:model quantize

Prerequisites

OSS is activated, and Machine Learning Studio is authorized to access OSS. For more information, see Activate OSS and Grant PAI the permissions to access OSS.

Limits

• The model quantize component is available only in Machine Learning Designer of Machine Learning Platform for AI.
• You can use the model quantize component based only on the computing resources of Deep Learning Containers (DLC) of Machine Learning Platform for AI.
• If an object detection component is configured as an upstream component of the model quantize component, make sure that the model-related configurations and the dataset configurations for the two components are the same.

Configure the component in Machine Learning Designer

• Input ports

  Input port (left-to-right)	Data type	Recommended upstream component	Required
  Input model	Object Storage Service (OSS)	object detection or model prune	No
  Training data	OSS	Read File Data	Yes
  Test data	OSS	Read File Data	Yes
  Category file	OSS	Read File Data	Yes

• Component parameters

  Tab	Parameter	Description
  Fields Setting	model type	The type of the model to train. Valid values: YOLOX YOLOX_EDGE Default value: YOLOX_EDGE.
  	oss dir to save model	The OSS path in which the model and logs are stored during training.
  	oss path to training tfrecord	The OSS path of the file that contains the training data. If a Read File Data component is configured as an upstream component of the model quantize component, you do not need to set this parameter.
  	oss annotation path for training set	The OSS path of the file that contains the labeling results of the training dataset.
  	oss path to evaluation tfrecord	The OSS path of the file that contains the test data. If a Read File Data component is configured as an upstream component of the model quantize component, you do not need to set this parameter.
  	oss annotation path for validataion set	The OSS path of the file that contains the labeling results of the test dataset.
  	oss path of class list file	The OSS path of the category file. If a Read File Data component is configured as an upstream component of the model quantize component, you do not need to set this parameter.
  	YOLOX data format	The format of the datasets. Valid values: COCO DetSourcePAI Default value: COCO.
  	oss path to pretrained model	The OSS path in which your pre-trained model is stored. If you have a pre-trained model, set this parameter to the OSS path of your pre-trained model. This parameter is optional.
  Parameters Setting	YOLOX edge model type	The type of the YOLOX preset model. This parameter is required only if you set the model type parameter to YOLOX_EDGE on the Fields Setting tab. Default value: yolox-customized.
  	YOLOX model type	This parameter is required only if you set the model type parameter to YOLOX on the Fields Setting tab. Valid values: yolox-s yolox-m yolox-l yolox-x Default value: yolox-s.
  	yolox_edge_model_depth	The depth of the YOLOX model. This parameter is required only if you set the model type parameter to YOLOX_EDGE on the Fields Setting tab. The value is a floating-point number. Default value: 1.0. Valid values: 0.01 to 1.0.
  	yolox_edge_model_width	The width of the YOLOX model. This parameter is required only if you set the model type parameter to YOLOX_EDGE on the Fields Setting tab. The value is a floating-point number. Default value: 1.0. Valid values: 0.01 to 1.0.
  	YOLOX edge model activation	The type of the activation function for the YOLOX model. This parameter is required only if you set the model type parameter to YOLOX_EDGE on the Fields Setting tab. Valid values: relu lrelu silu hsilu Default value: relu.
  	test confidence:[0.01,1.0]	The confidence level of the test. This parameter is required only if you set the model type parameter to YOLOX_EDGE on the Fields Setting tab. The value is a floating-point number. Default value: 0.01. Valid values: 0.01 to 1.0.
  	nms threshold:[0.01,1.0]	The Non-Maximum Suppression (NMS) threshold. This parameter is required only if you set the model type parameter to YOLOX_EDGE on the Fields Setting tab. The value is a floating-point number. Default value: 0.65. Valid values: 0.01 to 1.0.
  	num classes	The number of categories.
  	image scale	The resolution of resized images for YOLOX. The value is in the format of Height Width. Default value: 640 640.
  	initial learning rate	The initial learning rate for model training. The value is a floating-point number. Default value: 0.01.
  	train batch size	The size of a training batch. Specifically, this parameter specifies the number of data samples used for training in a single model iteration process. Default value: 16.
  	eval batch size	The size of an evaluation batch. Specifically, this parameter specifies the number of data samples used for evaluation in a single model iteration process. Default value: 1.
  	num epochs	The total number of training epochs. Default value: 20.
  	warmup epochs	The number of epochs for the warm-up strategy. Default value: 5.
  	last no augmented lr epochs	The number of epochs to complete after the learning rate is stable if the warm-up strategy is used. Default value: 5.
  	save checkpoint epoch	The interval at which a checkpoint is saved. The interval is measured in epochs. Default value: 1. The default value indicates that a checkpoint is saved every time an epoch is complete.
  Tuning	io thread num for training	The number of threads that are used to read the training data.
  	single worker or distributed on MaxCompute or DLC	The compute engine that is used to run the component. The system automatically sets this parameter based on the type of the training model that you select.
  	number of worker	The number of workers used for distributed training. Default value: 1.
  	gpu machine type	The instance type of the GPU-accelerated instance to be used. Default value: 8vCPU+32GB Mem+1xv100-ecs.gn6v-c8g1.2xlarge.

Example

The following figure shows a sample pipeline in which the model quantize component is used. In this example, the components are configured by performing the following steps:

1. Perform data labeling by using iTAG. For more information, see iTAG.
2. Use the Read File Data-1, Read File Data-2, and Read File Data-3 components to read the training dataset, test dataset, and category file. You can configure the dataset to be read for a Read File Data component by setting the OSS Data Path parameter to the OSS path of the training dataset, test dataset, or category file.
3. Use the object detection-1 component to create an object detection model.
4. Use the Read File Data-4, Read File Data-5, and Read File Data-6 components to read the training dataset, test dataset, and category file. You can configure the dataset to be read for a Read File Data component by setting the OSS Data Path parameter to the OSS path of the training dataset, test dataset, or category file.
5. Connect the object detection-1, Read File Data-4, Read File Data-5, and Read File Data-6 components to the model quantize component and set the component parameters. For more information, see the "Configure the component in Machine Learning Designer" section of this topic.