提交 53535b4f 编写于 作者: L Liufang Sang 提交者: whs

fix en api doc (#20245)

上级 478e4d68
......@@ -180,7 +180,7 @@ paddle.fluid.layers.topk (ArgSpec(args=['input', 'k', 'name'], varargs=None, key
paddle.fluid.layers.warpctc (ArgSpec(args=['input', 'label', 'blank', 'norm_by_times', 'input_length', 'label_length'], varargs=None, keywords=None, defaults=(0, False, None, None)), ('document', 'a5be881ada816e47ea7a6ee4396da357'))
paddle.fluid.layers.sequence_reshape (ArgSpec(args=['input', 'new_dim'], varargs=None, keywords=None, defaults=None), ('document', 'eeb1591cfc854c6ffdac77b376313c44'))
paddle.fluid.layers.transpose (ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '8e72db173d4c082e27cb11f31d8c9bfa'))
paddle.fluid.layers.im2sequence (ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None)), ('document', '33134416fc27dd65a767e5f15116ee16'))
paddle.fluid.layers.im2sequence (ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None)), ('document', 'fe352915a543cec434f74e9b32ac49da'))
paddle.fluid.layers.nce (ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0, False)), ('document', '83d4ca6dfb957912807f535756e76992'))
paddle.fluid.layers.sampled_softmax_with_cross_entropy (ArgSpec(args=['logits', 'label', 'num_samples', 'num_true', 'remove_accidental_hits', 'use_customized_samples', 'customized_samples', 'customized_probabilities', 'seed'], varargs=None, keywords=None, defaults=(1, True, False, None, None, 0)), ('document', 'd4435a63d34203339831ee6a86ef9242'))
paddle.fluid.layers.hsigmoid (ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name', 'path_table', 'path_code', 'is_custom', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, False, False)), ('document', 'b83e7dfa81059b39bb137922dc914f50'))
......@@ -218,7 +218,7 @@ paddle.fluid.layers.scatter_nd_add (ArgSpec(args=['ref', 'index', 'updates', 'na
paddle.fluid.layers.scatter_nd (ArgSpec(args=['index', 'updates', 'shape', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e43f1d3a938b35da246aea3e72a020ec'))
paddle.fluid.layers.sequence_scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'abe3f714120117a5a3d3e639853932bf'))
paddle.fluid.layers.random_crop (ArgSpec(args=['x', 'shape', 'seed'], varargs=None, keywords=None, defaults=(None,)), ('document', '042af0b8abea96b40c22f6e70d99e042'))
paddle.fluid.layers.mean_iou (ArgSpec(args=['input', 'label', 'num_classes'], varargs=None, keywords=None, defaults=None), ('document', 'e714b4aa7993dfe9c1a38886875dbaac'))
paddle.fluid.layers.mean_iou (ArgSpec(args=['input', 'label', 'num_classes'], varargs=None, keywords=None, defaults=None), ('document', 'dea29c0c3cdbd5b498afef60e58c9d7c'))
paddle.fluid.layers.relu (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '0942c174f4f6fb274976d4357356f6a2'))
paddle.fluid.layers.selu (ArgSpec(args=['x', 'scale', 'alpha', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'f93c61f5b0bf933cd425a64dca2c4fdd'))
paddle.fluid.layers.log (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '02f668664e3bfc4df6c00d7363467140'))
......@@ -239,7 +239,7 @@ paddle.fluid.layers.soft_relu (ArgSpec(args=['x', 'threshold', 'name'], varargs=
paddle.fluid.layers.flatten (ArgSpec(args=['x', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', '424ff350578992f201f2c5c30959ef89'))
paddle.fluid.layers.sequence_mask (ArgSpec(args=['x', 'maxlen', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 'int64', None)), ('document', '6c3f916921b24edaad220f1fcbf039de'))
paddle.fluid.layers.stack (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)), ('document', 'a76f347bf27ffe21b990340d5d9524d5'))
paddle.fluid.layers.pad2d (ArgSpec(args=['input', 'paddings', 'mode', 'pad_value', 'data_format', 'name'], varargs=None, keywords=None, defaults=([0, 0, 0, 0], 'constant', 0.0, 'NCHW', None)), ('document', '3f3abdb795a5c2aad8c2312249551ce5'))
paddle.fluid.layers.pad2d (ArgSpec(args=['input', 'paddings', 'mode', 'pad_value', 'data_format', 'name'], varargs=None, keywords=None, defaults=([0, 0, 0, 0], 'constant', 0.0, 'NCHW', None)), ('document', '4e277f064c1765f77f946da194626ca1'))
paddle.fluid.layers.unstack (ArgSpec(args=['x', 'axis', 'num'], varargs=None, keywords=None, defaults=(0, None)), ('document', 'b0c4ca08d4eb295189e1b107c920d093'))
paddle.fluid.layers.sequence_enumerate (ArgSpec(args=['input', 'win_size', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0, None)), ('document', 'b870fed41abd2aecf929ece65f555fa1'))
paddle.fluid.layers.unique (ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=('int32',)), ('document', 'cab0b06e5683875f12f0efc62fa230a9'))
......@@ -336,7 +336,7 @@ paddle.fluid.layers.reverse (ArgSpec(args=['x', 'axis'], varargs=None, keywords=
paddle.fluid.layers.has_inf (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '51a0fa1cfaf2507c00a215adacdb8a63'))
paddle.fluid.layers.has_nan (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '129cf426e71452fe8276d616a6dc21ae'))
paddle.fluid.layers.isfinite (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', 'b9fff4ffc8d11934cde099f4c39bf841'))
paddle.fluid.layers.range (ArgSpec(args=['start', 'end', 'step', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', 'a45b42f21bc5a4e84b60981a3d629ab3'))
paddle.fluid.layers.range (ArgSpec(args=['start', 'end', 'step', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', '3e982b788b95f959eafeeb0696a3cbde'))
paddle.fluid.layers.linspace (ArgSpec(args=['start', 'stop', 'num', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', '3663d1148946eed4c1c34c81be586b9e'))
paddle.fluid.layers.zeros_like (ArgSpec(args=['x', 'out'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd88a23bcdc443719b3953593f7cef14a'))
paddle.fluid.layers.ones_like (ArgSpec(args=['x', 'out'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd18d42059c6b189cbd3fab2fcb206c15'))
......
......@@ -7270,57 +7270,55 @@ def im2sequence(input,
name=None):
"""
Extracts image patches from the input tensor to form a tensor of shape
{input.batch_size * output_height * output_width, filter_size_H *
filter_size_W * input.channels} which is similar with im2col.
This op use filter / kernel to scan images and convert these images to
sequences. After expanding, the number of time step are
{input.batch_size * output_height * output_width, filter_size_height *
filter_size_width * input.channels}. This op use filter to scan images
and convert these images to sequences. After expanding, the number of time step are
output_height * output_width for an image, in which output_height and
output_width are calculated by below equation:
.. math::
output\_size = 1 + \
(2 * padding + img\_size - block\_size + stride - 1) / stride
output\_height = 1 + \
(padding\_up + padding\_down + input\_height - filter\_size\_height + stride\_height - 1) / stride\_height \\\\
output\_width = 1 + \
(padding\_left + padding\_right + input\_width - filter\_size\_width + stride\_width - 1) / stride\_width
And the dimension of each time step is block_y * block_x * input.channels.
And the dimension of each time step is filter_size_height * filter_size_width * input.channels.
Args:
input (Variable): The input should be a tensor in NCHW format.
Parameters:
input (Variable): The input should be a 4-D Tensor in :math:`NCHW` format. The data type is float32.
filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square.
filter_size(int32 | List[int32]): The filter size. If filter_size is a List,
it must contain two integers, :math:`[filter\_size\_height, filter\_size\_width]` .
Otherwise, the filter size will be a square :math:`[filter\_size, filter\_size]` . Default is 1.
stride(int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: stride = 1.
stride(int32 | List[int32]): The stride size. If stride is a List, it must
contain two integers, :math:`[stride\_height, stride\_width]` . Otherwise, the stride size will be a square :math:`[stride\_size, stride\_size]` . Default is 1.
padding(int|tuple): The padding size. If padding is a tuple, it can
contain two integers like (padding_H, padding_W) which means
padding_up = padding_down = padding_H and
padding_left = padding_right = padding_W. Or it can use
(padding_up, padding_left, padding_down, padding_right) to indicate
paddings of four direction. Otherwise, a scalar padding means
padding_up = padding_down = padding_left = padding_right = padding
Default: padding = 0.
padding(int32 | List[int32]): The padding size. If padding is a List, it can
contain four integers like :math:`[padding\_up, padding\_left, padding\_down, padding\_right]` to indicate
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_left = padding_right = padding_width. Otherwise, a scalar padding means
padding_up = padding_down = padding_left = padding_right = padding.
Default is 0.
input_image_size(Variable): the input contains image real size.It's dim
is [batchsize, 2]. It is dispensable.It is just for batch inference.
input_image_size(Variable, optional): the input contains image real size.It's dim
is :math:`[batchsize, 2]` . It is just for batch inference when not None. Default is None.
out_stride(int|tuple): The scaling of image through CNN. It is
dispensable. It is valid only when input_image_size is not null.
If out_stride is tuple, it must contain two intergers,
(out_stride_H, out_stride_W). Otherwise,
the out_stride_H = out_stride_W = out_stride.
out_stride(int32 | List[int32]): The scaling of image through CNN. It is valid only when input_image_size is not None.
If out_stride is List, it must contain two intergers,
:math:`[out\_stride\_height, out\_stride\_W]` . Otherwise,
the out_stride_height = out_stride_width = out_stride. Default is 1.
name (int): The name of this layer. It is optional.
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:
output: The output is a LoDTensor with shape
{input.batch_size * output_height * output_width,
filter_size_H * filter_size_W * input.channels}.
If we regard output as a matrix, each row of this matrix is
a step of a sequence.
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.
Return Type: Variable
Examples:
......@@ -7372,7 +7370,7 @@ def im2sequence(input,
.. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name='data', shape=[3, 32, 32],
data = fluid.data(name='data', shape=[None, 3, 32, 32],
dtype='float32')
output = fluid.layers.im2sequence(
input=data, stride=[1, 1], filter_size=[2, 2])
......@@ -10182,31 +10180,32 @@ def mean_iou(input, label, num_classes):
is then calculated from it.
Args:
input (Variable): A Tensor of prediction results for semantic labels with type int32 or int64.
Parameters:
input (Variable): A n-D Tensor of prediction results for semantic labels with type int32 or int64.
label (Variable): A Tensor of ground truth labels with type int32 or int64.
Its shape should be the same as input.
num_classes (int): The possible number of labels.
num_classes (int32): The possible number of labels.
Returns:
mean_iou (Variable),out_wrong(Variable),out_correct(Variable):
Three Variables.
Three variables:
- mean_iou(Variable) : A 1-D Tensor representing the mean intersection-over-union with shape [1]. \
Data type is float32.
- out_wrong(Variable) : A 1-D Tensor with shape [num_classes]. Data type is int32. \
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.
- mean_iou : A Tensor representing the mean intersection-over-union with shape [1].
- out_wrong: A Tensor with shape [num_classes]. The wrong numbers of each class.
- out_correct: A Tensor with shape [num_classes]. The correct numbers of each class.
Examples:
.. code-block:: python
import paddle.fluid as fluid
iou_shape = [32, 32]
iou_shape = [None, 32, 32]
num_classes = 5
predict = fluid.layers.data(name='predict', shape=iou_shape)
label = fluid.layers.data(name='label', shape=iou_shape)
iou, wrongs, corrects = fluid.layers.mean_iou(predict, label,
predict = fluid.data(name='predict', shape=iou_shape, dtype='int64')
label = fluid.data(name='label', shape=iou_shape, dtype='int64')
mean_iou, out_wrong, out_correct = fluid.layers.mean_iou(predict, label,
num_classes)
"""
helper = LayerHelper('mean_iou', **locals())
......@@ -10771,7 +10770,30 @@ def pad2d(input,
If mode is 'reflect', paddings[0] and paddings[1] must be no greater
than height-1. And the width dimension has the same condition.
Example:
Parameters:
input (Variable): The input image with [N, C, H, W] format or [N, H, W, C] format, which is a 4-D Tensor with data type float32.
paddings (Variable | List[int32]): The padding size. If padding is a List, it must
contain four integers, (padding_top, padding_bottom, padding_left, padding_right).
Otherwise, it is a 1-D Tensor with shape [4]. Data type is int32.
Default is [0, 0, 0, 0].
mode (str): Three modes: 'constant' (default), 'reflect', 'edge' .
When in 'constant' mode, this op uses a constant value to pad the input tensor.
When in 'reflect' mode, uses reflection of the input boundaries to pad the input tensor.
When in 'edge' mode, uses input boundaries to pad the input tensor.
Default is 'constant'
pad_value (float32): The value to fill the padded areas in 'constant' mode . Default is 0.0
data_format (str): An string from: "NHWC", "NCHW". Specify the data format of
the input data.
Default is "NCHW"
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: a 4-D Tensor padded accordding to paddings and mode and data type is same as input.
Return Type: Variable
Examples:
.. code-block:: text
Given that X is a channel of image from input:
......@@ -10807,29 +10829,11 @@ def pad2d(input,
[4, 4, 4, 5, 6, 6]
[4, 4, 4, 5, 6, 6]]
Args:
input (Variable): The input image with [N, C, H, W] format or [N, H, W, C] format.
paddings (tuple|list|Variable): The padding size. If padding is a tuple, it must
contain four integers, (padding_top, padding_bottom, padding_left, padding_right).
Default: padding = [0, 0, 0, 0].
mode (str): Three modes: constant(default), reflect, edge. Default: constant
pad_value (float32): The value to fill the padded areas in constant mode. Default: 0
data_format (str): An optional string from: "NHWC", "NCHW". Specify the data format of
the input data.
Default: "NCHW"
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The tensor variable padded accordding to paddings and mode.
Examples:
Code Examples:
.. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name='data', shape=[3, 32, 32],
data = fluid.data(name='data', shape=[None, 3, 32, 32],
dtype='float32')
result = fluid.layers.pad2d(input=data, paddings=[1, 2, 3, 4],
mode='reflect')
......
......@@ -850,18 +850,20 @@ def range(start, end, step, dtype):
Values are generated within the half-open interval [start, stop) (in other words,
the interval including start but excluding stop).
args:
start(int|float|Variable): Start of interval. The interval includes this value.
end(int|float|Variable): End of interval. The interval does not include this
Parameters:
start(float32 | float64 | int32 | int64 | Variable): Start of interval. The interval includes this value.
when start is Variable, it is a 1-D Tensor with shape [1].
end(float32 | float64 | int32 | int64 | Variable): End of interval. The interval does not include this
value, except in some cases where step is not an integer
and floating point round-off affects the length of out.
step(int|float|Variable): Spacing between values. For any output out, this is the
and floating point round-off affects the length of out. When end is Variable,
it is a 1-D Tensor with shape [1].
step(float32 | float64 | int32 | int64 | Variable): Spacing between values. For any output out, this is the
distance between two adjacent values, out[i+1] - out[i].
The default step size is 1.
dtype(string): 'float32'|'int32'|..., the data type of the output tensor.
dtype(str): the data type of the output tensor, can be float32, float64, int32, int64.
returns:
Evenly spaced values within a given interval.
Returns: a 1-D Tensor which is evenly spaced values within a given interval. Its data type is set by dtype.
Return type: Variable
examples:
......
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