调试
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@ -242,7 +242,7 @@ from app import file_tool
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# 启动训练
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# 启动训练
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@start_train_algorithm()
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#@start_train_algorithm()
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def train_R0DY(params_str, id):
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def train_R0DY(params_str, id):
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from app.yolov5.train_server import train_start
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from app.yolov5.train_server import train_start
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params = TrainParams()
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params = TrainParams()
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@ -314,7 +314,7 @@ def train_R0DY(params_str, id):
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# zip_outputPath = os.path.join(exp_outputPath, "inference_model.zip")
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# zip_outputPath = os.path.join(exp_outputPath, "inference_model.zip")
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@obtain_train_param()
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#@obtain_train_param()
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def returnTrainParams():
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def returnTrainParams():
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# nvmlInit()
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# nvmlInit()
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# gpuDeviceCount = nvmlDeviceGetCount() # 获取Nvidia GPU块数
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# gpuDeviceCount = nvmlDeviceGetCount() # 获取Nvidia GPU块数
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@ -338,7 +338,7 @@ def returnTrainParams():
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{"index": 7, "name": "CLASS_NAMES", "value": ['hole', '456'], "description": '类别名称', "default": '', "type": "L",
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{"index": 7, "name": "CLASS_NAMES", "value": ['hole', '456'], "description": '类别名称', "default": '', "type": "L",
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"items": '',
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"items": '',
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'show': False},
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'show': False},
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{"index": 8, "name": "DatasetDir", "value": "E:/aicheck/data_set/11442136178662604800/ori/",
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{"index": 8, "name": "DatasetDir", "value": "E:/aicheck/data_set/11442136178662604800/ori",
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"description": '数据集路径',
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"description": '数据集路径',
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"default": "./app/maskrcnn/datasets/test", "type": "S", 'show': False} # ORI_PATH
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"default": "./app/maskrcnn/datasets/test", "type": "S", 'show': False} # ORI_PATH
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]
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]
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@ -179,7 +179,7 @@ def get_file(ori_path: str, type_list: Union[object,str]):
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test_files = []
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test_files = []
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# 训练、测试比例强制9:1
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# 训练、测试比例强制9:1
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for img in imgs[0:1]:
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for img in imgs[0:1]:
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path = ori_path + '/images/' +img
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path = ori_path + '/images/' +img #'/images/'
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# print(os.path.exists(path))
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# print(os.path.exists(path))
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print('图像路径',path)
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print('图像路径',path)
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if os.path.exists(path):
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if os.path.exists(path):
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@ -187,7 +187,7 @@ def get_file(ori_path: str, type_list: Union[object,str]):
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print('1111')
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print('1111')
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#label = ori_path + 'labels/' + os.path.split(path)[1]
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#label = ori_path + 'labels/' + os.path.split(path)[1]
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(filename1, extension) = os.path.splitext(img) # 文件名与后缀名分开
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(filename1, extension) = os.path.splitext(img) # 文件名与后缀名分开
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label = ori_path + '/labels/' + filename1 + '.json'
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label = ori_path + '/labels/' + filename1 + '.json' #'/labels/'
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print('标签',label)
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print('标签',label)
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if label is not None:
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if label is not None:
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#train_files.append(label)
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#train_files.append(label)
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@ -3,5 +3,5 @@ train: E:/aicheck/data_set/11442136178662604800/trained/images/train/
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val: E:/aicheck/data_set/11442136178662604800/trained/images/val/
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val: E:/aicheck/data_set/11442136178662604800/trained/images/val/
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test: null
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test: null
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names:
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names:
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0: logo
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0: hole
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1: 3C
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1: '456'
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@ -304,7 +304,7 @@ def train(hyp, opt, device, data_list,id,callbacks): # hyp is path/to/hyp.yaml
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num_train_img=train_num,
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num_train_img=train_num,
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train_mod_savepath=best)
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train_mod_savepath=best)
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# @algorithm_process_value_websocket()
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@algorithm_process_value_websocket()
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def report_cellback(i, num_epochs, reportAccu):
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def report_cellback(i, num_epochs, reportAccu):
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report.rate_of_progess = ((i + 1) / num_epochs) * 100
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report.rate_of_progess = ((i + 1) / num_epochs) * 100
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report.progress = (i + 1)
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report.progress = (i + 1)
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@ -470,7 +470,7 @@ def train(hyp, opt, device, data_list,id,callbacks): # hyp is path/to/hyp.yaml
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print('##############',best)
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print('##############',best)
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for f in best:
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for f in best:
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print('##################',f)
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print('##################',f)
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if os.path.exists(f):
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if os.path.exists(best):
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strip_optimizer(f) # strip optimizers
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strip_optimizer(f) # strip optimizers
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if f is best:
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if f is best:
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LOGGER.info(f'\nValidating {f}...')
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LOGGER.info(f'\nValidating {f}...')
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