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[Docs] Fix the format of the docstring (#1573)

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* [Docs] Fix the format of docstring

* fix format
This commit is contained in:
Zaida Zhou 2021-12-09 22:15:52 +08:00 committed by GitHub
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commit 222f38075b
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21 changed files with 136 additions and 138 deletions
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1
mmcv/cnn/bricks/activation.py
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@ -83,6 +83,7 @@ def build_activation_layer(cfg):
Args:
cfg (dict): The activation layer config, which should contain:
- type (str): Layer type.
- layer args: Args needed to instantiate an activation layer.

6
mmcv/cnn/bricks/norm.py
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@ -83,9 +83,9 @@ def build_norm_layer(cfg, num_features, postfix=''):
to create named layer.
Returns:
(str, nn.Module): The first element is the layer name consisting of
abbreviation and postfix, e.g., bn1, gn. The second element is the
created norm layer.
tuple[str, nn.Module]: The first element is the layer name consisting
of abbreviation and postfix, e.g., bn1, gn. The second element is the
created norm layer.
"""
if not isinstance(cfg, dict):
raise TypeError('cfg must be a dict')

10
mmcv/cnn/bricks/plugin.py
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@ -57,15 +57,15 @@ def build_plugin_layer(cfg, postfix='', **kwargs):
Args:
cfg (None or dict): cfg should contain:
type (str): identify plugin layer type.
layer args: args needed to instantiate a plugin layer.
- type (str): identify plugin layer type.
- layer args: args needed to instantiate a plugin layer.
postfix (int, str): appended into norm abbreviation to
create named layer. Default: ''.
Returns:
tuple[str, nn.Module]:
name (str): abbreviation + postfix
layer (nn.Module): created plugin layer
tuple[str, nn.Module]: The first one is the concatenation of
abbreviation and postfix. The second is the created plugin layer.
"""
if not isinstance(cfg, dict):
raise TypeError('cfg must be a dict')

2
mmcv/cnn/bricks/upsample.py
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@ -55,7 +55,7 @@ def build_upsample_layer(cfg, *args, **kwargs):
- type (str): Layer type.
- scale_factor (int): Upsample ratio, which is not applicable to
deconv.
deconv.
- layer args: Args needed to instantiate a upsample layer.
args (argument list): Arguments passed to the ``__init__``
method of the corresponding conv layer.

20
mmcv/cnn/utils/flops_counter.py
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@ -48,16 +48,16 @@ def get_model_complexity_info(model,
Supported layers are listed as below:
- Convolutions: ``nn.Conv1d``, ``nn.Conv2d``, ``nn.Conv3d``.
- Activations: ``nn.ReLU``, ``nn.PReLU``, ``nn.ELU``, ``nn.LeakyReLU``,
``nn.ReLU6``.
- Activations: ``nn.ReLU``, ``nn.PReLU``, ``nn.ELU``,
``nn.LeakyReLU``, ``nn.ReLU6``.
- Poolings: ``nn.MaxPool1d``, ``nn.MaxPool2d``, ``nn.MaxPool3d``,
``nn.AvgPool1d``, ``nn.AvgPool2d``, ``nn.AvgPool3d``,
``nn.AdaptiveMaxPool1d``, ``nn.AdaptiveMaxPool2d``,
``nn.AdaptiveMaxPool3d``, ``nn.AdaptiveAvgPool1d``,
``nn.AdaptiveAvgPool2d``, ``nn.AdaptiveAvgPool3d``.
``nn.AvgPool1d``, ``nn.AvgPool2d``, ``nn.AvgPool3d``,
``nn.AdaptiveMaxPool1d``, ``nn.AdaptiveMaxPool2d``,
``nn.AdaptiveMaxPool3d``, ``nn.AdaptiveAvgPool1d``,
``nn.AdaptiveAvgPool2d``, ``nn.AdaptiveAvgPool3d``.
- BatchNorms: ``nn.BatchNorm1d``, ``nn.BatchNorm2d``,
``nn.BatchNorm3d``, ``nn.GroupNorm``, ``nn.InstanceNorm1d``,
``InstanceNorm2d``, ``InstanceNorm3d``, ``nn.LayerNorm``.
``nn.BatchNorm3d``, ``nn.GroupNorm``, ``nn.InstanceNorm1d``,
``InstanceNorm2d``, ``InstanceNorm3d``, ``nn.LayerNorm``.
- Linear: ``nn.Linear``.
- Deconvolution: ``nn.ConvTranspose2d``.
- Upsample: ``nn.Upsample``.
@ -78,8 +78,8 @@ def get_model_complexity_info(model,
Returns:
tuple[float | str]: If ``as_strings`` is set to True, it will return
FLOPs and parameter counts in a string format. otherwise, it will
return those in a float number format.
FLOPs and parameter counts in a string format. otherwise, it will
return those in a float number format.
"""
assert type(input_shape) is tuple
assert len(input_shape) >= 1

2
mmcv/fileio/file_client.py
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@ -1072,7 +1072,7 @@ class FileClient:
Returns:
bool: Return ``True`` if ``filepath`` points to a file, ``False``
otherwise.
otherwise.
"""
return self.client.isfile(filepath)

8
mmcv/image/colorspace.py
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@ -160,7 +160,7 @@ def rgb2ycbcr(img, y_only=False):
Returns:
ndarray: The converted YCbCr image. The output image has the same type
and range as input image.
and range as input image.
"""
img_type = img.dtype
img = _convert_input_type_range(img)
@ -194,7 +194,7 @@ def bgr2ycbcr(img, y_only=False):
Returns:
ndarray: The converted YCbCr image. The output image has the same type
and range as input image.
and range as input image.
"""
img_type = img.dtype
img = _convert_input_type_range(img)
@ -227,7 +227,7 @@ def ycbcr2rgb(img):
Returns:
ndarray: The converted RGB image. The output image has the same type
and range as input image.
and range as input image.
"""
img_type = img.dtype
img = _convert_input_type_range(img) * 255
@ -259,7 +259,7 @@ def ycbcr2bgr(img):
Returns:
ndarray: The converted BGR image. The output image has the same type
and range as input image.
and range as input image.
"""
img_type = img.dtype
img = _convert_input_type_range(img) * 255

24
mmcv/image/geometric.py
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@ -70,7 +70,7 @@ def imresize(img,
Returns:
tuple | ndarray: (`resized_img`, `w_scale`, `h_scale`) or
`resized_img`.
`resized_img`.
"""
h, w = img.shape[:2]
if backend is None:
@ -130,7 +130,7 @@ def imresize_to_multiple(img,
Returns:
tuple | ndarray: (`resized_img`, `w_scale`, `h_scale`) or
`resized_img`.
`resized_img`.
"""
h, w = img.shape[:2]
if size is not None and scale_factor is not None:
@ -175,7 +175,7 @@ def imresize_like(img,
Returns:
tuple or ndarray: (`resized_img`, `w_scale`, `h_scale`) or
`resized_img`.
`resized_img`.
"""
h, w = dst_img.shape[:2]
return imresize(img, (w, h), return_scale, interpolation, backend=backend)
@ -462,16 +462,16 @@ def impad(img,
reflect or symmetric. Default: constant.
- constant: pads with a constant value, this value is specified
with pad_val.
with pad_val.
- edge: pads with the last value at the edge of the image.
- reflect: pads with reflection of image without repeating the
last value on the edge. For example, padding [1, 2, 3, 4]
with 2 elements on both sides in reflect mode will result
in [3, 2, 1, 2, 3, 4, 3, 2].
- symmetric: pads with reflection of image repeating the last
value on the edge. For example, padding [1, 2, 3, 4] with
2 elements on both sides in symmetric mode will result in
[2, 1, 1, 2, 3, 4, 4, 3]
- reflect: pads with reflection of image without repeating the last
value on the edge. For example, padding [1, 2, 3, 4] with 2
elements on both sides in reflect mode will result in
[3, 2, 1, 2, 3, 4, 3, 2].
- symmetric: pads with reflection of image repeating the last value
on the edge. For example, padding [1, 2, 3, 4] with 2 elements on
both sides in symmetric mode will result in
[2, 1, 1, 2, 3, 4, 4, 3]
Returns:
ndarray: The padded image.

2
mmcv/ops/group_points.py
View File

@ -76,7 +76,7 @@ class QueryAndGroup(nn.Module):
Returns:
torch.Tensor: (B, 3 + C, npoint, sample_num) Grouped
concatenated coordinates and features of points.
concatenated coordinates and features of points.
"""
# if self.max_radius is None, we will perform kNN instead of ball query
# idx is of shape [B, npoint, sample_num]

10
mmcv/ops/nms.py
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@ -277,11 +277,11 @@ def batched_nms(boxes, scores, idxs, nms_cfg, class_agnostic=False):
- iou_thr (float): IoU threshold used for NMS.
- split_thr (float): threshold number of boxes. In some cases the
number of boxes is large (e.g., 200k). To avoid OOM during
training, the users could set `split_thr` to a small value.
If the number of boxes is greater than the threshold, it will
perform NMS on each group of boxes separately and sequentially.
Defaults to 10000.
number of boxes is large (e.g., 200k). To avoid OOM during
training, the users could set `split_thr` to a small value.
If the number of boxes is greater than the threshold, it will
perform NMS on each group of boxes separately and sequentially.
Defaults to 10000.
class_agnostic (bool): if true, nms is class agnostic,
i.e. IoU thresholding happens over all boxes,
regardless of the predicted class.

27
mmcv/ops/scatter_points.py
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@ -25,11 +25,10 @@ class _DynamicScatter(Function):
'mean'. Default: 'max'.
Returns:
tuple[torch.Tensor]: tuple[torch.Tensor]: A tuple contains two
elements. The first one is the voxel features with shape [M, C]
which are respectively reduced from input features that share
the same voxel coordinates . The second is voxel coordinates
with shape [M, ndim].
tuple[torch.Tensor]: A tuple contains two elements. The first one
is the voxel features with shape [M, C] which are respectively
reduced from input features that share the same voxel coordinates.
The second is voxel coordinates with shape [M, ndim].
"""
results = ext_module.dynamic_point_to_voxel_forward(
feats, coors, reduce_type)
@ -89,11 +88,10 @@ class DynamicScatter(nn.Module):
multi-dim voxel index) of each points.
Returns:
tuple[torch.Tensor]: tuple[torch.Tensor]: A tuple contains two
elements. The first one is the voxel features with shape [M, C]
which are respectively reduced from input features that share
the same voxel coordinates . The second is voxel coordinates
with shape [M, ndim].
tuple[torch.Tensor]: A tuple contains two elements. The first one
is the voxel features with shape [M, C] which are respectively
reduced from input features that share the same voxel coordinates.
The second is voxel coordinates with shape [M, ndim].
"""
reduce = 'mean' if self.average_points else 'max'
return dynamic_scatter(points.contiguous(), coors.contiguous(), reduce)
@ -107,11 +105,10 @@ class DynamicScatter(nn.Module):
multi-dim voxel index) of each points.
Returns:
tuple[torch.Tensor]:tuple[torch.Tensor]: A tuple contains two
elements. The first one is the voxel features with shape [M, C]
which are respectively reduced from input features that share
the same voxel coordinates . The second is voxel coordinates
with shape [M, ndim].
tuple[torch.Tensor]: A tuple contains two elements. The first one
is the voxel features with shape [M, C] which are respectively
reduced from input features that share the same voxel coordinates.
The second is voxel coordinates with shape [M, ndim].
"""
if coors.size(-1) == 3:
return self.forward_single(points, coors)

13
mmcv/runner/base_module.py
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@ -18,13 +18,12 @@ class BaseModule(nn.Module, metaclass=ABCMeta):
functionality of parameter initialization. Compared with
``torch.nn.Module``, ``BaseModule`` mainly adds three attributes.
- ``init_cfg``: the config to control the initialization.
- ``init_weights``: The function of parameter
initialization and recording initialization
information.
- ``_params_init_info``: Used to track the parameter
initialization information. This attribute only
exists during executing the ``init_weights``.
- ``init_cfg``: the config to control the initialization.
- ``init_weights``: The function of parameter initialization and recording
initialization information.
- ``_params_init_info``: Used to track the parameter initialization
information. This attribute only exists during executing the
``init_weights``.
Args:
init_cfg (dict, optional): Initialization config dict.

10
mmcv/runner/base_runner.py
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@ -207,8 +207,8 @@ class BaseRunner(metaclass=ABCMeta):
Returns:
list[float] | dict[str, list[float]]: Current learning rates of all
param groups. If the runner has a dict of optimizers, this
method will return a dict.
param groups. If the runner has a dict of optimizers, this method
will return a dict.
"""
if isinstance(self.optimizer, torch.optim.Optimizer):
lr = [group['lr'] for group in self.optimizer.param_groups]
@ -226,8 +226,8 @@ class BaseRunner(metaclass=ABCMeta):
Returns:
list[float] | dict[str, list[float]]: Current momentums of all
param groups. If the runner has a dict of optimizers, this
method will return a dict.
param groups. If the runner has a dict of optimizers, this method
will return a dict.
"""
def _get_momentum(optimizer):
@ -287,7 +287,7 @@ class BaseRunner(metaclass=ABCMeta):
hook_cfg (dict): Hook config. It should have at least keys 'type'
and 'priority' indicating its type and priority.
Notes:
Note:
The specific hook class to register should not use 'type' and
'priority' arguments during initialization.
"""

4
mmcv/runner/hooks/ema.py
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@ -13,8 +13,8 @@ class EMAHook(Hook):
.. math::
\text{Xema\_{t+1}} = (1 - \text{momentum}) \times
\text{Xema\_{t}} + \text{momentum} \times X_t
Xema\_{t+1} = (1 - \text{momentum}) \times
Xema\_{t} + \text{momentum} \times X_t
Args:
momentum (float): The momentum used for updating ema parameter.

26
mmcv/runner/hooks/logger/neptune.py
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@ -12,19 +12,21 @@ class NeptuneLoggerHook(LoggerHook):
Args:
init_kwargs (dict): a dict contains the initialization keys as below:
- project (str): Name of a project in a form of
namespace/project_name. If None, the value of
NEPTUNE_PROJECT environment variable will be taken.
- api_token (str): User’s API token.
If None, the value of NEPTUNE_API_TOKEN environment
variable will be taken. Note: It is strongly recommended
to use NEPTUNE_API_TOKEN environment variable rather than
placing your API token in plain text in your source code.
- name (str, optional, default is 'Untitled'): Editable name of
the run. Name is displayed in the run's Details and in
Runs table as a column.
Check https://docs.neptune.ai/api-reference/neptune#init for
more init arguments.
namespace/project_name. If None, the value of NEPTUNE_PROJECT
environment variable will be taken.
- api_token (str): User’s API token. If None, the value of
NEPTUNE_API_TOKEN environment variable will be taken. Note: It is
strongly recommended to use NEPTUNE_API_TOKEN environment
variable rather than placing your API token in plain text in your
source code.
- name (str, optional, default is 'Untitled'): Editable name of the
run. Name is displayed in the run's Details and in Runs table as
a column.
Check https://docs.neptune.ai/api-reference/neptune#init for more
init arguments.
interval (int): Logging interval (every k iterations).
ignore_last (bool): Ignore the log of last iterations in each epoch
if less than `interval`.

4
mmcv/utils/config.py
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@ -344,7 +344,7 @@ class Config:
config str. Only py/yml/yaml/json type are supported now!
Returns:
obj:`Config`: Config obj.
:obj:`Config`: Config obj.
"""
if file_format not in ['.py', '.json', '.yaml', '.yml']:
raise IOError('Only py/yml/yaml/json type are supported now!')
@ -561,7 +561,7 @@ class Config:
>>> assert cfg_dict == dict(
... model=dict(backbone=dict(depth=50, with_cp=True)))
# Merge list element
>>> # Merge list element
>>> cfg = Config(dict(pipeline=[
... dict(type='LoadImage'), dict(type='LoadAnnotations')]))
>>> options = dict(pipeline={'0': dict(type='SelfLoadImage')})

2
mmcv/utils/path.py
View File

@ -40,7 +40,7 @@ def scandir(dir_path, suffix=None, recursive=False, case_sensitive=True):
"""Scan a directory to find the interested files.
Args:
dir_path (str | obj:`Path`): Path of the directory.
dir_path (str | :obj:`Path`): Path of the directory.
suffix (str | tuple(str), optional): File suffix that we are
interested in. Default: None.
recursive (bool, optional): If set to True, recursively scan the

10
mmcv/utils/registry.py
View File

@ -59,6 +59,7 @@ class Registry:
"""A registry to map strings to classes.
Registered object could be built from registry.
Example:
>>> MODELS = Registry('models')
>>> @MODELS.register_module()
@ -128,16 +129,15 @@ class Registry:
The name of the package where registry is defined will be returned.
Example:
# in mmdet/models/backbone/resnet.py
>>> # in mmdet/models/backbone/resnet.py
>>> MODELS = Registry('models')
>>> @MODELS.register_module()
>>> class ResNet:
>>> pass
The scope of ``ResNet`` will be ``mmdet``.
Returns:
scope (str): The inferred scope name.
str: The inferred scope name.
"""
# inspect.stack() trace where this function is called, the index-2
# indicates the frame where `infer_scope()` is called
@ -158,8 +158,8 @@ class Registry:
None, 'ResNet'
Return:
scope (str, None): The first scope.
key (str): The remaining key.
tuple[str | None, str]: The former element is the first scope of
the key, which can be ``None``. The latter is the remaining key.
"""
split_index = key.find('.')
if split_index != -1:

72
mmcv/utils/timer.py
View File

@ -12,27 +12,26 @@ class TimerError(Exception):
class Timer:
"""A flexible Timer class.
:Example:
>>> import time
>>> import mmcv
>>> with mmcv.Timer():
>>> # simulate a code block that will run for 1s
>>> time.sleep(1)
1.000
>>> with mmcv.Timer(print_tmpl='it takes {:.1f} seconds'):
>>> # simulate a code block that will run for 1s
>>> time.sleep(1)
it takes 1.0 seconds
>>> timer = mmcv.Timer()
>>> time.sleep(0.5)
>>> print(timer.since_start())
0.500
>>> time.sleep(0.5)
>>> print(timer.since_last_check())
0.500
>>> print(timer.since_start())
1.000
Examples:
>>> import time
>>> import mmcv
>>> with mmcv.Timer():
>>> # simulate a code block that will run for 1s
>>> time.sleep(1)
1.000
>>> with mmcv.Timer(print_tmpl='it takes {:.1f} seconds'):
>>> # simulate a code block that will run for 1s
>>> time.sleep(1)
it takes 1.0 seconds
>>> timer = mmcv.Timer()
>>> time.sleep(0.5)
>>> print(timer.since_start())
0.500
>>> time.sleep(0.5)
>>> print(timer.since_last_check())
0.500
>>> print(timer.since_start())
1.000
"""
def __init__(self, start=True, print_tmpl=None):
@ -64,7 +63,8 @@ class Timer:
def since_start(self):
"""Total time since the timer is started.
Returns (float): Time in seconds.
Returns:
float: Time in seconds.
"""
if not self._is_running:
raise TimerError('timer is not running')
@ -77,7 +77,8 @@ class Timer:
Either :func:`since_start` or :func:`since_last_check` is a checking
operation.
Returns (float): Time in seconds.
Returns:
float: Time in seconds.
"""
if not self._is_running:
raise TimerError('timer is not running')
@ -95,21 +96,20 @@ def check_time(timer_id):
This method is suitable for running a task on a list of items. A timer will
be registered when the method is called for the first time.
:Example:
>>> import time
>>> import mmcv
>>> for i in range(1, 6):
>>> # simulate a code block
>>> time.sleep(i)
>>> mmcv.check_time('task1')
2.000
3.000
4.000
5.000
Examples:
>>> import time
>>> import mmcv
>>> for i in range(1, 6):
>>> # simulate a code block
>>> time.sleep(i)
>>> mmcv.check_time('task1')
2.000
3.000
4.000
5.000
Args:
timer_id (str): Timer identifier.
str: Timer identifier.
"""
if timer_id not in _g_timers:
_g_timers[timer_id] = Timer()

19
mmcv/video/io.py
View File

@ -50,15 +50,14 @@ class VideoReader:
the second time, there is no need to decode again if it is stored in the
cache.
:Example:
>>> import mmcv
>>> v = mmcv.VideoReader('sample.mp4')
>>> len(v) # get the total frame number with `len()`
120
>>> for img in v: # v is iterable
>>> mmcv.imshow(img)
>>> v[5] # get the 6th frame
Examples:
>>> import mmcv
>>> v = mmcv.VideoReader('sample.mp4')
>>> len(v) # get the total frame number with `len()`
120
>>> for img in v: # v is iterable
>>> mmcv.imshow(img)
>>> v[5] # get the 6th frame
"""
def __init__(self, filename, cache_capacity=10):
@ -189,7 +188,7 @@ class VideoReader:
Returns:
ndarray or None: If the video is fresh, return None, otherwise
return the frame.
return the frame.
"""
if self._position == 0:
return None

2
mmcv/video/optflow.py
View File

@ -242,7 +242,7 @@ def sparse_flow_from_bytes(content):
Returns:
Tuple(ndarray, ndarray): Loaded optical flow with the shape (H, W, 2)
and flow valid mask with the shape (H, W).
and flow valid mask with the shape (H, W).
""" # nopa
content = np.frombuffer(content, np.uint8)