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Update type hints (open-mmlab#10040)
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@IRONICBo
IRONICBo authored Apr 4, 2023
1 parent 17a29a5 commit 4ca5ad8
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Showing 7 changed files with 179 additions and 99 deletions.
63 changes: 38 additions & 25 deletions mmdet/models/task_modules/coders/bucketing_bbox_coder.py
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Original file line number Diff line number Diff line change
@@ -1,10 +1,14 @@
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence, Tuple, Union

import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor

from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes, bbox_rescale, get_box_tensor
from mmdet.structures.bbox import (BaseBoxes, HorizontalBoxes, bbox_rescale,
get_box_tensor)
from .base_bbox_coder import BaseBBoxCoder


Expand Down Expand Up @@ -32,13 +36,13 @@ class BucketingBBoxCoder(BaseBBoxCoder):
"""

def __init__(self,
num_buckets,
scale_factor,
offset_topk=2,
offset_upperbound=1.0,
cls_ignore_neighbor=True,
clip_border=True,
**kwargs):
num_buckets: int,
scale_factor: int,
offset_topk: int = 2,
offset_upperbound: float = 1.0,
cls_ignore_neighbor: bool = True,
clip_border: bool = True,
**kwargs) -> None:
super().__init__(**kwargs)
self.num_buckets = num_buckets
self.scale_factor = scale_factor
Expand All @@ -47,7 +51,8 @@ def __init__(self,
self.cls_ignore_neighbor = cls_ignore_neighbor
self.clip_border = clip_border

def encode(self, bboxes, gt_bboxes):
def encode(self, bboxes: Union[Tensor, BaseBoxes],
gt_bboxes: Union[Tensor, BaseBoxes]) -> Tuple[Tensor]:
"""Get bucketing estimation and fine regression targets during
training.
Expand All @@ -71,7 +76,12 @@ def encode(self, bboxes, gt_bboxes):
self.cls_ignore_neighbor)
return encoded_bboxes

def decode(self, bboxes, pred_bboxes, max_shape=None):
def decode(
self,
bboxes: Union[Tensor, BaseBoxes],
pred_bboxes: Tensor,
max_shape: Optional[Tuple[int]] = None
) -> Tuple[Union[Tensor, BaseBoxes], Tensor]:
"""Apply transformation `pred_bboxes` to `boxes`.
Args:
boxes (torch.Tensor or :obj:`BaseBoxes`): Basic boxes.
Expand All @@ -97,7 +107,9 @@ def decode(self, bboxes, pred_bboxes, max_shape=None):
return bboxes, loc_confidence


def generat_buckets(proposals, num_buckets, scale_factor=1.0):
def generat_buckets(proposals: Tensor,
num_buckets: int,
scale_factor: float = 1.0) -> Tuple[Tensor]:
"""Generate buckets w.r.t bucket number and scale factor of proposals.
Args:
Expand Down Expand Up @@ -145,13 +157,13 @@ def generat_buckets(proposals, num_buckets, scale_factor=1.0):
return bucket_w, bucket_h, l_buckets, r_buckets, t_buckets, d_buckets


def bbox2bucket(proposals,
gt,
num_buckets,
scale_factor,
offset_topk=2,
offset_upperbound=1.0,
cls_ignore_neighbor=True):
def bbox2bucket(proposals: Tensor,
gt: Tensor,
num_buckets: int,
scale_factor: float,
offset_topk: int = 2,
offset_upperbound: float = 1.0,
cls_ignore_neighbor: bool = True) -> Tuple[Tensor]:
"""Generate buckets estimation and fine regression targets.
Args:
Expand Down Expand Up @@ -268,13 +280,14 @@ def bbox2bucket(proposals,
return offsets, offsets_weights, bucket_labels, bucket_cls_weights


def bucket2bbox(proposals,
cls_preds,
offset_preds,
num_buckets,
scale_factor=1.0,
max_shape=None,
clip_border=True):
def bucket2bbox(proposals: Tensor,
cls_preds: Tensor,
offset_preds: Tensor,
num_buckets: int,
scale_factor: float = 1.0,
max_shape: Optional[Union[Sequence[int], Tensor,
Sequence[Sequence[int]]]] = None,
clip_border: bool = True) -> Tuple[Tensor]:
"""Apply bucketing estimation (cls preds) and fine regression (offset
preds) to generate det bboxes.
Expand Down
77 changes: 45 additions & 32 deletions mmdet/models/task_modules/coders/delta_xywh_bbox_coder.py
View file
Original file line number Diff line number Diff line change
@@ -1,11 +1,13 @@
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from typing import Optional, Sequence, Union

import numpy as np
import torch
from torch import Tensor

from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes, get_box_tensor
from mmdet.structures.bbox import BaseBoxes, HorizontalBoxes, get_box_tensor
from .base_bbox_coder import BaseBBoxCoder


Expand All @@ -32,20 +34,21 @@ class DeltaXYWHBBoxCoder(BaseBBoxCoder):
"""

def __init__(self,
target_means=(0., 0., 0., 0.),
target_stds=(1., 1., 1., 1.),
clip_border=True,
add_ctr_clamp=False,
ctr_clamp=32,
**kwargs):
target_means: Sequence[float] = (0., 0., 0., 0.),
target_stds: Sequence[float] = (1., 1., 1., 1.),
clip_border: bool = True,
add_ctr_clamp: bool = False,
ctr_clamp: int = 32,
**kwargs) -> None:
super().__init__(**kwargs)
self.means = target_means
self.stds = target_stds
self.clip_border = clip_border
self.add_ctr_clamp = add_ctr_clamp
self.ctr_clamp = ctr_clamp

def encode(self, bboxes, gt_bboxes):
def encode(self, bboxes: Union[Tensor, BaseBoxes],
gt_bboxes: Union[Tensor, BaseBoxes]) -> Tensor:
"""Get box regression transformation deltas that can be used to
transform the ``bboxes`` into the ``gt_bboxes``.
Expand All @@ -65,11 +68,14 @@ def encode(self, bboxes, gt_bboxes):
encoded_bboxes = bbox2delta(bboxes, gt_bboxes, self.means, self.stds)
return encoded_bboxes

def decode(self,
bboxes,
pred_bboxes,
max_shape=None,
wh_ratio_clip=16 / 1000):
def decode(
self,
bboxes: Union[Tensor, BaseBoxes],
pred_bboxes: Tensor,
max_shape: Optional[Union[Sequence[int], Tensor,
Sequence[Sequence[int]]]] = None,
wh_ratio_clip: Optional[float] = 16 / 1000
) -> Union[Tensor, BaseBoxes]:
"""Apply transformation `pred_bboxes` to `boxes`.
Args:
Expand Down Expand Up @@ -123,7 +129,12 @@ def decode(self,
return decoded_bboxes


def bbox2delta(proposals, gt, means=(0., 0., 0., 0.), stds=(1., 1., 1., 1.)):
def bbox2delta(
proposals: Tensor,
gt: Tensor,
means: Sequence[float] = (0., 0., 0., 0.),
stds: Sequence[float] = (1., 1., 1., 1.)
) -> Tensor:
"""Compute deltas of proposals w.r.t. gt.
We usually compute the deltas of x, y, w, h of proposals w.r.t ground
Expand Down Expand Up @@ -168,15 +179,16 @@ def bbox2delta(proposals, gt, means=(0., 0., 0., 0.), stds=(1., 1., 1., 1.)):
return deltas


def delta2bbox(rois,
deltas,
means=(0., 0., 0., 0.),
stds=(1., 1., 1., 1.),
max_shape=None,
wh_ratio_clip=16 / 1000,
clip_border=True,
add_ctr_clamp=False,
ctr_clamp=32):
def delta2bbox(rois: Tensor,
deltas: Tensor,
means: Sequence[float] = (0., 0., 0., 0.),
stds: Sequence[float] = (1., 1., 1., 1.),
max_shape: Optional[Union[Sequence[int], Tensor,
Sequence[Sequence[int]]]] = None,
wh_ratio_clip: float = 16 / 1000,
clip_border: bool = True,
add_ctr_clamp: bool = False,
ctr_clamp: int = 32) -> Tensor:
"""Apply deltas to shift/scale base boxes.
Typically the rois are anchor or proposed bounding boxes and the deltas are
Expand Down Expand Up @@ -267,15 +279,16 @@ def delta2bbox(rois,
return bboxes


def onnx_delta2bbox(rois,
deltas,
means=(0., 0., 0., 0.),
stds=(1., 1., 1., 1.),
max_shape=None,
wh_ratio_clip=16 / 1000,
clip_border=True,
add_ctr_clamp=False,
ctr_clamp=32):
def onnx_delta2bbox(rois: Tensor,
deltas: Tensor,
means: Sequence[float] = (0., 0., 0., 0.),
stds: Sequence[float] = (1., 1., 1., 1.),
max_shape: Optional[Union[Sequence[int], Tensor,
Sequence[Sequence[int]]]] = None,
wh_ratio_clip: float = 16 / 1000,
clip_border: Optional[bool] = True,
add_ctr_clamp: bool = False,
ctr_clamp: int = 32) -> Tensor:
"""Apply deltas to shift/scale base boxes.
Typically the rois are anchor or proposed bounding boxes and the deltas are
Expand Down
22 changes: 18 additions & 4 deletions mmdet/models/task_modules/coders/distance_point_bbox_coder.py
View file
Original file line number Diff line number Diff line change
@@ -1,6 +1,10 @@
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence, Union

from torch import Tensor

from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import (HorizontalBoxes, bbox2distance,
from mmdet.structures.bbox import (BaseBoxes, HorizontalBoxes, bbox2distance,
distance2bbox, get_box_tensor)
from .base_bbox_coder import BaseBBoxCoder

Expand All @@ -17,11 +21,15 @@ class DistancePointBBoxCoder(BaseBBoxCoder):
border of the image. Defaults to True.
"""

def __init__(self, clip_border=True, **kwargs):
def __init__(self, clip_border: Optional[bool] = True, **kwargs) -> None:
super().__init__(**kwargs)
self.clip_border = clip_border

def encode(self, points, gt_bboxes, max_dis=None, eps=0.1):
def encode(self,
points: Tensor,
gt_bboxes: Union[Tensor, BaseBoxes],
max_dis: Optional[float] = None,
eps: float = 0.1) -> Tensor:
"""Encode bounding box to distances.
Args:
Expand All @@ -41,7 +49,13 @@ def encode(self, points, gt_bboxes, max_dis=None, eps=0.1):
assert gt_bboxes.size(-1) == 4
return bbox2distance(points, gt_bboxes, max_dis, eps)

def decode(self, points, pred_bboxes, max_shape=None):
def decode(
self,
points: Tensor,
pred_bboxes: Tensor,
max_shape: Optional[Union[Sequence[int], Tensor,
Sequence[Sequence[int]]]] = None
) -> Union[Tensor, BaseBoxes]:
"""Decode distance prediction to bounding box.
Args:
Expand Down
51 changes: 31 additions & 20 deletions mmdet/models/task_modules/coders/legacy_delta_xywh_bbox_coder.py
View file
Original file line number Diff line number Diff line change
@@ -1,9 +1,12 @@
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence, Union

import numpy as np
import torch
from torch import Tensor

from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes, get_box_tensor
from mmdet.structures.bbox import BaseBoxes, HorizontalBoxes, get_box_tensor
from .base_bbox_coder import BaseBBoxCoder


Expand Down Expand Up @@ -32,14 +35,15 @@ class LegacyDeltaXYWHBBoxCoder(BaseBBoxCoder):
"""

def __init__(self,
target_means=(0., 0., 0., 0.),
target_stds=(1., 1., 1., 1.),
**kwargs):
target_means: Sequence[float] = (0., 0., 0., 0.),
target_stds: Sequence[float] = (1., 1., 1., 1.),
**kwargs) -> None:
super().__init__(**kwargs)
self.means = target_means
self.stds = target_stds

def encode(self, bboxes, gt_bboxes):
def encode(self, bboxes: Union[Tensor, BaseBoxes],
gt_bboxes: Union[Tensor, BaseBoxes]) -> Tensor:
"""Get box regression transformation deltas that can be used to
transform the ``bboxes`` into the ``gt_bboxes``.
Expand All @@ -60,11 +64,14 @@ def encode(self, bboxes, gt_bboxes):
self.stds)
return encoded_bboxes

def decode(self,
bboxes,
pred_bboxes,
max_shape=None,
wh_ratio_clip=16 / 1000):
def decode(
self,
bboxes: Union[Tensor, BaseBoxes],
pred_bboxes: Tensor,
max_shape: Optional[Union[Sequence[int], Tensor,
Sequence[Sequence[int]]]] = None,
wh_ratio_clip: Optional[float] = 16 / 1000
) -> Union[Tensor, BaseBoxes]:
"""Apply transformation `pred_bboxes` to `boxes`.
Args:
Expand All @@ -91,10 +98,12 @@ def decode(self,
return decoded_bboxes


def legacy_bbox2delta(proposals,
gt,
means=(0., 0., 0., 0.),
stds=(1., 1., 1., 1.)):
def legacy_bbox2delta(
proposals: Tensor,
gt: Tensor,
means: Sequence[float] = (0., 0., 0., 0.),
stds: Sequence[float] = (1., 1., 1., 1.)
) -> Tensor:
"""Compute deltas of proposals w.r.t. gt in the MMDet V1.x manner.
We usually compute the deltas of x, y, w, h of proposals w.r.t ground
Expand Down Expand Up @@ -139,12 +148,14 @@ def legacy_bbox2delta(proposals,
return deltas


def legacy_delta2bbox(rois,
deltas,
means=(0., 0., 0., 0.),
stds=(1., 1., 1., 1.),
max_shape=None,
wh_ratio_clip=16 / 1000):
def legacy_delta2bbox(rois: Tensor,
deltas: Tensor,
means: Sequence[float] = (0., 0., 0., 0.),
stds: Sequence[float] = (1., 1., 1., 1.),
max_shape: Optional[
Union[Sequence[int], Tensor,
Sequence[Sequence[int]]]] = None,
wh_ratio_clip: float = 16 / 1000) -> Tensor:
"""Apply deltas to shift/scale base boxes in the MMDet V1.x manner.
Typically the rois are anchor or proposed bounding boxes and the deltas are
Expand Down
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