def test_decode_centernet():
print("output: heatmaps", Y[0]["hm"].shape)
print("output: wh_scale", Y[0]["wh"].shape)
print("output: xy_offset", Y[0]["reg"].shape)
heatmaps, scale, offset = Y[0]["hm"], Y[0]["wh"], Y[0]["reg"]
detections = decode_centernet(heat=heatmaps, wh=scale, reg=offset, cat_spec_wh=False, K=10, flag_split=False)
print(detections.shape)
def process(self, images, return_time=False):
output = self.model(images)[-1]
heatmaps = output["hm"].sigmoid()
wh = output["wh"]
reg = output["reg"] if self.opt.reg_offset else None
if self.opt.flip_test:
heatmaps = (heatmaps[0:1] + flip_tensor(heatmaps[1:2])) / 2
wh = (wh[0:1] + flip_tensor(wh[1:2])) / 2
reg = reg[0:1] if reg is not None else None
nd.waitall()
forward_time = time.time()
dets = decode_centernet(heatmaps, wh, reg, K=self.opt.K)
if return_time:
return output, dets, forward_time
else:
return output, dets
def validate(model, val_loader, ctx, eval_metric):
"""Test on validation dataset."""
eval_metric.reset()
for batch in val_loader:
data = gluon.utils.split_and_load(batch[0], ctx_list=ctx, batch_axis=0, even_split=False)
label = gluon.utils.split_and_load(batch[1], ctx_list=ctx, batch_axis=0, even_split=False)
det_bboxes = []
det_ids = []
det_scores = []
gt_bboxes = []
gt_ids = []
gt_difficults = []
for x, y in zip(data, label):
#print("x.shape", x.shape)
#print("x element value: ", x[0, 0, 128, 128])
# get prediction results
pred = model(x)
heatmaps, scale, offset = pred[-1]["hm"], pred[-1]["wh"], pred[-1]["reg"] # 2nd stack hourglass result
#DEBUGING decode_centernet:
bboxes, scores, ids = decode_centernet(heat=heatmaps, wh=scale, reg=offset, flag_split=True)
num_gt_bboxes = y.shape[1]
bboxes = bboxes[:, 0:num_gt_bboxes, :]
scores = scores[:, 0:num_gt_bboxes, :]
ids = ids[:, 0:num_gt_bboxes, :]
print("Top-k, bbox: {}, scores: {}, id {}, gt_bbox: {}, gt_id: {}".format(bboxes[0, :, :], scores[0, :, :], ids[0, :, :], y[0, :, 0:4], y[0, :, 4]))
det_ids.append(ids)
det_scores.append(scores)
# clip to image size
det_bboxes.append(bboxes.clip(0, batch[0].shape[2]))
# split ground truths
gt_bboxes.append(y.slice_axis(axis=-1, begin=0, end=4))
gt_ids.append(y.slice_axis(axis=-1, begin=4, end=5))
gt_difficults.append(y.slice_axis(axis=-1, begin=5, end=6) if y.shape[-1] > 5 else None)
# update metric
eval_metric.update(det_bboxes, det_ids, det_scores, gt_bboxes, gt_ids, gt_difficults)
return eval_metric.get()