调试

app/controller/AlgorithmController.py

@@ -249,7 +249,6 @@ def train_R0DY(params_str, id):
     params.read_from_str(params_str)
     print(params.get('device').default)
     data_list = file_tool.get_file(ori_path=params.get('DatasetDir').value, type_list=params.get('CLASS_NAMES').value)
-    print('---------------------',data_list)
     weights = params.get('resumeModPath').value  # 初始化模型绝对路径
     img_size = params.get('img_size').value
     savemodel = os.path.splitext(params.get('saveModDir').value)[0] + '_' + str(img_size) + '.pt'  # 模型命名加上图像参数

app/yolov5/train_server.py

@@ -77,7 +77,6 @@ def yaml_rewrite(file='data.yaml',data_list=[]):
         f.close()
     classes = class_dict["classes"]
     nc, names = (len(classes), classes)
-    print("names##############",names)
     coco_dict['train'] = data_list[0]
     coco_dict['val'] = data_list[1]
     label_name = {}
@@ -85,7 +84,6 @@ def yaml_rewrite(file='data.yaml',data_list=[]):
         label_name[i] = names[i]
     #print(label_name)
     coco_dict['names'] = label_name
-    print('coco_names################:',coco_dict['names'])
     with open(file, 'w') as f:
         yaml.safe_dump(coco_dict, f, sort_keys=False)
 
@@ -306,7 +304,7 @@ def train(hyp, opt, device, data_list,id,callbacks):  # hyp is path/to/hyp.yaml
                     num_train_img=train_num,
                     train_mod_savepath=best)
 
-    @algorithm_process_value_websocket()
+    # @algorithm_process_value_websocket()
     def report_cellback(i, num_epochs, reportAccu):
         report.rate_of_progess = ((i + 1) / num_epochs) * 100
         report.progress = (i + 1)

app/yolov5/utils/dataloaders.py

@@ -428,13 +428,11 @@ class LoadImagesAndLabels(Dataset):
 
         # Check cache
         self.label_files = img2label_paths(self.im_files)  # labels
-        print('labelfile############',self.label_files)
         cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache')
         try:
             cache, exists = np.load(cache_path, allow_pickle=True).item(), True  # load dict
             assert cache['version'] == self.cache_version  # matches current version
-            assert cache['hash'] == get_hash(self.label_files + self.im_files)  # identical hash
-            print('load_cache#########')
+            assert cache['hash'] == get_hash(self.label_files + self.im_files)  # identical has
         except Exception:
             cache, exists = self.cache_labels(cache_path, prefix), False  # run cache ops
 
@@ -449,7 +447,6 @@ class LoadImagesAndLabels(Dataset):
 
         # Read cache
         [cache.pop(k) for k in ('hash', 'version', 'msgs')]  # remove items
-        print('@@@@@@@@@@@@@@@@@@@@@')
         labels, shapes, self.segments = zip(*cache.values())
         nl = len(np.concatenate(labels, 0))  # number of labels
         assert nl > 0 or not augment, f'{prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}'