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@@ -76,6 +76,8 @@ def maskrcnn_inference(x, labels):
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"""
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mask_prob = x.sigmoid()
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+
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+
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# select masks corresponding to the predicted classes
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num_masks = x.shape[0]
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boxes_per_image = [label.shape[0] for label in labels]
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@@ -84,6 +86,8 @@ def maskrcnn_inference(x, labels):
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mask_prob = mask_prob[index, labels][:, None]
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mask_prob = mask_prob.split(boxes_per_image, dim=0)
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+
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+
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return mask_prob
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@@ -1449,20 +1453,21 @@ class RoIHeads(nn.Module):
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if feature_logits is not None:
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- circles_probs, circles_scores,circle_points = arc_inference(feature_logits, circle_proposals,th=0)
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+ circles_probs, circles_scores, circle_points = arc_inference(feature_logits,
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+ circle_proposals, th=0)
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# print(f'circles_probs:{circles_probs.shape}, circles_scores:{circles_scores.shape}')
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- proposals_per_image = [box.size(0) for box in circle_proposals]
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+ proposals_per_image = [box.size(0) for box in circle_proposals]
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print(f'circle_proposals_per_image:{proposals_per_image}')
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- feature_logits_props=[]
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+ feature_logits_props = []
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start_idx = 0
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for num_p in proposals_per_image:
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current_features = feature_logits[start_idx:start_idx + num_p]
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merged_feature = torch.sum(current_features, dim=0, keepdim=True)
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feature_logits_props.append(merged_feature)
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+ start_idx += num_p
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-
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-
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- for keypoint_prob, kps, r,f in zip(circles_probs, circles_scores, result,feature_logits_props):
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+ for keypoint_prob, kps, r, f in zip(circles_probs, circles_scores, result,
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+ feature_logits_props):
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r["circles"] = keypoint_prob
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r["circles_scores"] = kps
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print(f'circles feature map:{f.shape}')
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