Merge branch 'master' of https://gitea.star-rising.cn/xkrs_manan/RODY
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commit
60db36c854
@ -242,7 +242,7 @@ from app import file_tool
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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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from app.yolov5.train_server import train_start
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params = TrainParams()
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@ -261,60 +261,60 @@ def train_R0DY(params_str, id):
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# 启动验证程序
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# @start_test_algorithm()
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# def validate_RODY(params_str, id):
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# from app.yolov5.validate_server import validate_start
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# params = TrainParams()
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# params.read_from_str(params_str)
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# weights = params.get('modPath').value # 验证模型绝对路径
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@start_test_algorithm()
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def validate_RODY(params_str, id):
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from app.yolov5.validate_server import validate_start
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params = TrainParams()
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params.read_from_str(params_str)
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weights = params.get('modPath').value # 验证模型绝对路径
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(filename, extension) = os.path.splitext(weights) # 文件名与后缀名分开
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img_size = int(filename.split('ROD')[1].split('_')[2]) # 获取图像参数
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# v_num = int(filename.split('ROD')[1].split('_')[1]) #获取版本号
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output = params.get('outputPath').value
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batch_size = params.get('batch_size').default
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device = params.get('device').value
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validate_start(weights, img_size, batch_size, device, output, id)
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@start_detect_algorithm()
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def detect_RODY(params_str, id):
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from app.yolov5.detect_server import detect_start
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params = TrainParams()
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params.read_from_str(params_str)
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weights = params.get('modPath').value # 检测模型绝对路径
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input = params.get('inputPath').value
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outpath = params.get('outputPath').value
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# (filename, extension) = os.path.splitext(weights) # 文件名与后缀名分开
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# img_size = int(filename.split('ROD')[1].split('_')[2]) #获取图像参数
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# # v_num = int(filename.split('ROD')[1].split('_')[1]) #获取版本号
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# output = params.get('outputPath').value
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# v_num = int(filename.split('ROD')[1].split('_')[1]) #获取版本号
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# batch_size = params.get('batch_size').default
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# device = params.get('device').value
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#
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# validate_start(weights, img_size, batch_size, device, output, id)
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#
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#
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# @start_detect_algorithm()
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# def detect_RODY(params_str, id):
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# from app.yolov5.detect_server import detect_start
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# params = TrainParams()
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# params.read_from_str(params_str)
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# weights = params.get('modPath').value # 检测模型绝对路径
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# input = params.get('inputPath').value
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# outpath = params.get('outputPath').value
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# # (filename, extension) = os.path.splitext(weights) # 文件名与后缀名分开
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# # img_size = int(filename.split('ROD')[1].split('_')[2]) #获取图像参数
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# # v_num = int(filename.split('ROD')[1].split('_')[1]) #获取版本号
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# # batch_size = params.get('batch_size').default
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# device = params.get('device').value
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#
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# detect_start(input, weights, outpath, device, id)
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#
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#
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# @start_download_pt()
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# def Export_model_RODY(params_str):
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# from app.yolov5.export import Start_Model_Export
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# import zipfile
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# params = TrainParams()
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# params.read_from_str(params_str)
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# exp_inputPath = params.get('exp_inputPath').value # 模型路径
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# exp_device = params.get('device').value
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# modellist = Start_Model_Export(exp_inputPath, exp_device)
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# exp_outputPath = exp_inputPath.replace('pt', 'zip') # 压缩文件
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# zipf = zipfile.ZipFile(exp_outputPath, 'w')
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# for file in modellist:
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# zipf.write(file, arcname=Path(file).name) # 将torchscript和onnx模型压缩
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#
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# return exp_outputPath
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# zipf.write(modellist[1], arcname=modellist[1])
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# zip_inputpath = os.path.join(exp_outputPath, "inference_model")
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# zip_outputPath = os.path.join(exp_outputPath, "inference_model.zip")
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device = params.get('device').value
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detect_start(input, weights, outpath, device, id)
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#@obtain_train_param()
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@start_download_pt()
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def Export_model_RODY(params_str):
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from app.yolov5.export import Start_Model_Export
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import zipfile
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params = TrainParams()
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params.read_from_str(params_str)
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exp_inputPath = params.get('exp_inputPath').value # 模型路径
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exp_device = params.get('device').value
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modellist = Start_Model_Export(exp_inputPath, exp_device)
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exp_outputPath = exp_inputPath.replace('pt', 'zip') # 压缩文件
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zipf = zipfile.ZipFile(exp_outputPath, 'w')
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for file in modellist:
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zipf.write(file, arcname=Path(file).name) # 将torchscript和onnx模型压缩
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return exp_outputPath
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zipf.write(modellist[1], arcname=modellist[1])
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zip_inputpath = os.path.join(exp_outputPath, "inference_model")
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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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def returnTrainParams():
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# nvmlInit()
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# gpuDeviceCount = nvmlDeviceGetCount() # 获取Nvidia GPU块数
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@ -347,63 +347,63 @@ def returnTrainParams():
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return params_str
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# @obtain_test_param()
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# def returnValidateParams():
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# # nvmlInit()
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# # gpuDeviceCount = nvmlDeviceGetCount() # 获取Nvidia GPU块数
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# # _kernel = [f"cuda:{a}" for a in range(gpuDeviceCount)]
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# params_list = [
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# {"index": 0, "name": "modPath", "value": "E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt",
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# "description": '验证模型路径', "default": "./app/maskrcnn/saved_model/test.pt", "type": "S", 'show': False},
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# {"index": 1, "name": "batch_size", "value": 1, "description": '批次图像数量', "default": 1, "type": "I",
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# 'show': False},
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# {"index": 2, "name": "img_size", "value": 640, "description": '训练图像大小', "default": 640, "type": "I",
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# 'show': False},
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# {"index": 3, "name": "outputPath", "value": 'E:/aicheck/data_set/11442136178662604800/val_results/',
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# "description": '输出结果路径',
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# "default": './app/maskrcnn/datasets/M006B_waibi/res', "type": "S", 'show': False},
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# {"index": 4, "name": "device", "value": "0", "description": '训练核心', "default": "cuda", "type": "S",
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# "items": '', 'show': False} # _kernel
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# ]
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# # {"index": 9, "name": "saveEpoch", "value": 2, "description": '保存模型轮次', "default": 2, "type": "I", 'show': True}]
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# params_str = json.dumps(params_list)
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# return params_str
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#
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#
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# @obtain_detect_param()
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# def returnDetectParams():
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# # nvmlInit()
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# # gpuDeviceCount = nvmlDeviceGetCount() # 获取Nvidia GPU块数
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# # _kernel = [f"cuda:{a}" for a in range(gpuDeviceCount)]
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# params_list = [
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# {"index": 0, "name": "inputPath", "value": 'E:/aicheck/data_set/11442136178662604800/input/',
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# "description": '输入图像路径', "default": './app/maskrcnn/datasets/M006B_waibi/JPEGImages', "type": "S",
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# 'show': False},
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# {"index": 1, "name": "outputPath", "value": 'E:/aicheck/data_set/11442136178662604800/val_results/',
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# "description": '输出结果路径',
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# "default": './app/maskrcnn/datasets/M006B_waibi/res', "type": "S", 'show': False},
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# {"index": 0, "name": "modPath", "value": "E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt",
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# "description": '模型路径', "default": "./app/maskrcnn/saved_model/test.pt", "type": "S", 'show': False},
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# {"index": 3, "name": "device", "value": "0", "description": '推理核', "default": "cpu", "type": "S",
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# 'show': False},
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# ]
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# # {"index": 9, "name": "saveEpoch", "value": 2, "description": '保存模型轮次', "default": 2, "type": "I", 'show': True}]
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# params_str = json.dumps(params_list)
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# return params_str
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#
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#
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# @obtain_download_pt_param()
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# def returnDownloadParams():
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# params_list = [
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# {"index": 0, "name": "exp_inputPath", "value": 'E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt',
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# "description": '转化模型输入路径',
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# "default": '/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/new磁环检测test_183504733393264640_R-DDM_11.pt/',
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# "type": "S", 'show': False},
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# {"index": 1, "name": "device", "value": 'gpu', "description": 'CPU或GPU', "default": 'gpu', "type": "S",
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# 'show': False}
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# ]
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# params_str = json.dumps(params_list)
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# return params_str
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@obtain_test_param()
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def returnValidateParams():
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# nvmlInit()
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# gpuDeviceCount = nvmlDeviceGetCount() # 获取Nvidia GPU块数
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# _kernel = [f"cuda:{a}" for a in range(gpuDeviceCount)]
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params_list = [
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{"index": 0, "name": "modPath", "value": "E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt",
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"description": '验证模型路径', "default": "./app/maskrcnn/saved_model/test.pt", "type": "S", 'show': False},
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{"index": 1, "name": "batch_size", "value": 1, "description": '批次图像数量', "default": 1, "type": "I",
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'show': False},
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{"index": 2, "name": "img_size", "value": 640, "description": '训练图像大小', "default": 640, "type": "I",
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'show': False},
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{"index": 3, "name": "outputPath", "value": 'E:/aicheck/data_set/11442136178662604800/val_results/',
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"description": '输出结果路径',
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"default": './app/maskrcnn/datasets/M006B_waibi/res', "type": "S", 'show': False},
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{"index": 4, "name": "device", "value": "0", "description": '训练核心', "default": "cuda", "type": "S",
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"items": '', 'show': False} # _kernel
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]
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# {"index": 9, "name": "saveEpoch", "value": 2, "description": '保存模型轮次', "default": 2, "type": "I", 'show': True}]
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params_str = json.dumps(params_list)
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return params_str
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@obtain_detect_param()
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def returnDetectParams():
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# nvmlInit()
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# gpuDeviceCount = nvmlDeviceGetCount() # 获取Nvidia GPU块数
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# _kernel = [f"cuda:{a}" for a in range(gpuDeviceCount)]
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params_list = [
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{"index": 0, "name": "inputPath", "value": 'E:/aicheck/data_set/11442136178662604800/input/',
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"description": '输入图像路径', "default": './app/maskrcnn/datasets/M006B_waibi/JPEGImages', "type": "S",
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'show': False},
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{"index": 1, "name": "outputPath", "value": 'E:/aicheck/data_set/11442136178662604800/val_results/',
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"description": '输出结果路径',
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"default": './app/maskrcnn/datasets/M006B_waibi/res', "type": "S", 'show': False},
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{"index": 0, "name": "modPath", "value": "E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt",
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"description": '模型路径', "default": "./app/maskrcnn/saved_model/test.pt", "type": "S", 'show': False},
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{"index": 3, "name": "device", "value": "0", "description": '推理核', "default": "cpu", "type": "S",
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'show': False},
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]
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# {"index": 9, "name": "saveEpoch", "value": 2, "description": '保存模型轮次', "default": 2, "type": "I", 'show': True}]
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params_str = json.dumps(params_list)
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return params_str
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@obtain_download_pt_param()
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def returnDownloadParams():
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params_list = [
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{"index": 0, "name": "exp_inputPath", "value": 'E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt',
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"description": '转化模型输入路径',
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"default": '/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/new磁环检测test_183504733393264640_R-DDM_11.pt/',
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"type": "S", 'show': False},
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{"index": 1, "name": "device", "value": 'gpu', "description": 'CPU或GPU', "default": 'gpu', "type": "S",
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'show': False}
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]
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params_str = json.dumps(params_list)
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return params_str
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if __name__ == '__main__':
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