...@@ -13016,8 +13018,8 @@ def prroi_pool(input,
Equals the reciprocal of total stride in convolutional layers, Default: 1.0. Equals the reciprocal of total stride in convolutional layers, Default: 1.0.
pooled_height (integer): The pooled output height. Default: 1. pooled_height (integer): The pooled output height. Default: 1.
pooled_width (integer): The pooled output width. Default: 1. pooled_width (integer): The pooled output width. Default: 1.
batch_roi_nums (Variable): The number of roi for each image in batch. It batch_roi_nums (Variable): The number of roi for each image in batch. It
should be 1-D Tensor, with shape [N] and dtype int64, should be 1-D Tensor, with shape [N] and dtype int64,
where N is the batch size. Default: None. Be note: The lod of input should be where N is the batch size. Default: None. Be note: The lod of input should be
empty when batch_roi_nums has values; empty when batch_roi_nums has values;
name (str, default None): The name of this operation. name (str, default None): The name of this operation.
...@@ -13033,7 +13035,7 @@ def prroi_pool(input, ...@@ -13033,7 +13035,7 @@ def prroi_pool(input,
x = fluid.data(name='x', shape=[None, 490, 28, 28], dtype='float32') x = fluid.data(name='x', shape=[None, 490, 28, 28], dtype='float32')
rois = fluid.data(name='rois', shape=[None, 4], lod_level=1, dtype='float32') rois = fluid.data(name='rois', shape=[None, 4], lod_level=1, dtype='float32')
pool_out = fluid.layers.prroi_pool(x, rois, 1.0, 7, 7) pool_out = fluid.layers.prroi_pool(x, rois, 1.0, 7, 7)
## prroi_pool with batch_roi_num ## prroi_pool with batch_roi_num
batchsize=4 batchsize=4
x2 = fluid.data(name='x2', shape=[batchsize, 490, 28, 28], dtype='float32') x2 = fluid.data(name='x2', shape=[batchsize, 490, 28, 28], dtype='float32')
...@@ -13075,7 +13077,7 @@ def pixel_shuffle(x, upscale_factor): ...@@ -13075,7 +13077,7 @@ def pixel_shuffle(x, upscale_factor):
to a tensor of shape [N, C/r**2, H*r, W*r]. to a tensor of shape [N, C/r**2, H*r, W*r].
This is useful for implementing efficient sub-pixel convolution This is useful for implementing efficient sub-pixel convolution
with a stride of 1/r. with a stride of 1/r.
Please refer to the paper: `Real-Time Single Image and Video Super-Resolution Please refer to the paper: `Real-Time Single Image and Video Super-Resolution
Using an Efficient Sub-Pixel Convolutional Neural Network <https://arxiv.org/abs/1609.05158v2>`_ . 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.
...@@ -13101,13 +13103,13 @@ def pixel_shuffle(x, upscale_factor): ...@@ -13101,13 +13103,13 @@ def pixel_shuffle(x, upscale_factor):
place = fluid.CPUPlace() place = fluid.CPUPlace()
exe = fluid.Executor(place) exe = fluid.Executor(place)
exe.run(fluid.default_startup_program()) exe.run(fluid.default_startup_program())
input_data = np.random.rand(2,9,4,4).astype("float32") input_data = np.random.rand(2,9,4,4).astype("float32")
output_data = exe.run(fluid.default_main_program(), output_data = exe.run(fluid.default_main_program(),
feed={"input":input_data}, feed={"input":input_data},
fetch_list=[output], fetch_list=[output],
return_numpy=True) return_numpy=True)
# print(output.shape) # print(output.shape)
# (2L, 1L, 12L, 12L) # (2L, 1L, 12L, 12L)
...@@ -13242,7 +13244,7 @@ def where(condition): ...@@ -13242,7 +13244,7 @@ def where(condition):
condition(Variable): A bool tensor with rank at least 1, the data type is bool. condition(Variable): A bool tensor with rank at least 1, the data type is bool.
Returns: Returns:
Variable, the output data type is int64. : The tensor variable storing a 2-D tensor, which involves all coordinate. Variable, the output data type is int64. : The tensor variable storing a 2-D tensor, which involves all coordinate.
Examples: Examples:
.. code-block:: python .. code-block:: python
...@@ -13298,7 +13300,7 @@ def sign(x): ...@@ -13298,7 +13300,7 @@ def sign(x):
import numpy as np import numpy as np
# [1.0, 0.0, -1.0] # [1.0, 0.0, -1.0]
data = fluid.layers.sign(np.array([3.0, 0.0, -2.0], dtype='float32')) data = fluid.layers.sign(np.array([3.0, 0.0, -2.0], dtype='float32'))
""" """
helper = LayerHelper("sign", **locals()) helper = LayerHelper("sign", **locals())
...@@ -13315,7 +13317,7 @@ def sign(x): ...@@ -13315,7 +13317,7 @@ def sign(x):
def unique(x, dtype='int32'): def unique(x, dtype='int32'):
""" """
**unique** **unique**
Return a unique tensor for `x` and an index tensor pointing to this unique tensor. Return a unique tensor for `x` and an index tensor pointing to this unique tensor.
...@@ -13357,7 +13359,7 @@ def unique(x, dtype='int32'): ...@@ -13357,7 +13359,7 @@ def unique(x, dtype='int32'):
def unique_with_counts(x, dtype='int32'): def unique_with_counts(x, dtype='int32'):
""" """
This OP return a unique tensor for `x` , and count tensor that the count of unique result in raw input, \ This OP return a unique tensor for `x` , and count tensor that the count of unique result in raw input, \
and an index tensor pointing to this unique tensor. and an index tensor pointing to this unique tensor.
**NOTICE**: This op support the variable type of Tensor only. **NOTICE**: This op support the variable type of Tensor only.
...@@ -13365,7 +13367,7 @@ def unique_with_counts(x, dtype='int32'): ...@@ -13365,7 +13367,7 @@ def unique_with_counts(x, dtype='int32'):
x(Variable): A 1-D input tensor with input shape of :math:`[N]` , the input data type is float32, float64, int32, int64. x(Variable): A 1-D input tensor with input shape of :math:`[N]` , the input data type is float32, float64, int32, int64.
dtype(np.dtype|core.VarDesc.VarType|str): The type of count and index tensor, it could be int32, int64. Defalut value is int32. dtype(np.dtype|core.VarDesc.VarType|str): The type of count and index tensor, it could be int32, int64. Defalut value is int32.
Returns: Returns:
tuple, the variable type in tuple is Tensor, the output :attr:`out` data type is the same as input :attr:`x`, \ tuple, the variable type in tuple is Tensor, the output :attr:`out` data type is the same as input :attr:`x`, \
and data type of output :attr:`index` and :attr:`count` will be int32 or int64.: The :attr:`out` is unique tensor for input :attr:`x`,\ and data type of output :attr:`index` and :attr:`count` will be int32 or int64.: The :attr:`out` is unique tensor for input :attr:`x`,\
the data shape is :math:`[K]`, the `K` may be different to the `N` in shape of :attr:`x`. :attr:`index` is an index tensor pointing\ the data shape is :math:`[K]`, the `K` may be different to the `N` in shape of :attr:`x`. :attr:`index` is an index tensor pointing\
...@@ -13432,24 +13434,24 @@ def deformable_conv(input, ...@@ -13432,24 +13434,24 @@ def deformable_conv(input,
Compute 2-D deformable convolution on 4-D input. Compute 2-D deformable convolution on 4-D input.
Given input image x, output feature map y, the deformable convolution operation can be expressed as follow: Given input image x, output feature map y, the deformable convolution operation can be expressed as follow:
Deformable Convolution v2: Deformable Convolution v2:
.. math:: .. math::
y(p) = \sum_{k=1}^{K}{w_k * x(p + p_k + \Delta p_k) * \Delta m_k} y(p) = \sum_{k=1}^{K}{w_k * x(p + p_k + \Delta p_k) * \Delta m_k}
Deformable Convolution v1: Deformable Convolution v1:
.. math:: .. math::
y(p) = \sum_{k=1}^{K}{w_k * x(p + p_k + \Delta p_k)} y(p) = \sum_{k=1}^{K}{w_k * x(p + p_k + \Delta p_k)}
Where :math:`\Delta p_k` and :math:`\Delta m_k` are the learnable offset and modulation scalar for the k-th location, Where :math:`\Delta p_k` and :math:`\Delta m_k` are the learnable offset and modulation scalar for the k-th location,
Which :math:`\Delta m_k` is one in deformable convolution v1. Please refer to `Deformable ConvNets v2: More Deformable, Better Results Which :math:`\Delta m_k` is one in deformable convolution v1. Please refer to `Deformable ConvNets v2: More Deformable, Better Results
<https://arxiv.org/abs/1811.11168v2>`_ and `Deformable Convolutional Networks <https://arxiv.org/abs/1703.06211>`_. <https://arxiv.org/abs/1811.11168v2>`_ and `Deformable Convolutional Networks <https://arxiv.org/abs/1703.06211>`_.
Example: Example:
- Input: - Input:
...@@ -13501,7 +13503,7 @@ def deformable_conv(input, ...@@ -13501,7 +13503,7 @@ def deformable_conv(input,
connected to the second half of the input channels. Default: groups=1. connected to the second half of the input channels. Default: groups=1.
deformable_groups (int): The number of deformable group partitions. deformable_groups (int): The number of deformable group partitions.
Default: deformable_groups = 1. Default: deformable_groups = 1.
im2col_step (int): Maximum number of images per im2col computation; im2col_step (int): Maximum number of images per im2col computation;
The total batch size should be devisable by this value or smaller The total batch size should be devisable by this value or smaller
than this value; if you face out of memory problem, you can try than this value; if you face out of memory problem, you can try
to use a smaller value here. to use a smaller value here.
...@@ -13510,7 +13512,7 @@ def deformable_conv(input, ...@@ -13510,7 +13512,7 @@ def deformable_conv(input,
of deformable conv. If it is set to None or one attribute of ParamAttr, of deformable conv. If it is set to None or one attribute of ParamAttr,
deformable conv will create ParamAttr as param_attr. deformable conv will create ParamAttr as param_attr.
If the Initializer of the param_attr is not set, the parameter is If the Initializer of the param_attr is not set, the parameter is
initialized with :math:`Normal(0.0, std)`, and the initialized with :math:`Normal(0.0, std)`, and the
:math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None. :math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None.
bias_attr (ParamAttr|bool, Optional): The parameter attribute for the bias of bias_attr (ParamAttr|bool, Optional): The parameter attribute for the bias of
deformable conv layer. If it is set to False, no bias will be added deformable conv layer. If it is set to False, no bias will be added
...@@ -13531,7 +13533,7 @@ def deformable_conv(input, ...@@ -13531,7 +13533,7 @@ def deformable_conv(input,
.. code-block:: python .. code-block:: python
#deformable conv v2: #deformable conv v2:
import paddle.fluid as fluid import paddle.fluid as fluid
C_in, H_in, W_in = 3, 32, 32 C_in, H_in, W_in = 3, 32, 32
filter_size, deformable_groups = 3, 1 filter_size, deformable_groups = 3, 1
...@@ -13659,7 +13661,7 @@ def unfold(x, kernel_sizes, strides=1, paddings=0, dilations=1, name=None): ...@@ -13659,7 +13661,7 @@ def unfold(x, kernel_sizes, strides=1, paddings=0, dilations=1, name=None):
Parameters: Parameters:
x(Varaible): 4-D Tensor, input tensor of format [N, C, H, W], x(Varaible): 4-D Tensor, input tensor of format [N, C, H, W],
data type can be float32 or float64 data type can be float32 or float64
kernel_sizes(int|list): The size of convolution kernel, should be [k_h, k_w] kernel_sizes(int|list): The size of convolution kernel, should be [k_h, k_w]
or an integer k treated as [k, k]. or an integer k treated as [k, k].
...@@ -13676,16 +13678,16 @@ def unfold(x, kernel_sizes, strides=1, paddings=0, dilations=1, name=None): ...@@ -13676,16 +13678,16 @@ def unfold(x, kernel_sizes, strides=1, paddings=0, dilations=1, name=None):
dilations(int|list): the dilations of convolution kernel, should be dilations(int|list): the dilations of convolution kernel, should be
[dilation_h, dilation_w], or an integer dilation treated as [dilation_h, dilation_w], or an integer dilation treated as
[dilation, dilation]. For default, it will be [1, 1]. [dilation, dilation]. For default, it will be [1, 1].
name(str, optional): The default value is None. name(str, optional): The default value is None.
Normally there is no need for user to set this property. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` For more information, please refer to :ref:`api_guide_Name`
Returns: Returns:
The tensor variable corresponding to the sliding local blocks. The tensor variable corresponding to the sliding local blocks.
The output shape is [N, Cout, Lout] as decriabled above. The output shape is [N, Cout, Lout] as decriabled above.
Cout is the total number of values within each block, Cout is the total number of values within each block,
and Lout is the total number of such blocks. and Lout is the total number of such blocks.
The data type of output is the same as the input :math:`x` The data type of output is the same as the input :math:`x`
Return Type: Return Type:
...@@ -13768,21 +13770,21 @@ def deformable_roi_pooling(input, ...@@ -13768,21 +13770,21 @@ def deformable_roi_pooling(input,
name=None): name=None):
""" """
Deformable ROI Pooling Layer Deformable ROI Pooling Layer
Performs deformable region-of-interest pooling on inputs. As described Performs deformable region-of-interest pooling on inputs. As described
in `Deformable Convolutional Networks <https://arxiv.org/abs/1703.06211>`_, it will get offset for each bin after in `Deformable Convolutional Networks <https://arxiv.org/abs/1703.06211>`_, it will get offset for each bin after
roi pooling so that pooling at correct region. Batch_size will change to the number of region bounding boxes after deformable_roi_pooling. roi pooling so that pooling at correct region. Batch_size will change to the number of region bounding boxes after deformable_roi_pooling.
The operation has three steps: The operation has three steps:
1. Dividing each region proposal into equal-sized sections with the pooled_width and pooled_height. 1. Dividing each region proposal into equal-sized sections with the pooled_width and pooled_height.
2. Add offset to pixel in ROI to get new location and the new value which are computed directly through 2. Add offset to pixel in ROI to get new location and the new value which are computed directly through
bilinear interpolation with four nearest pixel. bilinear interpolation with four nearest pixel.
3. Sample several points in each bin to get average values as output. 3. Sample several points in each bin to get average values as output.
Args: Args:
input (Variable):The input of deformable roi pooling and it is tensor which value type is float32. The shape of input is input (Variable):The input of deformable roi pooling and it is tensor which value type is float32. The shape of input is
[N, C, H, W]. Where N is batch size, C is number of input channels, [N, C, H, W]. Where N is batch size, C is number of input channels,
...@@ -13792,14 +13794,14 @@ def deformable_roi_pooling(input, ...@@ -13792,14 +13794,14 @@ def deformable_roi_pooling(input,
is 1. Given as [[x1, y1, x2, y2], ...], (x1, y1) is is 1. Given as [[x1, y1, x2, y2], ...], (x1, y1) is
the top left coordinates, and (x2, y2) is the bottom the top left coordinates, and (x2, y2) is the bottom
right coordinates, which value type is float32. right coordinates, which value type is float32.
trans (Variable): Offset of features on ROIs while pooling which value type is float32. The format is [N, C, H, W], where trans (Variable): Offset of features on ROIs while pooling which value type is float32. The format is [N, C, H, W], where
N is number of ROIs, C is number of channels, which indicate the offset distance N is number of ROIs, C is number of channels, which indicate the offset distance
in the x and y directions, H is pooled height, and W is pooled width. in the x and y directions, H is pooled height, and W is pooled width.
no_trans (bool): Whether to add offset to get new value or not while roi pooling, which value with type bool is True or False. no_trans (bool): Whether to add offset to get new value or not while roi pooling, which value with type bool is True or False.
If value is True, no offset will be added in operation. Default: False. If value is True, no offset will be added in operation. Default: False.
spatial_scale (float): Ratio of input feature map height (or width) to raw image height (or width), which value type is float32. spatial_scale (float): Ratio of input feature map height (or width) to raw image height (or width), which value type is float32.
Equals the reciprocal of total stride in convolutional layers, Default: 1.0. Equals the reciprocal of total stride in convolutional layers, Default: 1.0.
group_size (list|tuple): The number of groups which input channels are divided and the input is list or tuple, which value type is int32. (eg.number of input channels group_size (list|tuple): The number of groups which input channels are divided and the input is list or tuple, which value type is int32. (eg.number of input channels
is k1 * k2 * (C + 1), which k1 and k2 are group width and height and C+1 is number of output is k1 * k2 * (C + 1), which k1 and k2 are group width and height and C+1 is number of output
channels.) eg.(4, 6), which 4 is height of group and 6 is width of group. Default: [1, 1]. channels.) eg.(4, 6), which 4 is height of group and 6 is width of group. Default: [1, 1].
pooled_height (int): The pooled output height which value type is int32. Default: 1. pooled_height (int): The pooled output height which value type is int32. Default: 1.
...@@ -13821,50 +13823,50 @@ def deformable_roi_pooling(input, ...@@ -13821,50 +13823,50 @@ def deformable_roi_pooling(input,
# position_sensitive=True # position_sensitive=True
import paddle.fluid as fluid import paddle.fluid as fluid
input = fluid.data(name="input", input = fluid.data(name="input",
shape=[2, 192, 64, 64], shape=[2, 192, 64, 64],
dtype='float32') dtype='float32')
rois = fluid.data(name="rois", rois = fluid.data(name="rois",
shape=[-1, 4], shape=[-1, 4],
dtype='float32', dtype='float32',
lod_level=1) lod_level=1)
trans = fluid.data(name="trans", trans = fluid.data(name="trans",
shape=[2, 384, 64, 64], shape=[2, 384, 64, 64],
dtype='float32') dtype='float32')
x = fluid.layers.deformable_roi_pooling(input=input, x = fluid.layers.deformable_roi_pooling(input=input,
rois=rois, rois=rois,
trans=trans, trans=trans,
no_trans=False, no_trans=False,
spatial_scale=1.0, spatial_scale=1.0,
group_size=(1, 1), group_size=(1, 1),
pooled_height=8, pooled_height=8,
pooled_width=8, pooled_width=8,
part_size=(8, 8), part_size=(8, 8),
sample_per_part=4, sample_per_part=4,
trans_std=0.1, trans_std=0.1,
position_sensitive=True) position_sensitive=True)
# position_sensitive=False # position_sensitive=False
import paddle.fluid as fluid import paddle.fluid as fluid
input = fluid.data(name="input", input = fluid.data(name="input",
shape=[2, 192, 64, 64], shape=[2, 192, 64, 64],
dtype='float32') dtype='float32')
rois = fluid.data(name="rois", rois = fluid.data(name="rois",
shape=[-1, 4], shape=[-1, 4],
dtype='float32', dtype='float32',
lod_level=1) lod_level=1)
trans = fluid.data(name="trans", trans = fluid.data(name="trans",
shape=[2, 384, 64, 64], shape=[2, 384, 64, 64],
dtype='float32') dtype='float32')
x = fluid.layers.deformable_roi_pooling(input=input, x = fluid.layers.deformable_roi_pooling(input=input,
rois=rois, rois=rois,
trans=trans, trans=trans,
no_trans=False, no_trans=False,
spatial_scale=1.0, spatial_scale=1.0,
group_size=(1, 1), group_size=(1, 1),
pooled_height=8, pooled_height=8,
pooled_width=8, pooled_width=8,
part_size=(8, 8), part_size=(8, 8),
sample_per_part=4, sample_per_part=4,
trans_std=0.1, trans_std=0.1,
position_sensitive=False) position_sensitive=False)
""" """
...@@ -13925,8 +13927,8 @@ def shard_index(input, index_num, nshards, shard_id, ignore_value=-1): ...@@ -13925,8 +13927,8 @@ def shard_index(input, index_num, nshards, shard_id, ignore_value=-1):
the `shard_id` matches the shard with the input index inside, the index is the `shard_id` matches the shard with the input index inside, the index is
recomputed on the basis of the shard offset, elsewise it is set to recomputed on the basis of the shard offset, elsewise it is set to
`ignore_value`. The detail is as follows: `ignore_value`. The detail is as follows:
:: ::
shard_size = (index_num + nshards - 1) // nshards shard_size = (index_num + nshards - 1) // nshards
y = x % shard_size if x // shard_size == shard_id else ignore_value y = x % shard_size if x // shard_size == shard_id else ignore_value
...@@ -13935,22 +13937,22 @@ def shard_index(input, index_num, nshards, shard_id, ignore_value=-1): ...@@ -13935,22 +13937,22 @@ def shard_index(input, index_num, nshards, shard_id, ignore_value=-1):
Examples: Examples:
:: ::
Input: Input:
X.shape = [4, 1] X.shape = [4, 1]
X.data = [[1], [6], [12], [19]] X.data = [[1], [6], [12], [19]]
index_num = 20 index_num = 20
nshards = 2 nshards = 2
ignore_value = -1 ignore_value = -1
if shard_id == 0, we get: if shard_id == 0, we get:
Out.shape = [4, 1] Out.shape = [4, 1]
Out.data = [[1], [6], [-1], [-1]] Out.data = [[1], [6], [-1], [-1]]
if shard_id == 1, we get: if shard_id == 1, we get:
Out.shape = [4, 1] Out.shape = [4, 1]
Out.data = [[-1], [-1], [2], [9]] Out.data = [[-1], [-1], [2], [9]]
Args: Args:
- **input** (Variable): Input indices, last dimension must be 1. - **input** (Variable): Input indices, last dimension must be 1.
- **index_num** (scalar): An integer defining the range of the index. - **index_num** (scalar): An integer defining the range of the index.
...@@ -14015,26 +14017,26 @@ def hard_swish(x, threshold=6.0, scale=6.0, offset=3.0, name=None): ...@@ -14015,26 +14017,26 @@ def hard_swish(x, threshold=6.0, scale=6.0, offset=3.0, name=None):
threshold (float, optional): The threshold in Relu function. Default: 6.0 threshold (float, optional): The threshold in Relu function. Default: 6.0
scale (float, optional): The scale factor. Default: 6.0 scale (float, optional): The scale factor. Default: 6.0
offset (float, optional): The offset factor. Default: 3.0 offset (float, optional): The offset factor. Default: 3.0
name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns: Returns:
Variable: The output tensor with the same shape and data type as input. Variable: The output tensor with the same shape and data type as input.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
import numpy as np import numpy as np
DATATYPE='float32' DATATYPE='float32'
x_data = np.array([i for i in range(1,5)]).reshape([1,1,4]).astype(DATATYPE) x_data = np.array([i for i in range(1,5)]).reshape([1,1,4]).astype(DATATYPE)
x = fluid.data(name="x", shape=[None,1,4], dtype=DATATYPE) x = fluid.data(name="x", shape=[None,1,4], dtype=DATATYPE)
y = fluid.layers.hard_swish(x) y = fluid.layers.hard_swish(x)
place = fluid.CPUPlace() place = fluid.CPUPlace()
#place = fluid.CUDAPlace(0) #place = fluid.CUDAPlace(0)
exe = fluid.Executor(place) exe = fluid.Executor(place)
...@@ -14082,8 +14084,8 @@ def gather_tree(ids, parents): ...@@ -14082,8 +14084,8 @@ def gather_tree(ids, parents):
[[0 0] [[0 0]
[0 1]]] [0 1]]]
Then: Then:
gather_tree(ids, parents) gather_tree(ids, parents)
= [[[2 2] = [[[2 2]
[1 6]] [1 6]]
[[3 3] [[3 3]
...@@ -14139,17 +14141,17 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0): ...@@ -14139,17 +14141,17 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
Examples: Examples:
:: ::
Input: Input:
shape = [1, 2] shape = [1, 2]
Output: Output:
result=[[0.8505902, 0.8397286]] result=[[0.8505902, 0.8397286]]
Args: Args:
shape (list|tuple|Variable): The shape of the output Tensor, if the shape is a list or tuple, shape (list|tuple|Variable): The shape of the output Tensor, if the shape is a list or tuple,
its elements can be an integer its elements can be an integer
or a Tensor with the shape [1], and the type of the Tensor must be int32 or int64. or a Tensor with the shape [1], and the type of the Tensor must be int32 or int64.
If the shape is a Variable, it is a 1-D Tensor, and the type of the Tensor must be int32 or int64. If the shape is a Variable, it is a 1-D Tensor, and the type of the Tensor must be int32 or int64.
dtype(np.dtype|core.VarDesc.VarType|str, optional): The type of the output Tensor. Supported data types: float32, float64. dtype(np.dtype|core.VarDesc.VarType|str, optional): The type of the output Tensor. Supported data types: float32, float64.
Default: float32. Default: float32.
...@@ -14160,12 +14162,12 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0): ...@@ -14160,12 +14162,12 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
operator will always generate the same random numbers every time. operator will always generate the same random numbers every time.
Default 0. Default 0.
Returns: Returns:
Variable: A Tensor of the specified shape filled with uniform_random values. Variable: A Tensor of the specified shape filled with uniform_random values.
Raises: Raises:
TypeError: The shape type should be list or tuple or variable. TypeError: The shape type should be list or tuple or variable.
Examples: Examples:
.. code-block:: python .. code-block:: python
...@@ -14187,7 +14189,7 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0): ...@@ -14187,7 +14189,7 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
result_3 = fluid.layers.uniform_random(var_shape) result_3 = fluid.layers.uniform_random(var_shape)
var_shape_int32 = fluid.data(name='var_shape_int32', shape=[2], dtype="int32") var_shape_int32 = fluid.data(name='var_shape_int32', shape=[2], dtype="int32")
result_4 = fluid.layers.uniform_random(var_shape_int32) result_4 = fluid.layers.uniform_random(var_shape_int32)
""" """
......
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