Refine docs and fix format issues

.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.

docs/conf.py

@@ -39,10 +39,7 @@
 ]
 
 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 @@ def gen_base_anchors(self):
         """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 @@ def gen_single_level_base_anchors(self,
                 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 @@ def grid_anchors(self, featmap_sizes, device='cuda'):
             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 @@ def single_level_valid_flags(self,
                 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 @@ def gen_base_anchors(self):
         """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 = []
@@ -471,12 +471,13 @@ def __repr__(self):
 class LegacyAnchorGenerator(AnchorGenerator):
     """Legacy anchor generator used in MMDetection V1.x.
 
-    Difference to the V2.0 anchor generator:
+    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.
-    3. The anchors' corners are quantized.
+        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.
+        3. The anchors' corners are quantized.
 
     Args:
         strides (list[int] | list[tuple[int]]): Strides of anchors
@@ -523,7 +524,7 @@ def gen_single_level_base_anchors(self,
         """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 @@ def get_gt_priorities(self, gt_bboxes):
             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 @@ def get_gt_priorities(self, gt_bboxes):
     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 @@ def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None):
             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 @@ def assign_one_hot_gt_indices(self,
               match with multiple gts. Shape: (num_gt, ).
 
         Returns:
-            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)
+            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).
         """
         num_bboxes, num_gts = is_bbox_in_gt_core.shape
 

mmdet/core/bbox/samplers/sampling_result.py

@@ -97,9 +97,9 @@ def random(cls, rng=None, **kwargs):
             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 @@ def to_bitmap(self):
     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

@@ -37,16 +37,16 @@ def multi_apply(func, *args, **kwargs):
 
     Note:
         This function applies the ``func`` to multiple inputs and
-            map the multiple outputs of the ``func`` into different
-            list. Each list contains the same type of outputs corresponding
-            to different inputs.
+        map the multiple outputs of the ``func`` into different
+        list. Each list contains the same type of outputs corresponding
+        to different inputs.
 
     Args:
         func (Function): A function that will be applied to a list of
             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 @@ def _parse_ann_info(self, img_info, ann_info):
             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 = []
@@ -102,8 +102,8 @@ def results2txt(self, results, outfile_prefix):
                 the txt files will be named "somepath/xxx.txt".
 
         Returns:
-            list[str: str]: result txt files which contains corresponding
-            instance segmentation images.
+            list[str]: Result txt files which contains corresponding \
+                instance segmentation images.
         """
         try:
             import cityscapesscripts.helpers.labels as CSLabels
@@ -168,8 +168,8 @@ def format_results(self, results, txtfile_prefix=None):
                 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 @@ def evaluate(self,
                 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 @@ def _evaluate_cityscapes(self, results, txtfile_prefix, logger):
                 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 @@ def _parse_ann_info(self, img_info, ann_info):
             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 @@ def results2json(self, results, outfile_prefix):
                 "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 @@ def format_results(self, results, jsonfile_prefix=None, **kwargs):
                 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 @@ def __getitem__(self, idx):
             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 @@ def prepare_train_img(self, idx):
             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 @@ def prepare_test_img(self, idx):
             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.
         """