Refine docs and fix format issues (#3349)

* Refine docs and fix format issues

* reformat

* fix mock

* Refine format

* Reorganize tutorials

.dev_scripts/linter.sh

@@ -1,3 +1,3 @@
-yapf -r -i --style .style.yapf mmdet/ configs/ tests/ tools/
+yapf -r -i --style mmdet/ configs/ tests/ tools/
 isort -rc mmdet/ configs/ tests/ tools/
 flake8 .

.github/CONTRIBUTING.md

@@ -27,7 +27,7 @@ We use the following tools for linting and formatting:
 - [yapf](https://github.com/google/yapf): formatter
 - [isort](https://github.com/timothycrosley/isort): sort imports
 
-Style configurations of yapf and isort can be found in [.style.yapf](../.style.yapf) and [.isort.cfg](../.isort.cfg).
+Style configurations of yapf and isort can be found in [setup.cfg](../setup.cfg).
 
 We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`,
  fixes `end-of-files`, sorts `requirments.txt` automatically on every commit.

.style.yapf

@@ -1,4 +0,0 @@
-[style]
-BASED_ON_STYLE = pep8
-BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true
-SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true

docs/conf.py

@@ -39,10 +39,7 @@ extensions = [
 ]
 
 autodoc_mock_imports = [
-    'matplotlib', 'pycocotools', 'terminaltables', 'mmdet.version',
-    'mmdet.ops.corner_pool', 'mmdet.ops.dcn', 'mmdet.ops.masked_conv',
-    'mmdet.ops.nms', 'mmdet.ops.roi_align', 'mmdet.ops.roi_pool',
-    'mmdet.ops.sigmoid_focal_loss', 'mmdet.ops.carafe', 'mmdet.ops.utils'
+    'matplotlib', 'pycocotools', 'terminaltables', 'mmdet.version', 'mmcv.ops'
 ]
 
 # Add any paths that contain templates here, relative to this directory.

docs/index.rst

@@ -8,13 +8,19 @@ Welcome to MMDetection's documentation!
    getting_started.md
    config.md
    model_zoo.md
-   tutorials/finetune.md
-   tutorials/new_dataset.md
-   tutorials/data_pipeline.md
-   tutorials/new_modules.md
    compatibility.md
    changelog.md
    projects.md
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Tutorials
+
+   tutorials/index.rst
+
+.. toctree::
+   :caption: API Reference
+
    api.rst
 
 

docs/tutorials/index.rst

@@ -0,0 +1,7 @@
+.. toctree::
+   :maxdepth: 2
+
+   finetune.md
+   new_dataset.md
+   data_pipeline.md
+   new_modules.md

mmdet/core/anchor/anchor_generator.py

@@ -123,7 +123,7 @@ class AnchorGenerator(object):
         """Generate base anchors.
 
         Returns:
-            list(torch.Tensor): Base anchors of a feature grid in multiple
+            list(torch.Tensor): Base anchors of a feature grid in multiple \
                 feature levels.
         """
         multi_level_base_anchors = []
@@ -155,7 +155,7 @@ class AnchorGenerator(object):
                 related to a single feature grid. Defaults to None.
 
         Returns:
-            torch.Tensor: Anchors in a single-level feature maps
+            torch.Tensor: Anchors in a single-level feature maps.
         """
         w = base_size
         h = base_size
@@ -212,10 +212,10 @@ class AnchorGenerator(object):
             device (str): Device where the anchors will be put on.
 
         Return:
-            list[torch.Tensor]: Anchors in multiple feature levels.
-                The sizes of each tensor should be [N, 4], where
-                N = width * height * num_base_anchors, width and height
-                are the sizes of the corresponding feature lavel,
+            list[torch.Tensor]: Anchors in multiple feature levels. \
+                The sizes of each tensor should be [N, 4], where \
+                N = width * height * num_base_anchors, width and height \
+                are the sizes of the corresponding feature lavel, \
                 num_base_anchors is the number of anchors for that level.
         """
         assert self.num_levels == len(featmap_sizes)
@@ -308,7 +308,7 @@ class AnchorGenerator(object):
                 Defaults to 'cuda'.
 
         Returns:
-            torch.Tensor: The valid flags of each anchor in a single level
+            torch.Tensor: The valid flags of each anchor in a single level \
                 feature map.
         """
         feat_h, feat_w = featmap_size
@@ -433,7 +433,7 @@ class SSDAnchorGenerator(AnchorGenerator):
         """Generate base anchors.
 
         Returns:
-            list(torch.Tensor): Base anchors of a feature grid in multiple
+            list(torch.Tensor): Base anchors of a feature grid in multiple \
                 feature levels.
         """
         multi_level_base_anchors = []
@@ -471,11 +471,12 @@ class SSDAnchorGenerator(AnchorGenerator):
 class LegacyAnchorGenerator(AnchorGenerator):
     """Legacy anchor generator used in MMDetection V1.x.
 
+    Note:
         Difference to the V2.0 anchor generator:
 
         1. The center offset of V1.x anchors are set to be 0.5 rather than 0.
-    2. The width/height are minused by 1 when calculating the anchors' centers
-       and corners to meet the V1.x coordinate system.
+        2. The width/height are minused by 1 when calculating the anchors' \
+            centers and corners to meet the V1.x coordinate system.
         3. The anchors' corners are quantized.
 
     Args:
@@ -523,7 +524,7 @@ class LegacyAnchorGenerator(AnchorGenerator):
         """Generate base anchors of a single level.
 
         Note:
-            The width/height of anchors are minused by 1 when calculating
+            The width/height of anchors are minused by 1 when calculating \
                 the centers and corners to meet the V1.x coordinate system.
 
         Args:

mmdet/core/anchor/utils.py

@@ -31,7 +31,7 @@ def anchor_inside_flags(flat_anchors,
             Defaults to 0.
 
     Returns:
-        torch.Tensor: Flags indicating whether the anchors are inside a
+        torch.Tensor: Flags indicating whether the anchors are inside a \
             valid range.
     """
     img_h, img_w = img_shape[:2]

mmdet/core/bbox/assigners/center_region_assigner.py

@@ -108,7 +108,7 @@ class CenterRegionAssigner(BaseAssigner):
             gt_bboxes (Tensor): Ground truth boxes, shape (k, 4).
 
         Returns:
-            Tensor: The priority of gts so that gts with larger priority is
+            Tensor: The priority of gts so that gts with larger priority is \
               more likely to be assigned. Shape (k, )
         """
         gt_areas = bboxes_area(gt_bboxes)
@@ -119,9 +119,10 @@ class CenterRegionAssigner(BaseAssigner):
     def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None):
         """Assign gt to bboxes.
 
-        This method assigns gts to every bbox (proposal/anchor), each bbox will
-         be assigned with -1, or a semi-positive number. -1 means negative
-         sample, semi-positive number is the index (0-based) of assigned gt.
+        This method assigns gts to every bbox (proposal/anchor), each bbox \
+        will be assigned with -1, or a semi-positive number. -1 means \
+        negative sample, semi-positive number is the index (0-based) of \
+        assigned gt.
 
         Args:
             bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4).
@@ -131,12 +132,13 @@ class CenterRegionAssigner(BaseAssigner):
             gt_labels (tensor, optional): Label of gt_bboxes, shape (num_gts,).
 
         Returns:
-            :obj:`AssignResult`: The assigned result. Note that shadowed_labels
-              of shape (N, 2) is also added as an `assign_result` attribute.
-              `shadowed_labels` is a tensor composed of N pairs of
-              [anchor_ind, class_label], where N is the number of anchors that
-              lie in the outer region of a gt, anchor_ind is the shadowed
-              anchor index and class_label is the shadowed class label.
+            :obj:`AssignResult`: The assigned result. Note that \
+              shadowed_labels of shape (N, 2) is also added as an \
+              `assign_result` attribute. `shadowed_labels` is a tensor \
+              composed of N pairs of anchor_ind, class_label], where N \
+              is the number of anchors that lie in the outer region of a \
+              gt, anchor_ind is the shadowed anchor index and class_label \
+              is the shadowed class label.
 
         Example:
             >>> self = CenterRegionAssigner(0.2, 0.2)
@@ -261,11 +263,14 @@ class CenterRegionAssigner(BaseAssigner):
               match with multiple gts. Shape: (num_gt, ).
 
         Returns:
-            assigned_gt_inds: The assigned gt index of each prior bbox
+            tuple: Returns (assigned_gt_inds, shadowed_gt_inds).
+
+                - assigned_gt_inds: The assigned gt index of each prior bbox \
                     (i.e. index from 1 to num_gts). Shape: (num_prior, ).
-            shadowed_gt_inds: shadowed gt indices. It is a tensor of shape
-              (num_ignore, 2) with first column being the shadowed prior bbox
-              indices and the second column the shadowed gt indices (1-based)
+                - shadowed_gt_inds: shadowed gt indices. It is a tensor of \
+                    shape (num_ignore, 2) with first column being the \
+                    shadowed prior bbox indices and the second column the \
+                    shadowed gt indices (1-based).
         """
         num_bboxes, num_gts = is_bbox_in_gt_core.shape
 

mmdet/core/bbox/samplers/sampling_result.py

@@ -97,9 +97,9 @@ class SamplingResult(util_mixins.NiceRepr):
             kwargs (keyword arguments):
                 - num_preds: number of predicted boxes
                 - num_gts: number of true boxes
-                - p_ignore (float): probability of a predicted box assinged to
+                - p_ignore (float): probability of a predicted box assinged to \
                     an ignored truth.
-                - p_assigned (float): probability of a predicted box not being
+                - p_assigned (float): probability of a predicted box not being \
                     assigned.
                 - p_use_label (float | bool): with labels or not.
 

mmdet/core/mask/structures.py

@@ -507,9 +507,9 @@ class PolygonMasks(BaseInstanceMasks):
     def areas(self):
         """Compute areas of masks.
 
-        This func is modified from
-        https://github.com/facebookresearch/detectron2/blob/ffff8acc35ea88ad1cb1806ab0f00b4c1c5dbfd9/detectron2/structures/masks.py#L387
-        Only works with Polygons, using the shoelace formula
+        This func is modified from `detectron2
+        <https://github.com/facebookresearch/detectron2/blob/ffff8acc35ea88ad1cb1806ab0f00b4c1c5dbfd9/detectron2/structures/masks.py#L387>`_.
+        The function only works with Polygons using the shoelace formula.
 
         Return:
             ndarray: areas of each instance

mmdet/core/mask/utils.py

@@ -17,8 +17,8 @@ def split_combined_polys(polys, poly_lens, polys_per_mask):
             of each mask
 
     Returns:
-        list: a list (length = image num) of list (length = mask num) of
-            list (length = poly num) of numpy array
+        list: a list (length = image num) of list (length = mask num) of \
+            list (length = poly num) of numpy array.
     """
     mask_polys_list = []
     for img_id in range(len(polys)):

mmdet/core/post_processing/bbox_nms.py

@@ -23,7 +23,7 @@ def multiclass_nms(multi_bboxes,
             applying NMS
 
     Returns:
-        tuple: (bboxes, labels), tensors of shape (k, 5) and (k, 1). Labels
+        tuple: (bboxes, labels), tensors of shape (k, 5) and (k, 1). Labels \
             are 0-based.
     """
     num_classes = multi_scores.size(1) - 1

mmdet/core/utils/misc.py

@@ -46,7 +46,7 @@ def multi_apply(func, *args, **kwargs):
             arguments
 
     Returns:
-        tuple(list): A tuple containing multiple list, each list contains
+        tuple(list): A tuple containing multiple list, each list contains \
             a kind of returned results by the function
     """
     pfunc = partial(func, **kwargs) if kwargs else func

mmdet/datasets/cityscapes.py

@@ -45,8 +45,8 @@ class CityscapesDataset(CocoDataset):
             ann_info (list[dict]): Annotation info of an image.
 
         Returns:
-            dict: A dict containing the following keys: bboxes, bboxes_ignore,
-                labels, masks, seg_map.
+            dict: A dict containing the following keys: bboxes, \
+                bboxes_ignore, labels, masks, seg_map. \
                 "masks" are already decoded into binary masks.
         """
         gt_bboxes = []
@@ -102,7 +102,7 @@ class CityscapesDataset(CocoDataset):
                 the txt files will be named "somepath/xxx.txt".
 
         Returns:
-            list[str: str]: result txt files which contains corresponding
+            list[str]: Result txt files which contains corresponding \
                 instance segmentation images.
         """
         try:
@@ -168,8 +168,8 @@ class CityscapesDataset(CocoDataset):
                 If not specified, a temp file will be created. Default: None.
 
         Returns:
-            tuple: (result_files, tmp_dir), result_files is a dict containing
-                the json filepaths, tmp_dir is the temporal directory created
+            tuple: (result_files, tmp_dir), result_files is a dict containing \
+                the json filepaths, tmp_dir is the temporal directory created \
                 for saving txt/png files when txtfile_prefix is not specified.
         """
         assert isinstance(results, list), 'results must be a list'
@@ -229,7 +229,7 @@ class CityscapesDataset(CocoDataset):
                 also be computed. Default: 0.5.
 
         Returns:
-            dict[str, float]: COCO style evaluation metric or cityscapes mAP
+            dict[str, float]: COCO style evaluation metric or cityscapes mAP \
                 and AP@50.
         """
         eval_results = dict()
@@ -268,7 +268,7 @@ class CityscapesDataset(CocoDataset):
                 related information during evaluation. Default: None.
 
         Returns:
-            dict[str: float]: Cityscapes evaluation results, contains 'mAP'
+            dict[str: float]: Cityscapes evaluation results, contains 'mAP' \
                 and 'AP@50'.
         """
 

mmdet/datasets/coco.py

@@ -128,8 +128,8 @@ class CocoDataset(CustomDataset):
             with_mask (bool): Whether to parse mask annotations.
 
         Returns:
-            dict: A dict containing the following keys: bboxes, bboxes_ignore,
-                labels, masks, seg_map. "masks" are raw annotations and not
+            dict: A dict containing the following keys: bboxes, bboxes_ignore,\
+                labels, masks, seg_map. "masks" are raw annotations and not \
                 decoded into binary masks.
         """
         gt_bboxes = []
@@ -285,7 +285,7 @@ class CocoDataset(CustomDataset):
                 "somepath/xxx.proposal.json".
 
         Returns:
-            dict[str: str]: Possible keys are "bbox", "segm", "proposal", and
+            dict[str: str]: Possible keys are "bbox", "segm", "proposal", and \
                 values are corresponding filenames.
         """
         result_files = dict()
@@ -344,8 +344,8 @@ class CocoDataset(CustomDataset):
                 If not specified, a temp file will be created. Default: None.
 
         Returns:
-            tuple: (result_files, tmp_dir), result_files is a dict containing
-                the json filepaths, tmp_dir is the temporal directory created
+            tuple: (result_files, tmp_dir), result_files is a dict containing \
+                the json filepaths, tmp_dir is the temporal directory created \
                 for saving json files when jsonfile_prefix is not specified.
         """
         assert isinstance(results, list), 'results must be a list'

mmdet/datasets/custom.py

@@ -177,7 +177,7 @@ class CustomDataset(Dataset):
             idx (int): Index of data.
 
         Returns:
-            dict: Training/test data (with annotation if `test_mode` is set
+            dict: Training/test data (with annotation if `test_mode` is set \
                 True).
         """
 
@@ -197,7 +197,7 @@ class CustomDataset(Dataset):
             idx (int): Index of data.
 
         Returns:
-            dict: Training data and annotation after pipeline with new keys
+            dict: Training data and annotation after pipeline with new keys \
                 introduced by pipeline.
         """
 
@@ -216,7 +216,7 @@ class CustomDataset(Dataset):
             idx (int): Index of data.
 
         Returns:
-            dict: Testing data after pipeline with new keys intorduced by
+            dict: Testing data after pipeline with new keys intorduced by \
                 piepline.
         """
 

mmdet/datasets/pipelines/formating.py

@@ -119,7 +119,7 @@ class Transpose(object):
             results (dict): Result dict contains the data to transpose.
 
         Returns:
-            dict: The result dict contains the data transposed to
+            dict: The result dict contains the data transposed to \
                 ``self.order``.
         """
         for key in self.keys:
@@ -156,7 +156,7 @@ class ToDataContainer(object):
             results (dict): Result dict contains the data to convert.
 
         Returns:
-            dict: The result dict contains the data converted to
+            dict: The result dict contains the data converted to \
                 :obj:`mmcv.DataContainer`.
         """
 
@@ -184,7 +184,7 @@ class DefaultFormatBundle(object):
     - gt_bboxes_ignore: (1)to tensor, (2)to DataContainer
     - gt_labels: (1)to tensor, (2)to DataContainer
     - gt_masks: (1)to tensor, (2)to DataContainer (cpu_only=True)
-    - gt_semantic_seg: (1)unsqueeze dim-0 (2)to tensor,
+    - gt_semantic_seg: (1)unsqueeze dim-0 (2)to tensor, \
                        (3)to DataContainer (stack=True)
     """
 
@@ -195,7 +195,7 @@ class DefaultFormatBundle(object):
             results (dict): Result dict contains the data to convert.
 
         Returns:
-            dict: The result dict contains the data that is formatted with
+            dict: The result dict contains the data that is formatted with \
                 default bundle.
         """
 
@@ -258,9 +258,9 @@ class Collect(object):
     The "img_meta" item is always populated.  The contents of the "img_meta"
     dictionary depends on "meta_keys". By default this includes:
 
-        - "img_shape": shape of the image input to the network as a tuple
-            (h, w, c).  Note that images may be zero padded on the bottom/right
-            if the batch tensor is larger than this shape.
+        - "img_shape": shape of the image input to the network as a tuple \
+            (h, w, c).  Note that images may be zero padded on the \
+            bottom/right if the batch tensor is larger than this shape.
 
         - "scale_factor": a float indicating the preprocessing scale
 
@@ -273,6 +273,7 @@ class Collect(object):
         - "pad_shape": image shape after padding
 
         - "img_norm_cfg": a dict of normalization information:
+
             - mean - per channel mean subtraction
             - std - per channel std divisor
             - to_rgb - bool indicating if bgr was converted to rgb
@@ -303,6 +304,7 @@ class Collect(object):
 
         Returns:
             dict: The result dict contains the following keys
+
                 - keys in``self.keys``
                 - ``img_metas``
         """
@@ -349,7 +351,7 @@ class WrapFieldsToLists(object):
             results (dict): Result dict contains the data to wrap.
 
         Returns:
-            dict: The result dict where value of ``self.keys`` are wrapped
+            dict: The result dict where value of ``self.keys`` are wrapped \
                 into list.
         """
 

mmdet/datasets/pipelines/transforms.py

@@ -36,11 +36,11 @@ class Resize(object):
     `img_scale` can either be a tuple (single-scale) or a list of tuple
     (multi-scale). There are 3 multiscale modes:
 
-    - ``ratio_range is not None``: randomly sample a ratio from the ratio range
-      and multiply it with the image scale.
-    - ``ratio_range is None`` and ``multiscale_mode == "range"``: randomly
+    - ``ratio_range is not None``: randomly sample a ratio from the ratio \
+      range and multiply it with the image scale.
+    - ``ratio_range is None`` and ``multiscale_mode == "range"``: randomly \
       sample a scale from the multiscale range.
-    - ``ratio_range is None`` and ``multiscale_mode == "value"``: randomly
+    - ``ratio_range is None`` and ``multiscale_mode == "value"``: randomly \
       sample a scale from multiple scales.
 
     Args:
@@ -84,8 +84,8 @@ class Resize(object):
             img_scales (list[tuple]): Images scales for selection.
 
         Returns:
-            (tuple, int): Returns a tuple ``(img_scale, scale_dix)``,
-                where ``img_scale`` is the selected image scale and
+            (tuple, int): Returns a tuple ``(img_scale, scale_dix)``, \
+                where ``img_scale`` is the selected image scale and \
                 ``scale_idx`` is the selected index in the given candidates.
         """
 
@@ -104,8 +104,8 @@ class Resize(object):
                 and uper bound of image scales.
 
         Returns:
-            (tuple, None): Returns a tuple ``(img_scale, None)``, where
-                ``img_scale`` is sampled scale and None is just a placeholder
+            (tuple, None): Returns a tuple ``(img_scale, None)``, where \
+                ``img_scale`` is sampled scale and None is just a placeholder \
                 to be consistent with :func:`random_select`.
         """
 
@@ -135,9 +135,9 @@ class Resize(object):
                 the ``img_scale``.
 
         Returns:
-            (tuple, None): Returns a tuple ``(scale, None)``, where
-                ``scale`` is sampled ratio multiplied with ``img_scale`` and
-                None is just a placeholder to be consistent with
+            (tuple, None): Returns a tuple ``(scale, None)``, where \
+                ``scale`` is sampled ratio multiplied with ``img_scale`` and \
+                None is just a placeholder to be consistent with \
                 :func:`random_select`.
         """
 
@@ -162,7 +162,7 @@ class Resize(object):
             results (dict): Result dict from :obj:`dataset`.
 
         Returns:
-            dict: Two new keys 'scale` and 'scale_idx` are added into
+            dict: Two new keys 'scale` and 'scale_idx` are added into \
                 ``results``, which would be used by subsequent pipelines.
         """
 
@@ -244,7 +244,7 @@ class Resize(object):
             results (dict): Result dict from loading pipeline.
 
         Returns:
-            dict: Resized results, 'img_shape', 'pad_shape', 'scale_factor',
+            dict: Resized results, 'img_shape', 'pad_shape', 'scale_factor', \
                 'keep_ratio' keys are added into result dict.
         """
 
@@ -332,8 +332,8 @@ class RandomFlip(object):
             results (dict): Result dict from loading pipeline.
 
         Returns:
-            dict: Flipped results, 'flip', 'flip_direction' keys are added into
-                result dict.
+            dict: Flipped results, 'flip', 'flip_direction' keys are added \
+                into result dict.
         """
 
         if 'flip' not in results:
@@ -486,7 +486,7 @@ class RandomCrop(object):
         allow_negative_crop (bool): Whether to allow a crop that does not
             contain any bbox area. Default to False.
 
-    Notes:
+    Note:
         - If the image is smaller than the crop size, return the original image
         - The keys for bboxes, labels and masks must be aligned. That is,
           `gt_bboxes` corresponds to `gt_labels` and `gt_masks`, and
@@ -810,9 +810,9 @@ class MinIoURandomCrop(object):
         min_crop_size (float): minimum crop's size (i.e. h,w := a*h, a*w,
         where a >= min_crop_size).
 
-    Notes:
-        The keys for bboxes, labels and masks should be paired. That is,
-        `gt_bboxes` corresponds to `gt_labels` and `gt_masks`, and
+    Note:
+        The keys for bboxes, labels and masks should be paired. That is, \
+        `gt_bboxes` corresponds to `gt_labels` and `gt_masks`, and \
         `gt_bboxes_ignore` to `gt_labels_ignore` and `gt_masks_ignore`.
     """
 
@@ -838,7 +838,7 @@ class MinIoURandomCrop(object):
             results (dict): Result dict from loading pipeline.
 
         Returns:
-            dict: Result dict with images and bounding boxes cropped,
+            dict: Result dict with images and bounding boxes cropped, \
                 'img_shape' key is updated.
         """
 
@@ -1174,15 +1174,15 @@ class RandomCenterCropPad(object):
     """Random center crop and random around padding for CornerNet.
 
     This operation generates randomly cropped image from the original image and
-    pads it simultaneously. Different from `RandomCrop`, the output shape may
-    not equal to `crop_size` strictly. We choose a random value from `ratios`
-    and the output shape could be larger or smaller than `crop_size`. Also the
-    pad in this operation is different from `Pad`, actually we use around
-    padding instead of right-bottom padding.
+    pads it simultaneously. Different from :class:`RandomCrop`, the output
+    shape may not equal to ``crop_size`` strictly. We choose a random value
+    from ``ratios`` and the output shape could be larger or smaller than
+    ``crop_size``. The padding operation is also different from :class:`Pad`,
+    here we use around padding instead of right-bottom padding.
 
     The relation between output image (padding image) and original image:
 
-    .. code-block: text
+    .. code-block::
 
                           output image
                +----------------------------+
@@ -1198,20 +1198,22 @@ class RandomCenterCropPad(object):
                +----------------------------+
 
     There are 5 main areas in the figure:
-        - output image: output image of this operation, also called padding
+
+        - output image: output image of this operation, also called padding \
             image in following instruction.
         - original image: input image of this operation.
         - padded area: non-intersect area of output image and original image.
         - cropped area: the overlap of output image and original image.
-        - center range: a smaller area where random center chosen from.
-            center range is computed by `border` and original image's shape
+        - center range: a smaller area where random center chosen from. \
+            center range is computed by `border` and original image's shape \
             to avoid our random center is too close to original image's border.
 
     Also this operation act differently in train and test mode, the summary
     pipeline is listed below.
 
     Train pipeline:
-        1. Choose a `random_ratio` from `ratios`, the shape of padding image
+
+        1. Choose a `random_ratio` from `ratios`, the shape of padding image \
             will be `random_ratio * crop_size`.
         2. Choose a `random_center` in `center range`.
         3. Generate padding image with center matches the `random_center`.
@@ -1220,8 +1222,9 @@ class RandomCenterCropPad(object):
         6. Refine annotations.
 
     Test pipeline:
+
         1. Compute output shape according to `test_pad_mode`.
-        2. Generate padding image with center matches the original image
+        2. Generate padding image with center matches the original image \
             center.
         3. Initialize the padding image with pixel value equals to `mean`.
         4. Copy the `cropped area` to padding image.
@@ -1247,7 +1250,7 @@ class RandomCenterCropPad(object):
             127 as padding shape value.
 
             - 'logical_or': final_shape = input_shape | padding_shape_value
-            - 'size_divisor': final_shape = int(
+            - 'size_divisor': final_shape = int( \
                 ceil(input_shape / padding_shape_value) * padding_shape_value)
     """
 
@@ -1316,6 +1319,7 @@ class RandomCenterCropPad(object):
         Args:
             patch (list[int]): The cropped area, [left, top, right, bottom].
             boxes (numpy array, (N x 4)): Ground truth boxes.
+
         Returns:
             mask (numpy array, (N,)): Each box is inside or outside the patch.
         """
@@ -1339,6 +1343,7 @@ class RandomCenterCropPad(object):
             image (np array, H x W x C): Original image.
             center (list[int]): Target crop center coord.
             size (list[int]): Target crop size. [target_h, target_w]
+
         Returns:
             cropped_img (np array, target_h x target_w x C): Cropped image.
             border (np array, 4): The distance of four border of `cropped_img`

mmdet/models/backbones/regnet.py

@@ -234,8 +234,8 @@ class RegNet(ResNet):
             divisor (int, optional): The divisor of channels. Defaults to 8.
 
         Returns:
-            list, int: return a list of widths of each stage and the number of
-                stages
+            list, int: return a list of widths of each stage and the number \
+                of stages
         """
         assert width_slope >= 0
         assert initial_width > 0

mmdet/models/dense_heads/anchor_head.py

@@ -119,9 +119,9 @@ class AnchorHead(BaseDenseHead):
 
         Returns:
             tuple:
-                cls_score (Tensor): Cls scores for a single scale level
+                cls_score (Tensor): Cls scores for a single scale level \
                     the channels number is num_anchors * num_classes.
-                bbox_pred (Tensor): Box energies / deltas for a single scale
+                bbox_pred (Tensor): Box energies / deltas for a single scale \
                     level, the channels number is num_anchors * 4.
         """
         cls_score = self.conv_cls(x)
@@ -136,13 +136,14 @@ class AnchorHead(BaseDenseHead):
                 a 4D-tensor.
 
         Returns:
-            tuple: Usually a tuple of classification scores and bbox prediction
-                cls_scores (list[Tensor]): Classification scores for all scale
-                    levels, each is a 4D-tensor, the channels number is
-                    num_anchors * num_classes.
-                bbox_preds (list[Tensor]): Box energies / deltas for all scale
-                    levels, each is a 4D-tensor, the channels number is
-                    num_anchors * 4.
+            tuple: A tuple of classification scores and bbox prediction.
+
+                - cls_scores (list[Tensor]): Classification scores for all \
+                    scale levels, each is a 4D-tensor, the channels number \
+                    is num_anchors * num_classes.
+                - bbox_preds (list[Tensor]): Box energies / deltas for all \
+                    scale levels, each is a 4D-tensor, the channels number \
+                    is num_anchors * 4.
         """
         return multi_apply(self.forward_single, feats)
 
@@ -156,8 +157,8 @@ class AnchorHead(BaseDenseHead):
 
         Returns:
             tuple:
-                anchor_list (list[Tensor]): Anchors of each image
-                valid_flag_list (list[Tensor]): Valid flags of each image
+                anchor_list (list[Tensor]): Anchors of each image.
+                valid_flag_list (list[Tensor]): Valid flags of each image.
         """
         num_imgs = len(img_metas)
 
@@ -308,13 +309,17 @@ class AnchorHead(BaseDenseHead):
                 set of anchors.
 
         Returns:
-            tuple:
-                labels_list (list[Tensor]): Labels of each level
-                label_weights_list (list[Tensor]): Label weights of each level
-                bbox_targets_list (list[Tensor]): BBox targets of each level
-                bbox_weights_list (list[Tensor]): BBox weights of each level
-                num_total_pos (int): Number of positive samples in all images
-                num_total_neg (int): Number of negative samples in all images
+            tuple: Usually returns a tuple containing learning targets.
+
+                - labels_list (list[Tensor]): Labels of each level.
+                - label_weights_list (list[Tensor]): Label weights of each \
+                    level.
+                - bbox_targets_list (list[Tensor]): BBox targets of each level.
+                - bbox_weights_list (list[Tensor]): BBox weights of each level.
+                - num_total_pos (int): Number of positive samples in all \
+                    images.
+                - num_total_neg (int): Number of negative samples in all \
+                    images.
             additional_returns: This function enables user-defined returns from
                 `self._get_targets_single`. These returns are currently refined
                 to properties at each feature map (i.e. having HxW dimension).

mmdet/models/dense_heads/fcos_head.py

@@ -45,6 +45,7 @@ class FCOSHead(AnchorFreeHead):
         loss_centerness (dict): Config of centerness loss.
         norm_cfg (dict): dictionary to construct and config norm layer.
             Default: norm_cfg=dict(type='GN', num_groups=32, requires_grad=True).
+
     Example:
         >>> self = FCOSHead(11, 7)
         >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
@@ -108,13 +109,13 @@ class FCOSHead(AnchorFreeHead):
 
         Returns:
             tuple:
-                cls_scores (list[Tensor]): Box scores for each scale level,
-                    each is a 4D-tensor, the channel number is
+                cls_scores (list[Tensor]): Box scores for each scale level, \
+                    each is a 4D-tensor, the channel number is \
                     num_points * num_classes.
-                bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                    level, each is a 4D-tensor, the channel number is
+                bbox_preds (list[Tensor]): Box energies / deltas for each \
+                    scale level, each is a 4D-tensor, the channel number is \
                     num_points * 4.
-                centernesses (list[Tensor]): Centerss for each scale level,
+                centernesses (list[Tensor]): Centerss for each scale level, \
                     each is a 4D-tensor, the channel number is num_points * 1.
         """
         return multi_apply(self.forward_single, feats, self.scales,
@@ -132,7 +133,7 @@ class FCOSHead(AnchorFreeHead):
                 is True.
 
         Returns:
-            tuple: scores for each class, bbox predictions and centerness
+            tuple: scores for each class, bbox predictions and centerness \
                 predictions of input feature maps.
         """
         cls_score, bbox_pred, cls_feat, reg_feat = super().forward_single(x)
@@ -272,12 +273,12 @@ class FCOSHead(AnchorFreeHead):
             rescale (bool): If True, return boxes in original image space
 
         Returns:
-            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
-                The first item is an (n, 5) tensor, where the first 4 columns
-                are bounding box positions (tl_x, tl_y, br_x, br_y) and the
-                5-th column is a score between 0 and 1. The second item is a
-                (n,) tensor where each item is the predicted class label of the
-                corresponding box.
+            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. \
+                The first item is an (n, 5) tensor, where the first 4 columns \
+                are bounding box positions (tl_x, tl_y, br_x, br_y) and the \
+                5-th column is a score between 0 and 1. The second item is a \
+                (n,) tensor where each item is the predicted class label of \
+                the corresponding box.
         """
         assert len(cls_scores) == len(bbox_preds)
         num_levels = len(cls_scores)
@@ -335,8 +336,8 @@ class FCOSHead(AnchorFreeHead):
             rescale (bool): If True, return boxes in original image space.
 
         Returns:
-            Tensor: Labeled boxes in shape (n, 5), where the first 4 columns
-                are bounding box positions (tl_x, tl_y, br_x, br_y) and the
+            Tensor: Labeled boxes in shape (n, 5), where the first 4 columns \
+                are bounding box positions (tl_x, tl_y, br_x, br_y) and the \
                 5-th column is a score between 0 and 1.
         """
         cfg = self.test_cfg if cfg is None else cfg
@@ -408,8 +409,8 @@ class FCOSHead(AnchorFreeHead):
 
         Returns:
             tuple:
-                concat_lvl_labels (list[Tensor]): Labels of each level.
-                concat_lvl_bbox_targets (list[Tensor]): BBox targets of each
+                concat_lvl_labels (list[Tensor]): Labels of each level. \
+                concat_lvl_bbox_targets (list[Tensor]): BBox targets of each \
                     level.
         """
         assert len(points) == len(self.regress_ranges)

mmdet/models/detectors/atss.py

@@ -4,6 +4,7 @@ from .single_stage import SingleStageDetector
 
 @DETECTORS.register_module()
 class ATSS(SingleStageDetector):
+    """Implementation of `ATSS <https://arxiv.org/abs/1912.02424>`_."""
 
     def __init__(self,
                  backbone,

mmdet/models/detectors/base.py

@@ -180,8 +180,8 @@ class BaseDetector(nn.Module, metaclass=ABCMeta):
                 losses and other necessary infomation.
 
         Returns:
-            tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor
-                which may be a weighted sum of all losses, log_vars contains
+            tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor \
+                which may be a weighted sum of all losses, log_vars contains \
                 all the variables to be sent to the logger.
         """
         log_vars = OrderedDict()
@@ -223,14 +223,15 @@ class BaseDetector(nn.Module, metaclass=ABCMeta):
                 and reserved.
 
         Returns:
-            dict: It should contain at least 3 keys: ``loss``, ``log_vars``,
+            dict: It should contain at least 3 keys: ``loss``, ``log_vars``, \
                 ``num_samples``.
-                ``loss`` is a tensor for back propagation, which can be a
+
+                - ``loss`` is a tensor for back propagation, which can be a \
                 weighted sum of multiple losses.
-                ``log_vars`` contains all the variables to be sent to the
+                - ``log_vars`` contains all the variables to be sent to the
                 logger.
-                ``num_samples`` indicates the batch size (when the model is
-                DDP, it means the batch size on each GPU), which is used for
+                - ``num_samples`` indicates the batch size (when the model is \
+                DDP, it means the batch size on each GPU), which is used for \
                 averaging the logs.
         """
         losses = self(**data)

mmdet/models/losses/ghm_loss.py

@@ -22,8 +22,8 @@ class GHMC(nn.Module):
     """GHM Classification Loss.
 
     Details of the theorem can be viewed in the paper
-    "Gradient Harmonized Single-stage Detector".
-    https://arxiv.org/abs/1811.05181
+    `Gradient Harmonized Single-stage Detector
+    <https://arxiv.org/abs/1811.05181>`_.
 
     Args:
         bins (int): Number of the unit regions for distribution calculation.
@@ -100,8 +100,8 @@ class GHMR(nn.Module):
     """GHM Regression Loss.
 
     Details of the theorem can be viewed in the paper
-    "Gradient Harmonized Single-stage Detector"
-    https://arxiv.org/abs/1811.05181
+    `Gradient Harmonized Single-stage Detector
+    <https://arxiv.org/abs/1811.05181>`_.
 
     Args:
         mu (float): The parameter for the Authentic Smooth L1 loss.

mmdet/models/losses/iou_loss.py

@@ -29,8 +29,10 @@ def iou_loss(pred, target, eps=1e-6):
 
 @weighted_loss
 def bounded_iou_loss(pred, target, beta=0.2, eps=1e-3):
-    """`Improving Object Localization with Fitness NMS and Bounded IoU Loss.
+    """BIoULoss.
 
+    This is an implementation of paper
+    `Improving Object Localization with Fitness NMS and Bounded IoU Loss.
     <https://arxiv.org/abs/1711.00164>`_.
 
     Args:

mmdet/models/necks/bfp.py

@@ -13,7 +13,8 @@ class BFP(nn.Module):
     BFP takes multi-level features as inputs and gather them into a single one,
     then refine the gathered feature and scatter the refined results to
     multi-level features. This module is used in Libra R-CNN (CVPR 2019), see
-    https://arxiv.org/pdf/1904.02701.pdf for details.
+    the paper `Libra R-CNN: Towards Balanced Learning for Object Detection
+    <https://arxiv.org/abs/1904.02701>`_ for details.
 
     Args:
         in_channels (int): Number of input channels (feature maps of all levels

mmdet/models/necks/fpn.py

@@ -8,10 +8,10 @@ from ..builder import NECKS
 
 @NECKS.register_module()
 class FPN(nn.Module):
-    """Feature Pyramid Network.
+    r"""Feature Pyramid Network.
 
-    This is an implementation of - Feature Pyramid Networks for Object
-    Detection (https://arxiv.org/abs/1612.03144)
+    This is an implementation of paper `Feature Pyramid Networks for Object
+    Detection <https://arxiv.org/abs/1612.03144>`_.
 
     Args:
         in_channels (List[int]): Number of input channels per scale.

mmdet/models/necks/hrfpn.py

@@ -11,7 +11,8 @@ from ..builder import NECKS
 class HRFPN(nn.Module):
     """HRFPN (High Resolution Feature Pyrmamids)
 
-    arXiv: https://arxiv.org/abs/1904.04514
+    paper: `High-Resolution Representations for Labeling Pixels and Regions
+    <https://arxiv.org/abs/1904.04514>`_.
 
     Args:
         in_channels (list): number of channels for each branch.

setup.cfg

@@ -6,3 +6,8 @@ known_first_party = mmdet
 known_third_party = PIL,asynctest,cityscapesscripts,cv2,matplotlib,mmcv,numpy,onnx,pycocotools,pytest,robustness_eval,seaborn,six,terminaltables,torch,torchvision
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY
+
+[yapf]
+BASED_ON_STYLE = pep8
+BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true
+SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true