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Fix error when no detection left after refinement #43

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Fix error when no detection left after refinement #43

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11 changes: 8 additions & 3 deletions model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -783,7 +783,10 @@ def refine_detections(rois, probs, deltas, window, config):
# Filter out low confidence boxes
if config.DETECTION_MIN_CONFIDENCE:
keep_bool = keep_bool & (class_scores >= config.DETECTION_MIN_CONFIDENCE)
keep = torch.nonzero(keep_bool)[:,0]
detections = torch.nonzero(keep_bool)
if not len(detections):
return
keep = detections[:,0]

# Apply per-class NMS
pre_nms_class_ids = class_ids[keep.data]
Expand Down Expand Up @@ -1596,7 +1599,8 @@ def detect(self, images):

# Run object detection
detections, mrcnn_mask = self.predict([molded_images, image_metas], mode='inference')

if detections is None:
return
# Convert to numpy
detections = detections.data.cpu().numpy()
mrcnn_mask = mrcnn_mask.permute(0, 1, 3, 4, 2).data.cpu().numpy()
Expand Down Expand Up @@ -1671,7 +1675,8 @@ def set_bn_eval(m):
# Detections
# output is [batch, num_detections, (y1, x1, y2, x2, class_id, score)] in image coordinates
detections = detection_layer(self.config, rpn_rois, mrcnn_class, mrcnn_bbox, image_metas)

if detections is None:
return None, None
# Convert boxes to normalized coordinates
# TODO: let DetectionLayer return normalized coordinates to avoid
# unnecessary conversions
Expand Down