def random(cls, rng=None, **kwargs):
"""
Args:
rng (None | int | numpy.random.RandomState): seed or state
Kwargs:
num_preds: number of predicted boxes
num_gts: number of true boxes
p_ignore (float): probability of a predicted box assinged to an
ignored truth
p_assigned (float): probability of a predicted box not being
assigned
p_use_label (float | bool): with labels or not
Returns:
AssignResult :
Example:
>>> from mmdet.core.bbox.samplers.sampling_result import * # NOQA
>>> self = SamplingResult.random()
>>> print(self.__dict__)
"""
from mmdet.core.bbox.samplers.random_sampler import RandomSampler
from mmdet.core.bbox.assigners.assign_result import AssignResult
from mmdet.core.bbox import demodata
rng = demodata.ensure_rng(rng)
# make probabalistic?
num = 32
pos_fraction = 0.5
neg_pos_ub = -1
assign_result = AssignResult.random(rng=rng, **kwargs)
# Note we could just compute an assignment
bboxes = demodata.random_boxes(assign_result.num_preds, rng=rng)
gt_bboxes = demodata.random_boxes(assign_result.num_gts, rng=rng)
if rng.rand() > 0.2:
# sometimes algorithms squeeze their data, be robust to that
gt_bboxes = gt_bboxes.squeeze()
bboxes = bboxes.squeeze()
if assign_result.labels is None:
gt_labels = None
else:
gt_labels = None # todo
if gt_labels is None:
add_gt_as_proposals = False
else:
add_gt_as_proposals = True # make probabalistic?
sampler = RandomSampler(num,
pos_fraction,
neg_pos_ubo=neg_pos_ub,
add_gt_as_proposals=add_gt_as_proposals,
rng=rng)
self = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels)
return self
def __init__(self,
num,
pos_fraction,
neg_pos_ub=-1,
add_gt_as_proposals=True,
**kwargs):
from mmdet.core.bbox import demodata
super(RandomSampler, self).__init__(num, pos_fraction, neg_pos_ub,
add_gt_as_proposals)
self.rng = demodata.ensure_rng(kwargs.get('rng', None))
def _demodata_refine_boxes(n_roi, n_img, rng=0):
"""
Create random test data for the
``mmdet.models.bbox_heads.bbox_head.BBoxHead.refine_boxes`` method
"""
import numpy as np
from mmdet.core.bbox.demodata import random_boxes
from mmdet.core.bbox.demodata import ensure_rng
try:
import kwarray
except ImportError:
import pytest
pytest.skip("kwarray is required for this test")
scale = 512
rng = ensure_rng(rng)
img_metas = [{"img_shape": (scale, scale)} for _ in range(n_img)]
# Create rois in the expected format
roi_boxes = random_boxes(n_roi, scale=scale, rng=rng)
if n_img == 0:
assert n_roi == 0, "cannot have any rois if there are no images"
img_ids = torch.empty((0, ), dtype=torch.long)
roi_boxes = torch.empty((0, 4), dtype=torch.float32)
else:
img_ids = rng.randint(0, n_img, (n_roi, ))
img_ids = torch.from_numpy(img_ids)
rois = torch.cat([img_ids[:, None].float(), roi_boxes], dim=1)
# Create other args
labels = rng.randint(0, 2, (n_roi, ))
labels = torch.from_numpy(labels).long()
bbox_preds = random_boxes(n_roi, scale=scale, rng=rng)
# For each image, pretend random positive boxes are gts
is_label_pos = (labels.numpy() > 0).astype(np.int)
lbl_per_img = kwarray.group_items(is_label_pos, img_ids.numpy())
pos_per_img = [sum(lbl_per_img.get(gid, [])) for gid in range(n_img)]
# randomly generate with numpy then sort with torch
_pos_is_gts = [
rng.randint(0, 2, (npos, )).astype(np.uint8) for npos in pos_per_img
]
pos_is_gts = [
torch.from_numpy(p).sort(descending=True)[0] for p in _pos_is_gts
]
return rois, labels, bbox_preds, pos_is_gts, img_metas
def random(cls, **kwargs):
"""
Create random AssignResult for tests or debugging.
Kwargs:
num_preds: number of predicted boxes
num_gts: number of true boxes
p_ignore (float): probability of a predicted box assinged to an
ignored truth
p_assigned (float): probability of a predicted box not being
assigned
p_use_label (float | bool): with labels or not
rng (None | int | numpy.random.RandomState): seed or state
Returns:
AssignResult :
Example:
>>> from mmdet.core.bbox.assigners.assign_result import * # NOQA
>>> self = AssignResult.random()
>>> print(self.info)
"""
from mmdet.core.bbox import demodata
rng = demodata.ensure_rng(kwargs.get('rng', None))
num_gts = kwargs.get('num_gts', None)
num_preds = kwargs.get('num_preds', None)
p_ignore = kwargs.get('p_ignore', 0.3)
p_assigned = kwargs.get('p_assigned', 0.7)
p_use_label = kwargs.get('p_use_label', 0.5)
num_classes = kwargs.get('p_use_label', 3)
if num_gts is None:
num_gts = rng.randint(0, 8)
if num_preds is None:
num_preds = rng.randint(0, 16)
if num_gts == 0:
max_overlaps = torch.zeros(num_preds, dtype=torch.float32)
gt_inds = torch.zeros(num_preds, dtype=torch.int64)
if p_use_label is True or p_use_label < rng.rand():
labels = torch.zeros(num_preds, dtype=torch.int64)
else:
labels = None
else:
import numpy as np
# Create an overlap for each predicted box
max_overlaps = torch.from_numpy(rng.rand(num_preds))
# Construct gt_inds for each predicted box
is_assigned = torch.from_numpy(rng.rand(num_preds) < p_assigned)
# maximum number of assignments constraints
n_assigned = min(num_preds, min(num_gts, is_assigned.sum()))
assigned_idxs = np.where(is_assigned)[0]
rng.shuffle(assigned_idxs)
assigned_idxs = assigned_idxs[0:n_assigned]
assigned_idxs.sort()
is_assigned[:] = 0
is_assigned[assigned_idxs] = True
is_ignore = torch.from_numpy(
rng.rand(num_preds) < p_ignore) & is_assigned
gt_inds = torch.zeros(num_preds, dtype=torch.int64)
true_idxs = np.arange(num_gts)
rng.shuffle(true_idxs)
true_idxs = torch.from_numpy(true_idxs)
gt_inds[is_assigned] = true_idxs[:n_assigned]
gt_inds = torch.from_numpy(
rng.randint(1, num_gts + 1, size=num_preds))
gt_inds[is_ignore] = -1
gt_inds[~is_assigned] = 0
max_overlaps[~is_assigned] = 0
if p_use_label is True or p_use_label < rng.rand():
if num_classes == 0:
labels = torch.zeros(num_preds, dtype=torch.int64)
else:
labels = torch.from_numpy(
rng.randint(1, num_classes + 1, size=num_preds))
labels[~is_assigned] = 0
else:
labels = None
self = cls(num_gts, gt_inds, max_overlaps, labels)
return self
