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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/11/21 16:49:58 UTC
[GitHub] indhub closed pull request #13315: Fix sphinx docstring warnings.
indhub closed pull request #13315: Fix sphinx docstring warnings.
URL: https://github.com/apache/incubator-mxnet/pull/13315
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diff --git a/python/mxnet/contrib/io.py b/python/mxnet/contrib/io.py
index 6020b3ee1c9..3364fcd215d 100644
--- a/python/mxnet/contrib/io.py
+++ b/python/mxnet/contrib/io.py
@@ -37,8 +37,8 @@ class DataLoaderIter(DataIter):
dtype : str, optional
The dtype specifier, can be float32 or float16
- Example usage:
- ----------
+ Examples
+ --------
>>> import mxnet as mx
>>> from mxnet.gluon.data.vision import MNIST
>>> from mxnet.gluon.data import DataLoader
diff --git a/python/mxnet/contrib/svrg_optimization/svrg_module.py b/python/mxnet/contrib/svrg_optimization/svrg_module.py
index 47d0e57b45e..eecb87cf25b 100644
--- a/python/mxnet/contrib/svrg_optimization/svrg_module.py
+++ b/python/mxnet/contrib/svrg_optimization/svrg_module.py
@@ -37,8 +37,7 @@ class SVRGModule(Module):
data_names : list of str
Defaults to `('data')` for a typical model used in image classification.
label_names : list of str
- Defaults to `('softmax_label')` for a typical model used in image
- classification.
+ Defaults to `('softmax_label')` for a typical model used in image classification.
logger : Logger
Defaults to `logging`.
context : Context or list of Context
@@ -48,18 +47,19 @@ class SVRGModule(Module):
fixed_param_names: list of str
Default ``None``, indicating no network parameters are fixed.
state_names : list of str
- states are similar to data and label, but not provided by data iterator.
+ states are similar to data and label, but not provided by data iterator. \
Instead they are initialized to 0 and can be set by `set_states()`.
group2ctxs : dict of str to context or list of context, or list of dict of str to context
Default is `None`. Mapping the `ctx_group` attribute to the context assignment.
compression_params : dict
- Specifies type of gradient compression and additional arguments depending
- on the type of compression being used. For example, 2bit compression requires a threshold.
- Arguments would then be {'type':'2bit', 'threshold':0.5}
- See mxnet.KVStore.set_gradient_compression method for more details on gradient compression.
+ Specifies type of gradient compression and additional arguments depending \
+ on the type of compression being used. For example, 2bit compression requires a threshold. \
+ Arguments would then be {'type':'2bit', 'threshold':0.5} \
+ See mxnet.KVStore.set_gradient_compression method for more details on gradient compression. \
update_freq: int
- Specifies the number of times to update the full gradients to be used in the SVRG optimization. For instance,
+ Specifies the number of times to update the full gradients to be used in the SVRG optimization. For instance, \
update_freq = 2 will calculates the gradients over all data every two epochs
+
Examples
--------
>>> # An example of declaring and using SVRGModule.
diff --git a/python/mxnet/contrib/text/embedding.py b/python/mxnet/contrib/text/embedding.py
index e2a05c841af..b7f3fcbc5c5 100644
--- a/python/mxnet/contrib/text/embedding.py
+++ b/python/mxnet/contrib/text/embedding.py
@@ -475,7 +475,8 @@ class GloVe(_TokenEmbedding):
the resulting representations showcase interesting linear substructures of the word vector
space. (Source from https://nlp.stanford.edu/projects/glove/)
- Reference:
+ References
+ ----------
GloVe: Global Vectors for Word Representation.
Jeffrey Pennington, Richard Socher, and Christopher D. Manning.
@@ -545,7 +546,8 @@ class FastText(_TokenEmbedding):
representations and text classifiers. It works on standard, generic hardware. Models can later
be reduced in size to even fit on mobile devices. (Source from https://fasttext.cc/)
- References:
+ References
+ ----------
Enriching Word Vectors with Subword Information.
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov.
diff --git a/python/mxnet/gluon/data/vision/datasets.py b/python/mxnet/gluon/data/vision/datasets.py
index 0e380c06efa..267aa1e86cf 100644
--- a/python/mxnet/gluon/data/vision/datasets.py
+++ b/python/mxnet/gluon/data/vision/datasets.py
@@ -47,7 +47,7 @@ class MNIST(dataset._DownloadedDataset):
transform : function, default None
A user defined callback that transforms each sample. For example::
- transform=lambda data, label: (data.astype(np.float32)/255, label)
+ transform=lambda data, label: (data.astype(np.float32)/255, label)
"""
def __init__(self, root=os.path.join(base.data_dir(), 'datasets', 'mnist'),
@@ -107,7 +107,7 @@ class FashionMNIST(MNIST):
transform : function, default None
A user defined callback that transforms each sample. For example::
- transform=lambda data, label: (data.astype(np.float32)/255, label)
+ transform=lambda data, label: (data.astype(np.float32)/255, label)
"""
def __init__(self, root=os.path.join(base.data_dir(), 'datasets', 'fashion-mnist'),
@@ -139,7 +139,7 @@ class CIFAR10(dataset._DownloadedDataset):
transform : function, default None
A user defined callback that transforms each sample. For example::
- transform=lambda data, label: (data.astype(np.float32)/255, label)
+ transform=lambda data, label: (data.astype(np.float32)/255, label)
"""
def __init__(self, root=os.path.join(base.data_dir(), 'datasets', 'cifar10'),
@@ -203,7 +203,7 @@ class CIFAR100(CIFAR10):
transform : function, default None
A user defined callback that transforms each sample. For example::
- transform=lambda data, label: (data.astype(np.float32)/255, label)
+ transform=lambda data, label: (data.astype(np.float32)/255, label)
"""
def __init__(self, root=os.path.join(base.data_dir(), 'datasets', 'cifar100'),
@@ -234,13 +234,12 @@ class ImageRecordDataset(dataset.RecordFileDataset):
filename : str
Path to rec file.
flag : {0, 1}, default 1
- If 0, always convert images to greyscale.
-
+ If 0, always convert images to greyscale. \
If 1, always convert images to colored (RGB).
transform : function, default None
A user defined callback that transforms each sample. For example::
- transform=lambda data, label: (data.astype(np.float32)/255, label)
+ transform=lambda data, label: (data.astype(np.float32)/255, label)
"""
def __init__(self, filename, flag=1, transform=None):
@@ -257,7 +256,9 @@ def __getitem__(self, idx):
class ImageFolderDataset(dataset.Dataset):
- """A dataset for loading image files stored in a folder structure like::
+ """A dataset for loading image files stored in a folder structure.
+
+ like::
root/car/0001.jpg
root/car/xxxa.jpg
@@ -276,7 +277,7 @@ class ImageFolderDataset(dataset.Dataset):
transform : callable, default None
A function that takes data and label and transforms them::
- transform = lambda data, label: (data.astype(np.float32)/255, label)
+ transform = lambda data, label: (data.astype(np.float32)/255, label)
Attributes
----------
diff --git a/python/mxnet/image/image.py b/python/mxnet/image/image.py
index 5c4f01e2fd2..c9a457f5b7e 100644
--- a/python/mxnet/image/image.py
+++ b/python/mxnet/image/image.py
@@ -1322,8 +1322,8 @@ def locate():
def read_image(self, fname):
"""Reads an input image `fname` and returns the decoded raw bytes.
- Example usage:
- ----------
+ Examples
+ --------
>>> dataIter.read_image('Face.jpg') # returns decoded raw bytes.
"""
with open(os.path.join(self.path_root, fname), 'rb') as fin:
diff --git a/python/mxnet/optimizer/optimizer.py b/python/mxnet/optimizer/optimizer.py
index d632a8c7c64..442a11d0220 100644
--- a/python/mxnet/optimizer/optimizer.py
+++ b/python/mxnet/optimizer/optimizer.py
@@ -564,7 +564,8 @@ class Signum(Optimizer):
state = momentum * state + (1-momentum)*rescaled_grad
weight = (1 - lr * wd_lh) * weight - lr * sign(state)
- Reference:
+ References
+ ----------
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli & Anima Anandkumar. (2018).
signSGD: Compressed Optimisation for Non-Convex Problems. In ICML'18.
diff --git a/python/mxnet/rnn/rnn_cell.py b/python/mxnet/rnn/rnn_cell.py
index 3f8a459e40d..6738aa19e9c 100644
--- a/python/mxnet/rnn/rnn_cell.py
+++ b/python/mxnet/rnn/rnn_cell.py
@@ -1253,7 +1253,8 @@ def state_info(self):
class ConvLSTMCell(BaseConvRNNCell):
"""Convolutional LSTM network cell.
- Reference:
+ References
+ ----------
Xingjian et al. NIPS2015
Parameters
diff --git a/python/mxnet/symbol/contrib.py b/python/mxnet/symbol/contrib.py
index b551422320c..d54267fc02f 100644
--- a/python/mxnet/symbol/contrib.py
+++ b/python/mxnet/symbol/contrib.py
@@ -236,15 +236,15 @@ def foreach(body, data, init_states, name="foreach"):
output data, the body function can return (out, []).
The computation done by this operator is equivalent to the pseudo code below
- when the input data is NDArray:
-
- states = init_states
- outs = []
- for i in data.shape[0]:
- s = data[i]
- out, states = body(s, states)
- outs.append(out)
- outs = stack(*outs)
+ when the input data is NDArray::
+
+ states = init_states
+ outs = []
+ for i in data.shape[0]:
+ s = data[i]
+ out, states = body(s, states)
+ outs.append(out)
+ outs = stack(*outs)
Parameters
diff --git a/python/mxnet/torch.py b/python/mxnet/torch.py
index dde1eed0893..fdf8654a488 100644
--- a/python/mxnet/torch.py
+++ b/python/mxnet/torch.py
@@ -85,7 +85,7 @@ def _make_torch_function(handle):
'Invoke with\n{res}= mxnet.th.{name}(Parameters)\nor\n'+
'mxnet.th.{name}({res}, Parameters).\n\n' +
'{param_str}\n' +
- 'Reference: ' +
+ 'References: ' +
'https://github.com/torch/torch7/blob/master/doc/maths.md\n').format(
name=func_name[4:], param_str=param_str,
res=res))
diff --git a/src/operator/contrib/adaptive_avg_pooling.cc b/src/operator/contrib/adaptive_avg_pooling.cc
index 91ea6d8bee8..720cf984486 100644
--- a/src/operator/contrib/adaptive_avg_pooling.cc
+++ b/src/operator/contrib/adaptive_avg_pooling.cc
@@ -205,10 +205,10 @@ NNVM_REGISTER_OP(_contrib_AdaptiveAvgPooling2D)
Applies a 2D adaptive average pooling over a 4D input with the shape of (NCHW).
The pooling kernel and stride sizes are automatically chosen for desired output sizes.
-- If a single integer is provided for output_size, the output size is
+- If a single integer is provided for output_size, the output size is \
(N x C x output_size x output_size) for any input (NCHW).
-- If a tuple of integers (height, width) are provided for output_size, the output size is
+- If a tuple of integers (height, width) are provided for output_size, the output size is \
(N x C x height x width) for any input (NCHW).
)code" ADD_FILELINE)
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