Upd comments style

chainer_/chainercv2/model_provider.py

@@ -1043,7 +1043,7 @@ def get_model(name, **kwargs):
     name : str
         Name of model.
 
-    Returns
+    Returns:
     -------
     Chain
         Resulted model.

chainer_/chainercv2/models/centernet.py

@@ -133,7 +133,7 @@ class CenterNetHeatmapMaxDet(Chain):
     """
     CenterNet decoder for heads (heatmap, wh, reg).
 
-    Parameters
+    Parameters:
     ----------
     topk : int, default 40
         Keep only `topk` detections.
@@ -190,7 +190,7 @@ class CenterNet(Chain):
     """
     CenterNet model from 'Objects as Points,' https://arxiv.org/abs/1904.07850.
 
-    Parameters
+    Parameters:
     ----------
     backbone : nn.Sequential
         Feature extractor.
@@ -273,7 +273,7 @@ def get_centernet(backbone,
     """
     Create CenterNet model with specific parameters.
 
-    Parameters
+    Parameters:
     ----------
     backbone : nn.Sequential
         Feature extractor.
@@ -288,7 +288,7 @@ def get_centernet(backbone,
     root : str, default '~/.chainer/models'
         Location for keeping the model parameters.
 
-    Returns
+    Returns:
     -------
     HybridBlock
         A network.

chainer_/chainercv2/models/common.py

@@ -34,7 +34,7 @@ def round_channels(channels,
     divisor : int, default 8
         Alignment value.
 
-    Returns
+    Returns:
     -------
     int
         Weighted number of channels.
@@ -87,7 +87,7 @@ def get_activation_layer(activation):
     activation : function or str
         Activation function or name of activation function.
 
-    Returns
+    Returns:
     -------
     function
         Activation layer.
@@ -1150,7 +1150,7 @@ def channel_shuffle(x,
     groups : int
         Number of groups.
 
-    Returns
+    Returns:
     -------
     chainer.Variable or numpy.ndarray or cupy.ndarray
         Resulted variable.
@@ -1198,7 +1198,7 @@ def channel_shuffle2(x,
     groups : int
         Number of groups.
 
-    Returns
+    Returns:
     -------
     chainer.Variable or numpy.ndarray or cupy.ndarray
         Resulted variable.

chainer_/chainercv2/models/efficientnet.py

@@ -41,7 +41,7 @@ def calc_tf_padding(x,
     dilation : int, default 1
         Dilation value for convolution layer.
 
-    Returns
+    Returns:
     -------
     tuple of 4 int
         The size of the padding.

chainer_/chainercv2/models/fishnet.py

@@ -29,7 +29,7 @@ def channel_squeeze(x,
     groups : int
         Number of groups.
 
-    Returns
+    Returns:
     -------
     chainer.Variable or numpy.ndarray or cupy.ndarray
         Resulted variable.

chainer_/chainercv2/models/model_store.py

@@ -582,14 +582,14 @@ def get_model_file(model_name,
     Return location for the pretrained on local file system. This function will download from online model zoo when
     model cannot be found or has mismatch. The root directory will be created if it doesn't exist.
 
-    Parameters
+    Parameters:
     ----------
     model_name : str
         Name of the model.
     local_model_store_dir_path : str, default $CHAINER_HOME/models
         Location for keeping the model parameters.
 
-    Returns
+    Returns:
     -------
     file_path
         Path to the requested pretrained model file.
@@ -634,7 +634,7 @@ def get_model_file(model_name,
 def _download(url, path=None, overwrite=False, sha1_hash=None, retries=5, verify_ssl=True):
     """Download an given URL
 
-    Parameters
+    Parameters:
     ----------
     url : str
         URL to download
@@ -651,7 +651,7 @@ def _download(url, path=None, overwrite=False, sha1_hash=None, retries=5, verify
     verify_ssl : bool, default True
         Verify SSL certificates.
 
-    Returns
+    Returns:
     -------
     str
         The file path of the downloaded file.
@@ -717,14 +717,14 @@ class requests_failed_to_import(object):
 def _check_sha1(filename, sha1_hash):
     """Check whether the sha1 hash of the file content matches the expected hash.
 
-    Parameters
+    Parameters:
     ----------
     filename : str
         Path to the file.
     sha1_hash : str
         Expected sha1 hash in hexadecimal digits.
 
-    Returns
+    Returns:
     -------
     bool
         Whether the file content matches the expected hash.

chainer_/chainercv2/models/nasnet.py

@@ -42,7 +42,7 @@ def __init__(self,
     x_prev : Tensor
         Previous processed tensor.
 
-    Returns
+    Returns:
     -------
     x_next : Tensor
         Next processed tensor.
@@ -76,7 +76,7 @@ def nasnet_dual_path_scheme_ordinal(block,
     x : Tensor
         Current processed tensor.
 
-    Returns
+    Returns:
     -------
     x_next : Tensor
         Next processed tensor.
@@ -180,7 +180,7 @@ def process_with_padding(x,
     pad_width : tuple of tuple of int, default ((0, 0), (0, 0), (1, 0), (1, 0))
         Whether the layer uses a bias vector.
 
-    Returns
+    Returns:
     -------
     chainer.Variable or numpy.ndarray or cupy.ndarray
         Resulted tensor.

chainer_/chainercv2/models/ntsnet_cub.py

@@ -31,7 +31,7 @@ def hard_nms(cdds,
     iou_thresh : float, default 0.25
         IoU threshold.
 
-    Returns
+    Returns:
     -------
     np.array
         Filtered borders.
@@ -252,7 +252,7 @@ def _generate_default_anchor_maps(input_shape=(448, 448)):
         input_shape : tuple of 2 int
             Input image size.
 
-        Returns
+        Returns:
         -------
         center_anchors : np.array
             anchors * 4 (oy, ox, h, w).

chainer_/chainercv2/models/sparsenet.py

@@ -26,7 +26,7 @@ def sparsenet_exponential_fetch(lst):
     lst : list
         List of something.
 
-    Returns
+    Returns:
     -------
     list
         Filtered list.

chainer_/chainercv2/models/squeezenet.py

@@ -326,7 +326,7 @@ def squeezeresnet_v1_0(**kwargs):
 
 def squeezeresnet_v1_1(**kwargs):
     """
-    SqueezeNet v1.1 model with residual connections from 'SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
+    SqueezeNet v1.1 model with residual connections from 'SqueezeNet: AlexNet-level accuracy with 50x fewer Parameters:
     and <0.5MB model size,' https://arxiv.org/abs/1602.07360.
 
     Parameters:

chainer_/dataset_utils.py

@@ -23,12 +23,12 @@ def get_dataset_metainfo(dataset_name):
     """
     Get dataset metainfo by name of dataset.
 
-    Parameters
+    Parameters:
     ----------
     dataset_name : str
         Dataset name.
 
-    Returns
+    Returns:
     -------
     DatasetMetaInfo
         Dataset metainfo.

chainer_/datasets/ade20k_seg_dataset.py

@@ -9,7 +9,7 @@ class ADE20KSegDataset(SegDataset):
     """
     ADE20K semantic segmentation dataset.
 
-    Parameters
+    Parameters:
     ----------
     root : str
         Path to a folder with `ADEChallengeData2016` subfolder.

chainer_/datasets/cifar100_cls_dataset.py

@@ -13,7 +13,7 @@ class CIFAR100(DatasetMixin):
     CIFAR-100 image classification dataset.
 
 
-    Parameters
+    Parameters:
     ----------
     root : str, default '~/.chainer/datasets/cifar100'
         Path to temp folder for storing data.

chainer_/datasets/cifar10_cls_dataset.py

@@ -16,7 +16,7 @@ class CIFAR10(DatasetMixin):
     CIFAR-10 image classification dataset.
 
 
-    Parameters
+    Parameters:
     ----------
     root : str, default '~/.chainer/datasets/cifar10'
         Path to temp folder for storing data.

chainer_/datasets/cityscapes_seg_dataset.py

@@ -9,7 +9,7 @@ class CityscapesSegDataset(SegDataset):
     """
     Cityscapes semantic segmentation dataset.
 
-    Parameters
+    Parameters:
     ----------
     root : str
         Path to a folder with `leftImg8bit` and `gtFine` subfolders.

chainer_/datasets/coco_hpe1_dataset.py

@@ -14,7 +14,7 @@ class CocoHpe1Dataset(GetterDataset):
     """
     COCO keypoint detection (2D single human pose estimation) dataset.
 
-    Parameters
+    Parameters:
     ----------
     root : string
         Path to `annotations`, `train2017`, and `val2017` folders.
@@ -299,7 +299,7 @@ def bbox_clip_xyxy(xyxy, width, height):
 
         All bounding boxes will be clipped to the new region `(0, 0, width, height)`.
 
-        Parameters
+        Parameters:
         ----------
         xyxy : list, tuple or numpy.ndarray
             The bbox in format (xmin, ymin, xmax, ymax).
@@ -310,7 +310,7 @@ def bbox_clip_xyxy(xyxy, width, height):
         height : int or float
             Boundary height.
 
-        Returns
+        Returns:
         -------
         tuple or np.array
             Description of returned object.
@@ -339,14 +339,14 @@ def bbox_xywh_to_xyxy(xywh):
         """
         Convert bounding boxes from format (xmin, ymin, w, h) to (xmin, ymin, xmax, ymax)
 
-        Parameters
+        Parameters:
         ----------
         xywh : list, tuple or numpy.ndarray
             The bbox in format (x, y, w, h).
             If numpy.ndarray is provided, we expect multiple bounding boxes with
             shape `(N, 4)`.
 
-        Returns
+        Returns:
         -------
         tuple or np.ndarray
             The converted bboxes in format (xmin, ymin, xmax, ymax).

chainer_/datasets/coco_hpe2_dataset.py

@@ -16,7 +16,7 @@ class CocoHpe2Dataset(GetterDataset):
     """
     COCO keypoint detection (2D multiple human pose estimation) dataset.
 
-    Parameters
+    Parameters:
     ----------
     root : string
         Path to `annotations`, `train2017`, and `val2017` folders.

chainer_/datasets/coco_hpe3_dataset.py

@@ -14,7 +14,7 @@ class CocoHpe3Dataset(GetterDataset):
     """
     COCO keypoint detection (2D multiple human pose estimation) dataset.
 
-    Parameters
+    Parameters:
     ----------
     root : string
         Path to `annotations`, `train2017`, and `val2017` folders.

chainer_/datasets/coco_seg_dataset.py

@@ -15,7 +15,7 @@ class CocoSegDataset(SegDataset):
     """
     COCO semantic segmentation dataset.
 
-    Parameters
+    Parameters:
     ----------
     root: str
         Path to `annotations`, `train2017`, and `val2017` folders.

chainer_/datasets/cub200_2011_cls_dataset.py

@@ -14,7 +14,7 @@ class CUB200_2011(GetterDataset):
     """
     CUB-200-2011 fine-grained classification dataset.
 
-    Parameters
+    Parameters:
     ----------
     root : str, default '~/.chainer/datasets/CUB_200_2011'
         Path to the folder stored the dataset.

chainer_/datasets/imagenet1k_cls_dataset.py

@@ -20,7 +20,7 @@ class ImageNet1K(DatasetMixin):
     """
     ImageNet-1K classification dataset.
 
-    Parameters
+    Parameters:
     ----------
     root : str, default '~/.chainer/datasets/imagenet'
         Path to the folder stored the dataset.

chainer_/datasets/seg_dataset.py

@@ -8,7 +8,7 @@ class SegDataset(GetterDataset):
     """
     Segmentation base dataset.
 
-    Parameters
+    Parameters:
     ----------
     root : str
         Path to data folder.

chainer_/datasets/svhn_cls_dataset.py

@@ -15,7 +15,7 @@ class SVHN(DatasetMixin):
     Note: The SVHN dataset assigns the label `10` to the digit `0`. However, in this Dataset,
     we assign the label `0` to the digit `0`.
 
-    Parameters
+    Parameters:
     ----------
     root : str, default '~/.chainer/datasets/svhn'
         Path to temp folder for storing data.

chainer_/datasets/voc_seg_dataset.py

@@ -14,7 +14,7 @@ class VOCSegDataset(SegDataset):
     """
     Pascal VOC2012 semantic segmentation dataset.
 
-    Parameters
+    Parameters:
     ----------
     root : str
         Path to VOCdevkit folder.