不使用line

models/line_detect/line_predictor.py

@@ -19,7 +19,7 @@ from models.config.config_tool import read_yaml
 import numpy as np
 import torch.nn.functional as F
 
-from scipy.ndimage import gaussian_filter
+# from scipy.ndimage import gaussian_filter
 
 FEATURE_DIM = 8
 
@@ -308,6 +308,11 @@ class LineRCNNPredictor(nn.Module):
             # if mode != "training":
             if not self.training:
                 K = min(int((jmap > self.eval_junc_thres).float().sum().item()), max_K)
+                print(f'jmap max:{torch.max(jmap[0])}')
+                print(f'jmap min:{torch.min(jmap[0])}')
+                print(f'jmap num:{(jmap > self.eval_junc_thres).float().sum().item()}')
+                print(f'jmap:{jmap}')
+                print(f'K:{K}')
             else:
                 K = min(int(N * 2 + 2), max_K)
             if K < 2:

models/line_detect/roi_heads.py

@@ -146,7 +146,6 @@ def line_vectorizer_loss(result, x, ys, idx, jcs, n_batch, ps, n_out_line, n_out
     return result
 
 
-
 def wirepoint_head_line_loss(targets, output, x, y, idx, loss_weight):
     # output, feature: head返回结果
     # x, y, idx : line中间生成结果
@@ -1054,7 +1053,8 @@ class RoIHeads(nn.Module):
         features_lcnn = features['0']
         if self.has_line():
             # print('has line_head')
-            outputs = self.line_head(features_lcnn)
+            # outputs = self.line_head(features_lcnn)
+            outputs = features_lcnn[:, 0:5, :, :]
             loss_weight = {'junc_map': 8.0, 'line_map': 0.5, 'junc_offset': 0.25, 'lpos': 1, 'lneg': 1}
             x, y, idx, jcs, n_batch, ps, n_out_line, n_out_junc = self.line_predictor(
                 inputs=outputs, features=features_lcnn, targets=targets)
@@ -1082,8 +1082,6 @@ class RoIHeads(nn.Module):
             pass
             # print('has not line_head')
 
-
-
         if self.has_mask():
             mask_proposals = [p["boxes"] for p in result]
             if self.training:

models/line_detect/train.yaml

@@ -1,6 +1,6 @@
 io:
   logdir: logs/
-  datadir: D:\all\1Desktop\20250320data\0322_
+  datadir: /root/LineDetect/models/line_detect/train_results/20250414_113846
 #  datadir: I:\datasets\wirenet_1000
   resume_from:
   num_workers: 8