add readthedoc support

adet/__init__.py

@@ -1,3 +1,3 @@
 from adet import modeling
 
-__version__ = "0.1"
+__version__ = "0.1.1"

adet/checkpoint/adet_checkpoint.py

@@ -4,6 +4,10 @@
 
 
 class AdetCheckpointer(DetectionCheckpointer):
+    """
+    Same as :class:`DetectronCheckpointer`, but is able to convert models
+    in AdelaiDet, such as LPF backbone.
+    """
     def _load_file(self, filename):
         if filename.endswith(".pkl"):
             with PathManager.open(filename, "rb") as f:

adet/config/defaults.py

@@ -53,13 +53,10 @@
 # ---------------------------------------------------------------------------- #
 
 _C.MODEL.VOVNET = CN()
-
 _C.MODEL.VOVNET.CONV_BODY = "V-39-eSE"
 _C.MODEL.VOVNET.OUT_FEATURES = ["stage2", "stage3", "stage4", "stage5"]
 
 # Options: FrozenBN, GN, "SyncBN", "BN"
 _C.MODEL.VOVNET.NORM = "FrozenBN"
-
 _C.MODEL.VOVNET.OUT_CHANNELS = 256
-
 _C.MODEL.VOVNET.BACKBONE_OUT_CHANNELS = 256

adet/data/__init__.py

@@ -1,5 +1,5 @@
 from . import builtin  # ensure the builtin datasets are registered
-from .dataset_mapper import DatasetMapperWithBasis
+# from .dataset_mapper import DatasetMapperWithBasis
 
 
-__all__ = ["DatasetMapperWithBasis"]
+# __all__ = ["DatasetMapperWithBasis"]

adet/data/builtin.py

@@ -1,9 +1,8 @@
 import os
 
 from detectron2.data.datasets.register_coco import register_coco_instances
-from detectron2.data.datasets.builtin_meta import _get_builtin_metadata
 
-# register plane reconstruction
+# register person in context dataset
 
 _PREDEFINED_SPLITS_PIC = {
     "pic_person_train": ("pic/image/train", "pic/annotations/train_person.json"),
@@ -25,5 +24,4 @@ def register_all_coco(root="datasets"):
             os.path.join(root, image_root),
         )
 
-
 register_all_coco()

adet/layers/deform_conv.py

@@ -17,7 +17,15 @@ def backward(ctx, grad):
 
 
 class DFConv2d(nn.Module):
-    """Deformable convolutional layer"""
+    """
+    Deformable convolutional layer with configurable
+    deformable groups, dilations and groups.
+
+    Code is from:
+    https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/layers/misc.py
+
+
+    """
     def __init__(
             self,
             in_channels,

adet/layers/iou_loss.py

@@ -3,11 +3,25 @@
 
 
 class IOULoss(nn.Module):
+    """
+    Intersetion Over Union (IoU) loss which supports three
+    different IoU computations:
+
+    * IoU
+    * Linear IoU
+    * gIoU
+    """
     def __init__(self, loc_loss_type='iou'):
         super(IOULoss, self).__init__()
         self.loc_loss_type = loc_loss_type
 
     def forward(self, pred, target, weight=None):
+        """
+        Args:
+            pred: Nx4 predicted bounding boxes
+            target: Nx4 target bounding boxes
+            weight: N loss weight for each instance
+        """
         pred_left = pred[:, 0]
         pred_top = pred[:, 1]
         pred_right = pred[:, 2]

adet/layers/ml_nms.py

@@ -6,12 +6,13 @@ def ml_nms(boxlist, nms_thresh, max_proposals=-1,
     """
     Performs non-maximum suppression on a boxlist, with scores specified
     in a boxlist field via score_field.
-    Arguments:
-        boxlist(BoxList)
-        nms_thresh (float)
+    
+    Args:
+        boxlist (detectron2.structures.Boxes): 
+        nms_thresh (float): 
         max_proposals (int): if > 0, then only the top max_proposals are kept
             after non-maximum suppression
-        score_field (str)
+        score_field (str): 
     """
     if nms_thresh <= 0:
         return boxlist

adet/modeling/__init__.py

@@ -1,3 +1,6 @@
 from .fcos import FCOS
 from .backbone import build_fcos_resnet_fpn_backbone
 from .one_stage_detector import OneStageDetector
+
+_EXCLUDE = {"torch", "ShapeSpec"}
+__all__ = [k for k in globals().keys() if k not in _EXCLUDE and not k.startswith("_")]

adet/modeling/backbone/vovnet.py

@@ -148,7 +148,6 @@ def __init__(self,
 
         self.ese = eSEModule(concat_ch)
 
-
     def forward(self, x):
 
         identity_feat = x

adet/modeling/fcos/fcos.py

@@ -27,6 +27,9 @@ def forward(self, input):
 
 @PROPOSAL_GENERATOR_REGISTRY.register()
 class FCOS(nn.Module):
+    """
+    Implement FCOS (https://arxiv.org/abs/1904.01355).
+    """
     def __init__(self, cfg, input_shape: Dict[str, ShapeSpec]):
         super().__init__()
         # fmt: off

adet/modeling/one_stage_detector.py

@@ -4,6 +4,10 @@
 
 @META_ARCH_REGISTRY.register()
 class OneStageDetector(ProposalNetwork):
+    """
+    Same as :class:`detectron2.modeling.ProposalNetwork`.
+    Uses "instances" as the return key instead of using "proposal".
+    """
     def forward(self, batched_inputs):
         if self.training:
             return super().forward(batched_inputs)

adet/modeling/poolers.py

@@ -6,6 +6,8 @@
     ROIPooler, convert_boxes_to_pooler_format, assign_boxes_to_levels
 )
 
+__all__ = ["TopPooler"]
+
 
 def _box_max_size(boxes):
     box = boxes.tensor
@@ -20,7 +22,7 @@ def assign_boxes_to_levels_by_length(
     vector.
 
     Args:
-        box_lists (list[Boxes] | list[RotatedBoxes]): A list of N Boxes or N RotatedBoxes,
+        box_lists (list[detectron2.structures.Boxes]): A list of N Boxes or N RotatedBoxes,
             where N is the number of images in the batch.
         min_level (int): Smallest feature map level index. The input is considered index 0,
             the output of stage 1 is index 1, and so.

docs/.gitignore

@@ -0,0 +1 @@
+_build

docs/Makefile

@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)