TensorFlow Object Detection API中的Faster R-CNN /SSD模型参数调整

关于TensorFlow Object Detection API配置,可以参考之前的文章https://becominghuman.ai/tensorflow-object-detection-api-tutorial-training-and-evaluating-custom-object-detector-ed2594afcf73

在本文中,我将讨论如何更改预训练模型的配置。本文的目的是您可以根据您的应用程序配置TensorFlow/models,而API将不再是一个黑盒!

本文的概述:

  • 了解协议缓冲区和proto文件。
  • 利用proto文件知识,我们如何了解模型配置文件
  • 遵循3个步骤来更新模型的参数
  • 其他示例:
  1. 更改重量初始值设定项
  2. 更改体重优化器
  3. 评估预训练模型

协议缓冲区

要修改模型,我们需要了解它的内部机制。TensorFlow对象检测API使用协议缓冲区Protocol Buffers),这是与语言无关,与平台无关且可扩展的机制,用于序列化结构化数据。就像XML规模较小,但更快,更简单。API使用协议缓冲区语言proto2版本。我将尝试解释更新预配置模型所需的语言。有关协议缓冲区语言的更多详细信息,请参阅此文档Python教程

协议缓冲区的工作可分为以下三个步骤:

  • .proto文件中定义消息格式该文件的行为就像所有消息的蓝图一样,它显示消息所接受的所有参数是什么,参数的数据类型应该是什么,参数是必需的还是可选的,参数的标记号是什么,什么是参数的默认值等。API的protos文件可在此处找到为了理解,我使用grid_anchor_generator.proto文件。
  • syntax = "proto2";
    
    package object_detection.protos;
    
    // Configuration proto for GridAnchorGenerator. See
    // anchor_generators/grid_anchor_generator.py for details.
    message GridAnchorGenerator {
       // Anchor height in pixels.
      optional int32 height = 1 [default = 256];
    
      // Anchor width in pixels.
      optional int32 width = 2 [default = 256];
    
      // Anchor stride in height dimension in pixels.
      optional int32 height_stride = 3 [default = 16];
    
      // Anchor stride in width dimension in pixels.
      optional int32 width_stride = 4 [default = 16];
    
      // Anchor height offset in pixels.
      optional int32 height_offset = 5 [default = 0];
    
      // Anchor width offset in pixels.
      optional int32 width_offset = 6 [default = 0];
    
      // At any given location, len(scales) * len(aspect_ratios) anchors are
      // generated with all possible combinations of scales and aspect ratios.
    
      // List of scales for the anchors.
      repeated float scales = 7;
    
      // List of aspect ratios for the anchors.
      repeated float aspect_ratios = 8;
    }

    它是从线30-33的参数明确scales,并aspect_ratios是强制性的消息GridAnchorGenerator,而参数的其余部分都是可选的,如果不通过,将采取默认值。

    • 定义消息格式后,我们需要编译协议缓冲区。该编译器将从文件生成类.proto文件。在安装API的过程中,我们运行了以下命令,该命令将编译协议缓冲区:
    • # From tensorflow/models/research/
      protoc object_detection/protos/*.proto --python_out=.
      • 在定义和编译协议缓冲区之后,我们需要使用Python协议缓冲区API来写入和读取消息。在我们的例子中,我们可以将配置文件视为协议缓冲区API,它可以在不考虑TensorFlow API的内部机制的情况下写入和读取消息。换句话说,我们可以通过适当地更改配置文件来更新预训练模型的参数。
      • 了解配置文件

        显然,配置文件可以帮助我们根据需要更改模型的参数。弹出的下一个问题是如何更改模型的参数?本节和下一部分将回答这个问题,在这里proto文件的知识将很方便。出于演示目的,我正在使用faster_rcnn_resnet50_pets.config文件。

      • # Faster R-CNN with Resnet-50 (v1), configured for Oxford-IIIT Pets Dataset.
        # Users should configure the fine_tune_checkpoint field in the train config as
        # well as the label_map_path and input_path fields in the train_input_reader and
        # eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
        # should be configured.
        
        model {
          faster_rcnn {
            num_classes: 37
            image_resizer {
              keep_aspect_ratio_resizer {
                min_dimension: 600
                max_dimension: 1024
              }
            }
            feature_extractor {
              type: 'faster_rcnn_resnet50'
              first_stage_features_stride: 16
            }
            first_stage_anchor_generator {
              grid_anchor_generator {
                scales: [0.25, 0.5, 1.0, 2.0]
                aspect_ratios: [0.5, 1.0, 2.0]
                height_stride: 16
                width_stride: 16
              }
            }
            first_stage_box_predictor_conv_hyperparams {
              op: CONV
              regularizer {
                l2_regularizer {
                  weight: 0.0
                }
              }
              initializer {
                truncated_normal_initializer {
                  stddev: 0.01
                }
              }
            }
            first_stage_nms_score_threshold: 0.0
            first_stage_nms_iou_threshold: 0.7
            first_stage_max_proposals: 300
            first_stage_localization_loss_weight: 2.0
            first_stage_objectness_loss_weight: 1.0
            initial_crop_size: 14
            maxpool_kernel_size: 2
            maxpool_stride: 2
            second_stage_box_predictor {
              mask_rcnn_box_predictor {
                use_dropout: false
                dropout_keep_probability: 1.0
                fc_hyperparams {
                  op: FC
                  regularizer {
                    l2_regularizer {
                      weight: 0.0
                    }
                  }
                  initializer {
                    variance_scaling_initializer {
                      factor: 1.0
                      uniform: true
                      mode: FAN_AVG
                    }
                  }
                }
              }
            }
            second_stage_post_processing {
              batch_non_max_suppression {
                score_threshold: 0.0
                iou_threshold: 0.6
                max_detections_per_class: 100
                max_total_detections: 300
              }
              score_converter: SOFTMAX
            }
            second_stage_localization_loss_weight: 2.0
            second_stage_classification_loss_weight: 1.0
          }
        }
        
        train_config: {
          batch_size: 1
          optimizer {
            momentum_optimizer: {
              learning_rate: {
                manual_step_learning_rate {
                  initial_learning_rate: 0.0003
                  schedule {
                    step: 900000
                    learning_rate: .00003
                  }
                  schedule {
                    step: 1200000
                    learning_rate: .000003
                  }
                }
              }
              momentum_optimizer_value: 0.9
            }
            use_moving_average: false
          }
          gradient_clipping_by_norm: 10.0
          fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
          from_detection_checkpoint: true
          # Note: The below line limits the training process to 200K steps, which we
          # empirically found to be sufficient enough to train the pets dataset. This
          # effectively bypasses the learning rate schedule (the learning rate will
          # never decay). Remove the below line to train indefinitely.
          num_steps: 200000
          data_augmentation_options {
            random_horizontal_flip {
            }
          }
          max_number_of_boxes: 50
        }
        
        train_input_reader: {
          tf_record_input_reader {
            input_path: "PATH_TO_BE_CONFIGURED/pet_train.record"
          }
          label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
        }
        
        eval_config: {
          num_examples: 2000
          # Note: The below line limits the evaluation process to 10 evaluations.
          # Remove the below line to evaluate indefinitely.
          max_evals: 10
        }
        
        eval_input_reader: {
          tf_record_input_reader {
            input_path: "PATH_TO_BE_CONFIGURED/pet_val.record"
          }
          label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"
          shuffle: false
          num_readers: 1
        }

        第7至10行表示这num_classesfaster_rcnnmessage 的参数之一,而后者又是message的参数model同样,optimizer是父train_config消息的子消息,而message的batch_size另一个参数train_config我们可以通过签出相应的protos文件来验证这一点

      • syntax = "proto2";
        
        package object_detection.protos;
        
        import "object_detection/protos/anchor_generator.proto";
        import "object_detection/protos/box_predictor.proto";
        import "object_detection/protos/hyperparams.proto";
        import "object_detection/protos/image_resizer.proto";
        import "object_detection/protos/losses.proto";
        import "object_detection/protos/post_processing.proto";
        
        // Configuration for Faster R-CNN models.
        // See meta_architectures/faster_rcnn_meta_arch.py and models/model_builder.py
        //
        // Naming conventions:
        // Faster R-CNN models have two stages: a first stage region proposal network
        // (or RPN) and a second stage box classifier.  We thus use the prefixes
        // `first_stage_` and `second_stage_` to indicate the stage to which each
        // parameter pertains when relevant.
        message FasterRcnn {
        
          // Whether to construct only the Region Proposal Network (RPN).
          optional int32 number_of_stages = 1 [default=2];
        
          // Number of classes to predict.
          optional int32 num_classes = 3;
          
          // Image resizer for preprocessing the input image.
          optional ImageResizer image_resizer = 4;

        从第20行和第26行可以明显看出,这num_classesoptional消息参数之一faster_rcnn我希望到目前为止的讨论有助于理解配置文件的组织。现在,是时候正确更新模型的参数之一了。

      • 步骤1:确定要更新的参数

        假设我们需要更新fast_rcnn_resnet50_pets.config文件的image_resizer第10行中提到的参数

        步骤2:在存储库中搜索给定参数

        目标是找到proto参数文件。为此,我们需要在存储库中搜索。

      • www.wityx.com

         我们需要搜索以下代码:

      • parameter_name path:research/object_detection/protos
        #in our case parameter_name="image_resizer" thus,
        image_resizer path:research/object_detection/protos

        在此path:research/object_detection/protos限制搜索域。此处可以找到有关如何在GitHub上搜索的更多信息搜索的输出image_resizer path:research/object_detection/protos如下所示:

      • www.wityx.com
      • 从输出中很明显,要更新image_resizer参数,我们需要分析image_resizer.proto文件。

        步骤3:分析proto档案

        syntax = "proto2";
        
        package object_detection.protos;
        
        // Configuration proto for image resizing operations.
        // See builders/image_resizer_builder.py for details.
        message ImageResizer {
          oneof image_resizer_oneof {
            KeepAspectRatioResizer keep_aspect_ratio_resizer = 1;
            FixedShapeResizer fixed_shape_resizer = 2;
          }
        }
        
        // Enumeration type for image resizing methods provided in TensorFlow.
        enum ResizeType {
          BILINEAR = 0; // Corresponds to tf.image.ResizeMethod.BILINEAR
          NEAREST_NEIGHBOR = 1; // Corresponds to tf.image.ResizeMethod.NEAREST_NEIGHBOR
          BICUBIC = 2; // Corresponds to tf.image.ResizeMethod.BICUBIC
          AREA = 3; // Corresponds to tf.image.ResizeMethod.AREA
        }
        
        // Configuration proto for image resizer that keeps aspect ratio.
        message KeepAspectRatioResizer {
          // Desired size of the smaller image dimension in pixels.
          optional int32 min_dimension = 1 [default = 600];
        
          // Desired size of the larger image dimension in pixels.
          optional int32 max_dimension = 2 [default = 1024];
        
          // Desired method when resizing image.
          optional ResizeType resize_method = 3 [default = BILINEAR];
        
          // Whether to pad the image with zeros so the output spatial size is
          // [max_dimension, max_dimension]. Note that the zeros are padded to the
          // bottom and the right of the resized image.
          optional bool pad_to_max_dimension = 4 [default = false];
        
          // Whether to also resize the image channels from 3 to 1 (RGB to grayscale).
          optional bool convert_to_grayscale = 5 [default = false];
        
          // Per-channel pad value. This is only used when pad_to_max_dimension is True.
          // If unspecified, a default pad value of 0 is applied to all channels.
          repeated float per_channel_pad_value = 6;
        }
        
        // Configuration proto for image resizer that resizes to a fixed shape.
        message FixedShapeResizer {
          // Desired height of image in pixels.
          optional int32 height = 1 [default = 300];
        
          // Desired width of image in pixels.
          optional int32 width = 2 [default = 300];
        
          // Desired method when resizing image.
          optional ResizeType resize_method = 3 [default = BILINEAR];
        
          // Whether to also resize the image channels from 3 to 1 (RGB to grayscale).
          optional bool convert_to_grayscale = 4 [default = false];
        }

        从第8-10行可以看出,我们可以使用keep_aspect_ratio_resizer调整图像的大小fixed_shape_resizer在分析行23-44,我们可以观察到的消息keep_aspect_ratio_resizer有参数:min_dimensionmax_dimensionresize_methodpad_to_max_dimensionconvert_to_grayscale,和per_channel_pad_value此外,fixed_shape_resizer有参数:heightwidthresize_method,和convert_to_grayscaleproto文件中提到了所有参数的数据类型因此,要更改image_resizer类型,我们可以在配置文件中更改以下几行。

      • #before
        image_resizer {
        keep_aspect_ratio_resizer {
        min_dimension: 600 
        max_dimension: 1024
            }
        }
        #after
        image_resizer {
        fixed_shape_resizer {
        height: 600
        width: 500
        resize_method: AREA
          }
        }

        上面的代码将使用AREA调整大小方法将图像调整为500 * 600。TensorFlow中可用的各种调整大小的方法可以在这里找到

      • 其他例子

        我们可以使用上一节中讨论的步骤更新/添加任何参数。我将在此处演示一些经常使用的示例,但是上面讨论的步骤可能有助于更新/添加模型的任何参数。

        更改重量初始化器

        • 决定更改fast_rcnn_resnet50_pets.config文件的initializer第35行的参数
        • initializer path:research/object_detection/protos在存储库中搜索根据搜索结果,很明显我们需要分析hyperparams.proto文件。
        • www.wityx.com
          • hyperparams.proto文件中的第68–74行说明了initializer配置。
          • message Initializer {
              oneof initializer_oneof {
                TruncatedNormalInitializer truncated_normal_initializer = 1;
                VarianceScalingInitializer variance_scaling_initializer = 2;
                RandomNormalInitializer random_normal_initializer = 3;
              }
            }

            我们可以使用random_normal_intializer代替truncated_normal_initializer,因为我们需要分析hyperparams.proto文件中的第99–102行

          • message RandomNormalInitializer {
            optional float mean = 1 [default = 0.0];
            optional float stddev = 2 [default = 1.0];
            }
          • 显然random_normal_intializer有两个参数meanstddev我们可以将配置文件中的以下几行更改为use random_normal_intializer
          • #before
            initializer {
                truncated_normal_initializer {
                    stddev: 0.01
                   }
            }
            #after
            initializer {
                random_normal_intializer{
                   mean: 1 
                   stddev: 0.5
                   }
            }

            更改体重优化器

            • 决定更改faster_rcnn_resnet50_pets.config文件的第87行momentum_optimizer的父消息的参数optimizer
            • optimizer path:research/object_detection/protos在存储库中搜索根据搜索结果,很明显我们需要分析optimizer.proto文件。
            • www.wityx.com
            • message Optimizer {
                oneof optimizer {
                  RMSPropOptimizer rms_prop_optimizer = 1;
                  MomentumOptimizer momentum_optimizer = 2;
                  AdamOptimizer adam_optimizer = 3;
                }

              显然,代替momentum_optimizer我们可以使用adam_optimizer已被证明是良好的优化程序。为此,我们需要在f aster_rcnn_resnet50_pets.config文件中进行以下更改

          #before
          optimizer {  
            momentum_optimizer: {
                learning_rate: {
                     manual_step_learning_rate {
                    initial_learning_rate: 0.0003
                    schedule {
                      step: 900000
                      learning_rate: .00003
                    }
                    schedule {
                      step: 1200000
                      learning_rate: .000003
                    }
                  }
                }
                momentum_optimizer_value: 0.9
              }
          #after
          optimizer {
            adam_optimizer: {
                learning_rate: {
                 manual_step_learning_rate {
                    initial_learning_rate: 0.0003
                    schedule {
                      step: 900000
                      learning_rate: .00003
                    }
                    schedule {
                      step: 1200000
                      learning_rate: .000003
                    }
                  }
                }
              }

          评估预训练模型

          Eval等待300秒,以检查训练模型是否已更新!如果您的GPU不错,那么您可以同时进行训练和评估!通常,资源将被耗尽。为了克服这个问题,我们可以先训练模型,将其保存在目录中,然后再评估模型。为了稍后进行评估,我们需要在配置文件中进行以下更改:

        • #Before
          eval_config: {
            num_examples: 2000
            # Note: The below line limits the evaluation process to 10 evaluations.
            # Remove the below line to evaluate indefinitely.
            max_evals: 10
          }
          #after
          eval_config: {
          num_examples: 10
          num_visualizations: 10
          eval_interval_secs: 0
          }

          num_visualizations应该等于要评估的数量!可视化的数量越多,评估所需的时间就越多。如果您的GPU具有足够的能力同时进行训练和评估,则可以保留eval_interval_secs: 300此参数决定运行评估的频率。我按照上面讨论的3个步骤得出了这个结论。

          简而言之,协议缓冲区的知识帮助我们理解了模型参数是以消息形式传递的,并且可以更新我们可以引用的.proto文件的参数讨论了3个简单的步骤来找到.proto用于更新参数的正确文件。

          请在注释的配置文件中提及您要更新/添加的任何参数。

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posted on 2019-11-05 16:45  雨落无影  阅读(...)  评论(...编辑  收藏

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