def run_check_nms():
#test nms:
H, W = 480, 640
num_objects = 4
rois = []
for n in range(num_objects):
w = np.random.randint(64, 256)
h = np.random.randint(64, 256)
x0 = np.random.randint(0, W - w)
y0 = np.random.randint(0, H - h)
x1 = x0 + w
y1 = y0 + h
gt = [x0, y0, x1, y1]
M = np.random.randint(10, 20)
for m in range(M):
dw = int(np.random.uniform(0.5, 2) * w)
dh = int(np.random.uniform(0.5, 2) * h)
dx = int(np.random.uniform(-1, 1) * w * 0.5)
dy = int(np.random.uniform(-1, 1) * h * 0.5)
xx0 = x0 - dw // 2 + dx
yy0 = y0 - dh // 2 + dy
xx1 = xx0 + w + dw
yy1 = yy0 + h + dh
score = np.random.uniform(0.5, 2)
rois.append([xx0, yy0, xx1, yy1, score])
pass
rois = np.array(rois).astype(np.float32)
if 1:
keep = gpu_nms(rois, 0.5)
print('gpu_nms :', keep)
keep = cython_nms(rois, 0.5)
print('cython_nms :', keep)
#gpu [52, 39, 48, 43, 47, 21, 9, 6, 32, 8, 16, 36, 28, 29, 53, 41]
#py [52, 39, 48, 43, 47, 21, 9, 6, 32, 8, 16, 36, 28, 29, 53, 41]
#cython [52, 39, 48, 43, 47, 21, 9, 6, 32, 8, 16, 36, 28, 29, 53, 41]
if 1:
rois = torch.from_numpy(rois).cuda()
keep = torch_nms(rois, 0.5)
keep = keep.cpu().numpy()
print('torch_nms :', keep)
def rpn_nms(cfg, mode, inputs, window, logits_flat, deltas_flat):
if mode in [
'train',
]:
nms_pre_score_threshold = cfg.rpn_train_nms_pre_score_threshold
nms_overlap_threshold = cfg.rpn_train_nms_overlap_threshold
nms_min_size = cfg.rpn_train_nms_min_size
elif mode in [
'eval',
'valid',
'test',
]:
nms_pre_score_threshold = cfg.rpn_test_nms_pre_score_threshold
nms_overlap_threshold = cfg.rpn_test_nms_overlap_threshold
nms_min_size = cfg.rpn_test_nms_min_size
if mode in ['eval']:
nms_pre_score_threshold = 0.05 # set low numbe r to make roc curve.
else:
raise ValueError('rpn_nms(): invalid mode = %s?' % mode)
logits = logits_flat.data.cpu().numpy()
deltas = deltas_flat.data.cpu().numpy()
batch_size, _, height, width = inputs.size()
num_classes = cfg.num_classes
proposals = []
for b in range(batch_size):
proposal = [
np.empty((0, 7), np.float32),
]
ps = np_softmax(logits[b])
ds = deltas[b]
for c in range(1, num_classes): #skip background #num_classes
index = np.where(ps[:, c] > nms_pre_score_threshold)[0]
if len(index) > 0:
p = ps[index, c].reshape(-1, 1)
d = ds[index, c]
w = window[index]
box = rpn_decode(w, d)
box = clip_boxes(box, width, height)
keep = filter_boxes(box, min_size=nms_min_size)
if len(keep) > 0:
box = box[keep]
p = p[keep]
keep = gpu_nms(np.hstack((box, p)), nms_overlap_threshold)
prop = np.zeros((len(keep), 7), np.float32)
prop[:, 0] = b
prop[:, 1:5] = np.around(box[keep], 0)
prop[:, 5] = p[keep, 0]
prop[:, 6] = c
proposal.append(prop)
proposal = np.vstack(proposal)
proposals.append(proposal)
proposals = Variable(torch.from_numpy(np.vstack(proposals))).cuda()
return proposals