def test():
for i in range(1000):
a = torch.rand(4, 1000, device='cuda:0')
b = torch.rand(4, 1000, device='cuda:0')
ele = utils.elem_iou(a, b)
tab = utils.calc_iou(a, b)
if (ele != tab.diag()).any():
print('WARNING, found diff')
print(ele.tolist())
print(tab.tolist())
def test_iou_performance():
# "for VOC 5000 images, we only need to do calc_iou 5000 * 16 * 2"
anchor_creator = region.AnchorCreator(device=torch.device('cuda:0'))
anchors = anchor_creator((600, 800), (37, 50)).view(4, -1)
bbox = anchors[:, :5]
print(anchors.shape)
print(bbox.shape)
start = time.time()
for i in range(80000*2):
iou_tab = utils.calc_iou(anchors, bbox)
print(time.time() - start)
def test_anchor_target_creator():
bbox = torch.tensor([[115, 131, 613, 513], [230, 53, 460, 506],
[643, 133, 697, 272], [706, 158, 800, 257]],
device=torch.device('cuda:0'))
bbox = bbox.t()
img_size = (600, 800)
feat_size = (37, 50)
anchor_creator = region.AnchorCreator(device=torch.device('cuda:0'))
anchors = anchor_creator(img_size, feat_size).view(4, -1)
in_index = region.find_inside_index(anchors, img_size)
in_anchors = anchors[:, in_index]
anchor_tar_creator = region.AnchorTargetCreator()
print('in_anchors.shape', in_anchors.shape)
labels, param, bbox_labels = anchor_tar_creator(img_size, feat_size,
in_anchors, bbox)
print('1 labels:', (labels == 1).sum())
print('0 labels:', (labels == 0).sum())
print('-1 labels:', (labels == -1).sum())
pos_index = utils.index_of(labels == 1)
pos_anchors = in_anchors[:, pos_index[0]]
pos_bbox_labels = bbox_labels[:, pos_index[0]]
print('pos_anchors:', pos_anchors, pos_anchors.shape)
print('pos_bbox_labels:', pos_bbox_labels, pos_bbox_labels.shape)
ious = utils.calc_iou(pos_anchors, pos_bbox_labels)
print('check ious:', ious.tolist())
print('check ious diag:', ious.diag())
print('Next test performance')
img_size2 = (605, 805)
start = time.time()
for i in range(5000):
if i % 2 == 0:
labels, param, bbox_labels = anchor_tar_creator(
img_size, feat_size, in_anchors, bbox)
else:
labels, param, bbox_labels = anchor_tar_creator(
img_size2, feat_size, in_anchors, bbox)
used = time.time() - start
print('used {} secs'.format(used))
def test_iou():
bbox = torch.tensor([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], dtype=torch.float32)
a = torch.tensor([[1],[4],[7],[10]], dtype=torch.float32)
b = torch.tensor([[2],[5],[8],[11]], dtype=torch.float32)
print(utils.calc_iou(bbox, bbox))
)
X, Y, img_data, debug_img, debug_num_pos = next(data_gen_train)
loss_rpn = model_rpn.train_on_batch(X, Y)
P_rpn = model_rpn.predict_on_batch(X)
R = utils.rpn_to_roi(P_rpn[0],
P_rpn[1],
C,
K.image_dim_ordering(),
use_regr=True,
overlap_thresh=0.7,
max_boxes=300)
# note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
X2, Y1, Y2, IouS = utils.calc_iou(R, img_data, C, class_mapping)
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
rpn_accuracy_for_epoch.append(0)
continue
neg_samples = np.where(Y1[0, :, -1] == 1)
pos_samples = np.where(Y1[0, :, -1] == 0)
if len(neg_samples) > 0:
neg_samples = neg_samples[0]
else:
neg_samples = []
if len(pos_samples) > 0: