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Merge pull request #11513 from chengduoZH/refine_conv3d_doc

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Fix conv3d/conv3d_trans/slice/mean_iou Doc
wangkuiyi-patch-1
chengduo 7 years ago committed by GitHub
parent 3a25f21eae 7b82353010
commit ea03a2287e
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2 changed files with 56 additions and 53 deletions
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37
paddle/fluid/operators/slice_op.cc
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@ -95,23 +95,26 @@ of that dimension. If the value passed to start or end is larger than
the n (the number of elements in this dimension), it represents n.
For slicing to the end of a dimension with unknown size, it is recommended
to pass in INT_MAX. If axes are omitted, they are set to [0, ..., ndim-1].
Example 1:
Given:
data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
axes = [0, 1]
starts = [1, 0]
ends = [2, 3]
Then:
result = [ [5, 6, 7], ]
Example 2:
Given:
data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
starts = [0, 1]
ends = [-1, 1000]
Then:
result = [ [2, 3, 4], ]
Following examples will explain how slice works:
.. code-block:: text
Cast1:
Given:
data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
axes = [0, 1]
starts = [1, 0]
ends = [2, 3]
Then:
result = [ [5, 6, 7], ]
Cast2:
Given:
data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
starts = [0, 1]
ends = [-1, 1000]
Then:
result = [ [2, 3, 4], ]
)DOC");
}
};

72
python/paddle/fluid/layers/nn.py
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@ -1373,10 +1373,8 @@ def conv2d(input,
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[3, 32, 32], dtype='float32')
conv2d = fluid.layers.conv2d(
input=data, num_filters=2, filter_size=3, act="relu")
data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32')
conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu")
"""
num_channels = input.shape[1]
@ -1478,8 +1476,7 @@ def conv3d(input,
* :math:`\\ast`: Convolution operation.
* :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
* :math:`\\sigma`: Activation function.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be
different.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
Example:
@ -1541,10 +1538,8 @@ def conv3d(input,
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[3, 12, 32, 32], dtype='float32')
conv2d = fluid.layers.conv3d(
input=data, num_filters=2, filter_size=3, act="relu")
data = fluid.layers.data(name='data', shape=[3, 12, 32, 32], dtype='float32')
conv3d = fluid.layers.conv3d(input=data, num_filters=2, filter_size=3, act="relu")
"""
l_type = 'conv3d'
@ -2152,32 +2147,36 @@ def conv2d_transpose(input,
represent height and width, respectively. The details of convolution transpose
layer, please refer to the following explanation and references
`therein <http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf>`_.
If bias attribution and activation type are provided, bias is added to
the output of the convolution, and the corresponding activation function
is applied to the final result.
For each input :math:`X`, the equation is:
.. math::
Out = W \\ast X
Out = \sigma (W \\ast X + b)
In the above equation:
Where:
* :math:`X`: Input value, a tensor with NCHW format.
* :math:`W`: Filter value, a tensor with MCHW format.
* :math:`\\ast` : Convolution transpose operation.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be
different.
* :math:`\\ast`: Convolution operation.
* :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
* :math:`\\sigma`: Activation function.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
Example:
- Input:
Input shape: $(N, C_{in}, H_{in}, W_{in})$
Input shape: :math:`(N, C_{in}, H_{in}, W_{in})`
Filter shape: $(C_{in}, C_{out}, H_f, W_f)$
Filter shape: :math:`(C_{in}, C_{out}, H_f, W_f)`
- Output:
Output shape: $(N, C_{out}, H_{out}, W_{out})$
Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`
Where
@ -2231,10 +2230,8 @@ def conv2d_transpose(input,
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[3, 32, 32], dtype='float32')
conv2d_transpose = fluid.layers.conv2d_transpose(
input=data, num_filters=2, filter_size=3)
data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32')
conv2d_transpose = fluid.layers.conv2d_transpose(input=data, num_filters=2, filter_size=3)
"""
helper = LayerHelper("conv2d_transpose", **locals())
if not isinstance(input, Variable):
@ -2314,32 +2311,36 @@ def conv3d_transpose(input,
two elements. These two elements represent height and width, respectively.
The details of convolution transpose layer, please refer to the following
explanation and references `therein <http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf>`_.
If bias attribution and activation type are provided, bias is added to
the output of the convolution, and the corresponding activation function
is applied to the final result.
For each input :math:`X`, the equation is:
.. math::
Out = W \\ast X
Out = \sigma (W \\ast X + b)
In the above equation:
* :math:`X`: Input value, a tensor with NCDHW format.
* :math:`W`: Filter value, a tensor with MCDHW format.
* :math:`\\ast` : Convolution transpose operation.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be
different.
* :math:`\\ast`: Convolution operation.
* :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
* :math:`\\sigma`: Activation function.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
Example:
- Input:
Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$
Input shape: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`
Filter shape: $(C_{in}, C_{out}, D_f, H_f, W_f)$
Filter shape: :math:`(C_{in}, C_{out}, D_f, H_f, W_f)`
- Output:
Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$
Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`
Where
@ -2394,10 +2395,8 @@ def conv3d_transpose(input,
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[3, 12, 32, 32], dtype='float32')
conv2d_transpose = fluid.layers.conv3d_transpose(
input=data, num_filters=2, filter_size=3)
data = fluid.layers.data(name='data', shape=[3, 12, 32, 32], dtype='float32')
conv3d_transpose = fluid.layers.conv3d_transpose(input=data, num_filters=2, filter_size=3)
"""
l_type = "conv3d_transpose"
helper = LayerHelper(l_type, **locals())
@ -4745,8 +4744,8 @@ def mean_iou(input, label, num_classes):
IOU is defined as follows:
.. math::
IOU = true_positive / (true_positive + false_positive + false_negative).
IOU = \\frac{true\_positiv}{(true\_positive + false\_positive + false\_negative)}.
The predictions are accumulated in a confusion matrix and mean-IOU
is then calculated from it.
@ -4754,8 +4753,9 @@ def mean_iou(input, label, num_classes):
Args:
input (Variable): A 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.
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.
Returns:
mean_iou (Variable): A Tensor representing the mean intersection-over-union with shape [1].

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