def __call__(self, detections):
batches_scores, batches_boxes = detections
device = batches_scores.device
batch_size = batches_scores.size(0)
results = []
for batch_id in range(batch_size):
scores, boxes = batches_scores[batch_id], batches_boxes[
batch_id] # (N, #CLS) (N, 4)
num_boxes = scores.shape[0]
num_classes = scores.shape[1]
boxes = boxes.view(num_boxes, 1, 4).expand(num_boxes, num_classes,
4)
labels = torch.arange(num_classes, device=device)
labels = labels.view(1, num_classes).expand_as(scores)
# remove predictions with the background label
if self.cls_loss != 'FocalLoss':
boxes = boxes[:, 1:]
scores = scores[:, 1:]
labels = labels[:, 1:]
# batch everything, by making every class prediction be a separate instance
boxes = boxes.reshape(-1, 4)
scores = scores.reshape(-1)
labels = labels.reshape(-1)
# remove low scoring boxes
indices = torch.nonzero(
scores > self.cfg.TEST.CONFIDENCE_THRESHOLD).squeeze(1)
boxes, scores, labels = boxes[indices], scores[indices], labels[
indices]
boxes[:, 0::2] *= self.width
boxes[:, 1::2] *= self.height
keep = batched_nms(boxes, scores, labels,
self.cfg.TEST.NMS_THRESHOLD)
# keep only topk scoring predictions
keep = keep[:self.cfg.TEST.MAX_PER_IMAGE]
if self.cls_loss == 'FocalLoss':
boxes, scores, labels = boxes[keep], scores[
keep], labels[keep] + 1
else:
boxes, scores, labels = boxes[keep], scores[keep], labels[keep]
container = Container(boxes=boxes, labels=labels, scores=scores)
container.img_width = self.width
container.img_height = self.height
results.append(container)
return results
def __call__(self, detections):
batches_scores, batches_boxes = detections
batchb = len(batches_boxes)
#print("batch boxes: %d" % (batchb))
device = batches_scores.device
batch_size = batches_scores.size(0)
results = []
for batch_id in range(batch_size):
scores, boxes = batches_scores[batch_id], batches_boxes[
batch_id] # (N, #CLS) (N, 4)
num_boxes = scores.shape[0]
num_classes = scores.shape[1]
#print(num_boxes)
boxes = boxes.view(num_boxes, 1, 4).expand(num_boxes, num_classes,
4)
labels = torch.arange(num_classes, device=device)
labels = labels.view(1, num_classes).expand_as(scores)
_t = {'nms': Timer()}
_t['nms'].tic()
# remove predictions with the background label
boxes = boxes[:, 1:]
scores = scores[:, 1:]
labels = labels[:, 1:]
# batch everything, by making every class prediction be a separate instance
boxes = boxes.reshape(-1, 4)
scores = scores.reshape(-1)
labels = labels.reshape(-1)
# remove low scoring boxes
indices = torch.nonzero(
scores > self.cfg.TEST.CONFIDENCE_THRESHOLD).squeeze(1)
boxes, scores, labels = boxes[indices], scores[indices], labels[
indices]
boxes[:, 0::2] *= self.width
boxes[:, 1::2] *= self.height
bb = len(boxes)
#print("before boxes: %d" % (bb))
keep = batched_nms(boxes, scores, labels,
self.cfg.TEST.NMS_THRESHOLD)
#keep = nms(boxes, scores,self.cfg.TEST.NMS_THRESHOLD)
# keep only topk scoring predictions
keep = keep[:self.cfg.TEST.MAX_PER_IMAGE]
boxes, scores, labels = boxes[keep], scores[keep], labels[keep]
ab = len(boxes)
#print("after boxes: %d" % (ab))
#print(ab)
container = Container(boxes=boxes, labels=labels, scores=scores)
container.img_width = self.width
container.img_height = self.height
results.append(container)
nms_time = _t['nms'].toc()
#print("nms time: %.2f ms" % (1000*nms_time))
print((1000 * nms_time))
return results
