File size: 8,253 Bytes
e26e560 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 |
import mmcv
import pytest
import torch
from mmdet.core import bbox2roi
from mmdet.models.roi_heads.bbox_heads import BBoxHead
from .utils import _dummy_bbox_sampling
def test_bbox_head_loss():
"""Tests bbox head loss when truth is empty and non-empty."""
self = BBoxHead(in_channels=8, roi_feat_size=3)
# Dummy proposals
proposal_list = [
torch.Tensor([[23.6667, 23.8757, 228.6326, 153.8874]]),
]
target_cfg = mmcv.Config(dict(pos_weight=1))
# Test bbox loss when truth is empty
gt_bboxes = [torch.empty((0, 4))]
gt_labels = [torch.LongTensor([])]
sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes,
gt_labels)
bbox_targets = self.get_targets(sampling_results, gt_bboxes, gt_labels,
target_cfg)
labels, label_weights, bbox_targets, bbox_weights = bbox_targets
# Create dummy features "extracted" for each sampled bbox
num_sampled = sum(len(res.bboxes) for res in sampling_results)
rois = bbox2roi([res.bboxes for res in sampling_results])
dummy_feats = torch.rand(num_sampled, 8 * 3 * 3)
cls_scores, bbox_preds = self.forward(dummy_feats)
losses = self.loss(cls_scores, bbox_preds, rois, labels, label_weights,
bbox_targets, bbox_weights)
assert losses.get('loss_cls', 0) > 0, 'cls-loss should be non-zero'
assert losses.get('loss_bbox', 0) == 0, 'empty gt loss should be zero'
# Test bbox loss when truth is non-empty
gt_bboxes = [
torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
]
gt_labels = [torch.LongTensor([2])]
sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes,
gt_labels)
rois = bbox2roi([res.bboxes for res in sampling_results])
bbox_targets = self.get_targets(sampling_results, gt_bboxes, gt_labels,
target_cfg)
labels, label_weights, bbox_targets, bbox_weights = bbox_targets
# Create dummy features "extracted" for each sampled bbox
num_sampled = sum(len(res.bboxes) for res in sampling_results)
dummy_feats = torch.rand(num_sampled, 8 * 3 * 3)
cls_scores, bbox_preds = self.forward(dummy_feats)
losses = self.loss(cls_scores, bbox_preds, rois, labels, label_weights,
bbox_targets, bbox_weights)
assert losses.get('loss_cls', 0) > 0, 'cls-loss should be non-zero'
assert losses.get('loss_bbox', 0) > 0, 'box-loss should be non-zero'
@pytest.mark.parametrize(['num_sample', 'num_batch'], [[2, 2], [0, 2], [0, 0]])
def test_bbox_head_get_bboxes(num_sample, num_batch):
self = BBoxHead(reg_class_agnostic=True)
num_class = 6
rois = torch.rand((num_sample, 5))
cls_score = torch.rand((num_sample, num_class))
bbox_pred = torch.rand((num_sample, 4))
scale_factor = 2.0
det_bboxes, det_labels = self.get_bboxes(
rois, cls_score, bbox_pred, None, scale_factor, rescale=True)
if num_sample == 0:
assert len(det_bboxes) == 0 and len(det_labels) == 0
else:
assert det_bboxes.shape == bbox_pred.shape
assert det_labels.shape == cls_score.shape
rois = torch.rand((num_batch, num_sample, 5))
cls_score = torch.rand((num_batch, num_sample, num_class))
bbox_pred = torch.rand((num_batch, num_sample, 4))
det_bboxes, det_labels = self.get_bboxes(
rois, cls_score, bbox_pred, None, scale_factor, rescale=True)
assert len(det_bboxes) == num_batch and len(det_labels) == num_batch
def test_refine_boxes():
"""Mirrors the doctest in
``mmdet.models.bbox_heads.bbox_head.BBoxHead.refine_boxes`` but checks for
multiple values of n_roi / n_img."""
self = BBoxHead(reg_class_agnostic=True)
test_settings = [
# Corner case: less rois than images
{
'n_roi': 2,
'n_img': 4,
'rng': 34285940
},
# Corner case: no images
{
'n_roi': 0,
'n_img': 0,
'rng': 52925222
},
# Corner cases: few images / rois
{
'n_roi': 1,
'n_img': 1,
'rng': 1200281
},
{
'n_roi': 2,
'n_img': 1,
'rng': 1200282
},
{
'n_roi': 2,
'n_img': 2,
'rng': 1200283
},
{
'n_roi': 1,
'n_img': 2,
'rng': 1200284
},
# Corner case: no rois few images
{
'n_roi': 0,
'n_img': 1,
'rng': 23955860
},
{
'n_roi': 0,
'n_img': 2,
'rng': 25830516
},
# Corner case: no rois many images
{
'n_roi': 0,
'n_img': 10,
'rng': 671346
},
{
'n_roi': 0,
'n_img': 20,
'rng': 699807
},
# Corner case: cal_similarity num rois and images
{
'n_roi': 20,
'n_img': 20,
'rng': 1200238
},
{
'n_roi': 10,
'n_img': 20,
'rng': 1200238
},
{
'n_roi': 5,
'n_img': 5,
'rng': 1200238
},
# ----------------------------------
# Common case: more rois than images
{
'n_roi': 100,
'n_img': 1,
'rng': 337156
},
{
'n_roi': 150,
'n_img': 2,
'rng': 275898
},
{
'n_roi': 500,
'n_img': 5,
'rng': 4903221
},
]
for demokw in test_settings:
try:
n_roi = demokw['n_roi']
n_img = demokw['n_img']
rng = demokw['rng']
print(f'Test refine_boxes case: {demokw!r}')
tup = _demodata_refine_boxes(n_roi, n_img, rng=rng)
rois, labels, bbox_preds, pos_is_gts, img_metas = tup
bboxes_list = self.refine_bboxes(rois, labels, bbox_preds,
pos_is_gts, img_metas)
assert len(bboxes_list) == n_img
assert sum(map(len, bboxes_list)) <= n_roi
assert all(b.shape[1] == 4 for b in bboxes_list)
except Exception:
print(f'Test failed with demokw={demokw!r}')
raise
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
|